独立行政法人国立病院機構
水戸医療センター
研 究 業 績 集
令和3年度

巻頭言

病院長 米野琢哉
水戸医療センターの基本方針として、「臨床研究を積極的に推進します」を掲げています。
令和 3 年度は、COVID-19 対応に追われながらも、日常診療を黙々とこなし、臨床研究にも真撃に取り組んでいただきました。職員の皆様のご努力に敬意をはらいたいと思います。
臨床研究の推進は、診療の質向上のためにも必須です。様々な職種の方々が研究に取り組
むことによって、診療の活性化にもつながると期待しております。病院としても、臨床研究部を中心に、資金提供、研究に必要な文書作成のアドバイスなど研究実施のサポートを継続
していきます。今後も是非臨床研究にチャレンジしてみてください。

2021年度

受託研究実績金額

8,373万円（治験・製造販売後臨床試験・製造販売後調査）

契約種類別グラフ
各種調査¥16,231,732
製造販売後
臨床試験
¥791,877

治験¥66,453,742

診療科別グラフ神経内科 その他診療科
泌尿器科
整形外科
循環器科
血液内科
呼吸器科

消化器科

受託研究実績報告①

受託研究実績金額

（治験・製造販売後臨床試験・製造販売後調査）

実績金額¥120,000,000¥100,000,000¥97,324,100¥98,114,743¥81,660,560¥80,000,000¥76,600,888¥83,739,626¥60,000,000¥40,000,000¥20,000,000¥0
2017年度②

2018年度

治験・製造販売後臨床試験

2019年度

2020年度

2021年度

新規登録症例数

実施症例数
50
45

43

40

41

35

28

30
25
20

20

21

2020年度

2021年度1510
50
2017年度

2018年度

2019年度NHOネットワーク共同研究

研究責任者

堤 育代

研究課題名(採択番号)

新規症例登録数研究代表者(施設名)

未治療濾胞性リンパ腫におけるObinutuzumabの治療成績、QOL、費用
堤育代
対効果、予後に関する多施設前向きコ(水戸医療センター）
ホート研究

当該施設
文書同意
新規登録
有・無
症例数

有

6

有

1

有

2

有

9

無

2

（採択番号：H31-NHO(血液)-01）

吉沢和朗

筋強直性ジストロフィー患者における
吉田亘佑
脳梗塞発症頻度とCHADS2スコアによる(旭川医療センター）
予測の有用性についての前向き研究（採択番号：H31-NHO(神経)-01）

吉田近思

B細胞性急性リンパ性白血病におけるターゲットキャプチャーRNA-seqを用
安田貴彦
いたサブタイプ診断の実行可能性に関(名古屋医療センター）
する研究（採択番号 H31-NHO(血液)－02）

箭内英俊

根治照射不能な進行非小細胞肺癌患者における免疫チェックポイント阻害剤
岡本龍郎の効果予測因子としての栄養/免疫学的指標の臨床的意義に関する前向き観 （九州がんセンター ）察研究（採択番号 H31-NHO(癌呼)-02）

吉田近思

レジストリーデータを利用したAYA世
代DLBCLの臨床的・生物学的特性を明らかにする観察研究（採択番号：R3-NHO(血液)-01）鈴木康裕(名古屋医療センター）

競争的研究費
項目

研究課題名

研究者名

研究事業名

主任
分担

研究費
受領日

研究費
(単位：円)

厚生労働科学研究費

新型インフルエンザ等の感染症発生時のリスクマネジメントに資する
感染症のリスク評価及び公衆衛生時対策の強化のための研究

安田 貢

国立病院機構三重病院

分担

R3.6.24500,000

厚生労働科学研究費

心停止後臓器提供数の減少への効果的な対策に資する
研究

湯沢賢治

水戸医療センター

主任

R3.6.30

3,000,000

科学研究費助成事業(学術研究助成基金助成金)

医師の病院前診察における網羅的文献データベース構
築とエビデンス診療ギャップの解明

堤 悠介

水戸医療センター

主任

R3.7.9585,000

科学研究費助成事業(学術研究助成基金助成金)

次世代型肘検診から野球肘の病態解明～エコー検診と車載型小型MRIの融合～

小川 健

水戸医療センター

主任

R3.7.9

1,014,000

厚生労働科学研究費

HAMならびに類縁疾患の患者レジストリを介した診療連携モデルの構築によるガイドラインの活用促進と医療水準の均てん化に関する研究(20HA2001)

湯沢賢治

聖マリアンナ医科大学

分担

R3.8.30

300,000

日本医療研究開発機構研究費

遺伝子関連情報を基軸にした効率的免疫抑制管理による革新的長期管理ロジック開発（21ek0510033h0002）

湯沢賢治

東京女子医科大学

分担

R3.8.31

390,000

科学研究費助成事業(科学研究費補助金）

臓器移植法制・法政策の包括的再検証―改正法施行10年目の現況を踏まえた提言(20H01430)

湯沢賢治

北海道大学大学院法学研究科

分担

R3.9.24

390,000

日本医療研究開発機構研究費

HAM-HTLV-1 陽性難治性疾患の患者レジストリ活用によるエビデンスの創出(21ek0109529s0301)

湯沢賢治

聖マリアンナ医科大学

分担

R3.9.29

390,000

日本医療研究開発機構研究費

新規HTLV-1感染モデルを用いたHAMの発症予防法・治療法の開発(21ek0109441s0202)

湯沢賢治

聖マリアンナ医科大学

分担

R3.9.29910,000

民間セクターからの寄附金

腎代替療法の情報提供における腎移植に関する情報提供サイトに関する研究

湯沢賢治

中外製薬株式会社主任

R3.10.20

200,000

民間セクターからの寄附金

腹水濾過濃縮再静注法の臨床応用への適応拡大の研究

小﨑浩一

中外製薬株式会社主任

R3.10.20

200,000

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1
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1.500

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国際学会
学会名

演題名

演者名

発表年月日

63nd ASH Annual Meetingand Exposition

Importance of the Durationof TKI Treatment inTreatment-free Remission ofChronic Phase Chronic
Myeloid Leukemia: Results of
D-FREE Trial

吉田近思

R3.12.6

American Heart
Association Scientific
Sessions 2021

Association Between Frailty
and Vascular Endothelial
Growth Factor Families inPatients with Heart Failure:
The PREHOSP-CHF Study

小泉智三

R3.11.16

Japanese Society ofCardiology Annual
Scientific Sessions 2021

A Case of Recurrent Heart
Failure on Well-controlled
Eosinophilic Granulomatouswith Polyangitis

南健太郎

R4.3.11

American Heart
Association Scientific
Sessions 2021

Association Between Age andthe von-Willebrand Factor
ADAMTS 13 Axis in Patientswith ST elevation Myocardial
infarction

大澤 匠
伊藤雄太
小泉智三

R3.11.13～
11.15

大澤

匠

R3.12.276th Annual Meeting of
the American Society
Surgery of Hand

Chronological change inpostoperative magnetic
resonance imaging for Kienbö
ck’s disease using an
original grading system

小川 健

R3.9.29

Japanese Society ofCardiology Annual
Scientific Sessions 2021

Association Between Age andthe ADAMTS 13 von-Willebrand
Factor Axis in Patientswith Acute Arterial
Thrombosis (STEMI and AculteIschemic Stroke)大澤匠

R4.3.13

第76回日本消化器外科学会

Experience of cell-free andconcentrated ascites
reinfusion therapy (CART) in
our hospital.

小﨑浩一

R3.7.9Comparison of VWF-ADAMTS 13
axis involvement in atrial
European Society offibrillation and sinus
Cardiology Asia with APSC
rhythm in patients with ST＆AFC
elevation myocardial
infarction令和３年度院内臨床研究課題
研究代表者

課題名

配分額

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神経内科患者における心拍変動係数（ＣＶＲ-Ｒ）による自立神経機能評価の有用性についての観察研究

390,000

小﨑浩一

腹水濾過濃縮再静注法（CART）における採取腹水長期常温保存の臨床使用への可能性の検討

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牽引下MRIによる母指ＣＭ関節の軟骨評価に関する研究

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新鮮骨粗鬆症性椎体骨折患者の1週間安静入院による保存
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330,000

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水戸医療センター
令和3年度

代表的論文

Japanese Journal of Clinical Oncology, 2021, 51(9)1372–1382
https://doi.org/10.1093/jjco/hyab112
Advance Access Publication Date: 29 July 2021
Original Article

Original Article

Venetoclax plus low-dose cytarabine inJapanese patients with untreated acute myeloid
leukaemia ineligible for intensive chemotherapy
Takahiro Yamauchi1 ,*, Chikashi Yoshida2 , Kensuke Usuki3 ,Satoru Takada4 , Itaru Matsumura5 , Nobuaki Dobashi6 ,Yasushi Miyazaki7 , Toshihiro Miyamoto8 , Hiroatsu Iida9 , Norio Asou10 ,Junya Kuroda11 , Satoshi Ichikawa12 , Norio Komatsu13 ,Wellington Mendes14 , Hideyuki Honda15 , Sumiko Okubo16 ,Misaki Kurokawa15 , Qi Jiang14 , Andrew Wei17 , and Kenichi Ishizawa18
1

Department of Hematology and Oncology, University of Fukui Hospital, Fukui, Japan, 2 Department of Hematology,
National Hospital Organization, Mito Medical Center, Ibaraki, Japan, 3 Department of Hematology, NTT Medical
Center Tokyo, Tokyo, Japan, 4 Leukemia Research Center, Saiseikai Maebashi Hospital, Maebashi, Japan,
5
Department of Hematology and Rheumatology, Kindai University Hospital, Osaka, Japan, 6 Division of ClinicalOncology/Hematology, The Jikei University Daisan Hospital, Tokyo, Japan, 7 Department of Hematology, Atomic
Bomb Disease Institute, Nagasaki University, Nagasaki, Japan, 8 Medicine and Biosystemic Science, Graduate
School of Medical Sciences, Kyushu University, Fukuoka, Japan, 9 Department of Hematology, National Hospital
Organization Nagoya Medical Center, Nagoya, Japan, 10 Department of Hematology, Saitama Medical University
International Medical Center, Hidaka, Japan, 11 Division of Hematology and Oncology, Department of Medicine,
Kyoto Prefectural University of Medicine, Kyoto, Japan, 12 Department of Hematology, Tohoku University Hospital,
Sendai, Japan, 13 Department of Hematology, Juntendo University School of Medicine, Tokyo, Japan, 14 AbbVie Inc.,
North Chicago, IL, USA, 15 AbbVie GK, Tokyo, Japan, 16 AbbVie GK, Osaka, Japan, 17 Department of Clinical
Haematology, The Alfred Hospital and Monash University, Melbourne, VIC, Australia and 18 Department of Third
Internal Medicine, Yamagata University Hospital, Yamagata, Japan*For reprints and all correspondence: Takahiro Yamauchi, Department of Hematology and Oncology, University of Fukui
Hospital, 23-3 Matsuokashimoaizuki, Eiheiji Fukui 910-1193, Japan. E-mail: tyamauch@u-fukui.ac.jp
Received 13 May 2021; Editorial Decision 24 June 2021; Accepted 26 June 2021

Abstract
Background: In a multinational phase 3 trial (VIALE-C), venetoclax plus low-dose cytarabine
prolonged overall survival vs placebo plus low-dose cytarabine in patients with newly diagnosed
acute myeloid leukaemia ineligible for intensive chemotherapy, although it was not statisticallysigniﬁcant. Herein, we assess the beneﬁt of venetoclax plus low-dose cytarabine in the Japanese
subgroup of VIALE-C patients (n = 27).
Methods: VIALE-C, a randomized (2:1), double-blind study (NCT03069352), enrolled untreatedpatients (≥18 years) with acute myeloid leukaemia. Patients received venetoclax (600 mg days
1–28, 4-day ramp-up in cycle 1) or placebo in 28-day cycles with low-dose cytarabine (20 mg/m2
days 1–10). The primary endpoint was median overall survival.
Results: In the Japanese subgroup, at a 6-month follow-up from the primary analysis, medianoverall survival for venetoclax (n = 18) and placebo (n = 9), plus low-dose cytarabine, was 4.7© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium,provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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Jpn J Clin Oncol, 2021, Vol. 51, No. 9

1373

and 8.1 months, respectively (hazard ratio, 0.928, 95% conﬁdence intervals : 0.399, 2.156). The rateof complete remission plus complete remission with incomplete blood count recovery was higher
with venetoclax plus low-dose cytarabine (44.4%) vs placebo plus low-dose cytarabine (11.1%).
All patients experienced at least 1 adverse event. The most common grade ≥3 adverse eventswith venetoclax or placebo, plus low-dose cytarabine, were febrile neutropenia (50.0% vs 44.4%,
respectively) and thrombocytopenia (27.8% vs 44.4%, respectively). Serious adverse events werereported in 50.0 and 33.3% of patients in the venetoclax and placebo, plus low-dose cytarabinearms, respectively; pneumonia was the most common (22.2% each).
Conclusions: Limited survival beneﬁt in the Japanese subgroup can be attributed to small patientnumbers and to baseline imbalances observed between treatment arms, with more patients in thevenetoclax plus low-dose cytarabine arm presenting poor prognostic factors. Venetoclax plus lowdose cytarabine was well tolerated in Japanese patients with acute myeloid leukaemia ineligible
for intensive chemotherapy.
Key words: acute myeloid leukaemia, venetoclax, low-dose cytarabine, VIALE-C, Japan

Introduction
Acute myeloid leukaemia (AML), although relatively rare, is themost common adult leukaemia in Japan accounting for ∼70% of
myeloid leukaemias (1,2). The incidence of AML is age dependent
with a median age at diagnosis in Japan of 65 years (3). Survival
rates for patients with AML are the lowest amongst all leukaemias.
Retrospective population-based studies that included the overall
AML population (young adult to elderly) observed relatively low
5-year overall survival (OS) rates of 10–20% (4–7).
Standard first-line therapy for adults with newly diagnosed AML
is intensive chemotherapy (1,8). However, many patients are ineligible for intensive therapy because of advanced age or comorbidities (3,9,10). When compared with younger adult patients with
AML, elderly patients with AML also have increased frequencies ofadverse prognostic factors such as unfavourable risk karyotype andsecondary AML (11), leading to a poorer prognosis (12–14). Less
intensive treatment options include azacitidine, decitabine or lowdose cytarabine (LDAC) (8) with only LDAC monotherapy approvedin Japan (prior to March 2021) for the treatment of patients with
AML who are ineligible for intensive chemotherapy (10). However,reported rates of complete remission (CR) or CR with incomplete
blood count recovery (CRi) are ≤30% (3,15–17), underscoring theneed for additional new treatment options.
Venetoclax, a selective inhibitor of B-cell leukaemia/lymphoma-2(BCL2), has been evaluated either alone or in combination with otheractive agents in several hematologic malignancies (18–24). Venetoclax and cytarabine have complementary mechanisms of actionthat provide a biologic rationale for evaluation in AML. Cancer
cell survival is mediated by BCL2 family members, including BCL2,
BCL-XL , and MCL1. In AML, BCL2 promotes chemoresistance,
the survival of leukemic progenitor and blast cells, and has been
associated with poor outcomes (25,26). Resistance to the BCL2
inhibitor venetoclax may be mediated by other pro-survival factors,
like MCL1, that sequester endogenous BCL2 homology (BH)3-onlyproteins released by venetoclax upon binding to BCL2. In preclinical models, cytarabine synergized with venetoclax by enhancing
BH3-only activity and/or suppressing MCL1 to promote apoptosis(27,28).
Venetoclax-based therapy in elderly patients with previously
untreated AML was assessed in 2 large phase 1b/2 studies (24,29).
Combination therapy with venetoclax plus azacitidine or decitabine

resulted in a CR plus CRi rate of 67% and a median OS of
17.5 months (29). When combined with LDAC, venetoclax produced
a CR plus CRi rate of 54% and median OS of 10.1 months (24).
These results prompted the initiation of 2 phase 3 placebo-controlled
trials to compare azacitidine (VIALE-A) or LDAC (VIALE-C) with orwithout venetoclax (30,31). Both studies enrolled patients globallyand included patients from Japan. In the VIALE-A study, the additionof venetoclax to azacitidine significantly increased the CR plus CRi
rate (66% vs 28%; P < 0.001) and median OS (14.7 vs 9.6 months;
hazard ratio [HR]: 0.66; P < 0.001) compared with the control group(30). In the VIALE-C study at a 6-month follow-up analysis, theaddition of venetoclax to LDAC significantly increased the CR plus
CRi rate (48% vs 13%; P < 0.001) compared with the control group;
median OS was 8.4 months vs 4.1 months (HR, 0.70; P = 0.040)(31).
Venetoclax has been approved for use in the USA and several
other countries in combination with azacitidine, decitabine, or LDAC
in patients with newly diagnosed AML ≥75 years of age who are
ineligible for intensive induction chemotherapy; in March 2021,
venetoclax in combination with azacitidine or LDAC was approvedin Japan. Here we present efficacy and safety outcomes in thesubgroup of Japanese patients with AML ineligible for intensive
chemotherapy who participated in the VIALE-C study.

Methods
Study design
VIALE-C (NCT03069352) is a phase 3 randomized, double-blind
placebo-controlled, multicenter study that assessed the efficacy andsafety of venetoclax plus LDAC compared with placebo plus LDAC
in treatment-naive patients with AML who were ineligible for intensive chemotherapy (31). The primary endpoint of the study was OSand secondary endpoints included response rates (CR, CR plus CRi,
proportion of patients with CR plus CRi by initiation of cycle 2),transfusion independence rates, and event-free survival (EFS). The
study was conducted in accordance with the International Councilfor Harmonization requirements, Good Clinical Practice guidelines,
and the Declaration of Helsinki. The protocol was reviewed andapproved by an independent ethics committee/institutional review
board at each site before initiation. All patients provided written
informed consent before participating.

1374

Venetoclax plus LDAC in AML—Japan

Patients
Full eligibility criteria have been published previously (31). Briefly,
eligible patients were adults (≥18 years) with newly diagnosed AML,
as defined by the World Health Organization (32). Patients were ineligible for standard induction therapy either due to age (≥75 years) orlack of fitness, defined by the presence of at least 1 of the following:
Eastern Cooperative Oncology Group (ECOG) performance status
2 or 3, history of congestive heart failure requiring treatment orejection fraction ≤50% or chronic stable angina, diffusion capacityof the lung for carbon monoxide ≤65% or forced expiratory volume
in 1 second ≤65%, creatinine clearance ≥0.5 to <0.75 ml/s/m2 ,moderate hepatic impairment with total bilirubin >1.5 to ≤3.0
times the upper limit of normal, or other comorbidities deemedincompatible with standard therapy. Patients with secondary AML
could have received prior therapy for myelodysplastic syndromes(MDS). Main exclusion criteria comprised a projected life expectancy<12 weeks, prior therapy for AML (except for hydroxyurea either
prior to or during the first cycle of treatment) and previous treatment
with cytarabine for any indication.

Randomization and treatment
Patients were randomized 2:1 via interactive response technology toeither venetoclax plus LDAC or placebo plus LDAC. Randomization
was stratified by AML status (de novo vs secondary), patient age(<75 vs ≥75 years) and region (USA, Europe, China, Japan and restof world).
Venetoclax was administered orally once daily (QD), and to avoid
the risk of tumour lysis syndrome (TLS), dosing began at 100 mg on
day 1 of cycle 1 and increased stepwise over 4 days (ramp-up period)
to reach the target dose of 600 mg (100 mg day 1, 200 mg day 2,
400 mg day 3, 600 mg days 4–28 of cycle 1). Venetoclax was given
at the target dose of 600 mg QD in all subsequent 28-day cycles.
During the ramp-up period and until 24 hours after the target
dose of venetoclax was reached, all patients were hospitalized tomonitor for TLS and received TLS prophylaxis (uric acid-reducing
agents and hydration). Patients in the placebo arm received a placebo(identical-looking tablet) in the same manner as venetoclax. All
patients received subcutaneous LDAC at a dose of 20 mg/m2 QD on
days 1–10 of each 28-day cycle. Treatment was continued until disease progression (PD), unacceptable toxicity or other pre-establishedtreatment discontinuation criteria were met (31).

Assessments
Disease assessments were performed on bone marrow samples collected at screening, end of cycles 1 and 4, and every three cycles
thereafter (in the absence of PD) until 2 consecutive samples confirmed stable achievement of CR or CRi. Disease assessments werealso performed when relapse was suspected and/or at the final study
visit. Clinical responses were defined according to the modified International Working Group criteria for AML (33), and PD was definedas per European LeukemiaNet recommendations (34). Details on
the criteria for evaluating disease assessment have been reported
previously in the primary publication of this study (31). OS was
defined as the time from study randomization to death due to any
cause. EFS was defined as the time from study randomization toPD, confirmed relapse from CR or CRi, treatment failure (failure
to achieve CR, CRi, partial remission or morphologic leukaemiafree state as assessed by the investigator) or death from any cause.
Post-baseline transfusion independence was defined as a period ofat least 56 consecutive days without transfusions of either red blood

cells (RBCs) or platelets occurring between the first dose of study
drug and 30 days after the last dose of study drug.
Safety evaluations were performed throughout the study. Patientswere monitored for adverse events (AEs), serious AEs, vital signs,
laboratory measures, and clinically significant cardiac, pulmonary orradiologic findings. AEs were defined as those that occurred between
the first dose of study drug until 30 days after the last dose of study
drug. AEs were graded according to the National Cancer InstituteCommon Terminology Criteria for Adverse Events Version 4.03.

Statistical methods
Efficacy analyses were performed on the full analysis set, consistingof all patients who were randomized, whereas safety analyses were
performed on all patients who received at least 1 dose of study
drug. The pre-planned sample size for the VIALE-C study was
210 patients (randomized 2:1) to detect a statistically significant
reduction in mortality of 45.5%, with 90% power at an alpha level of
0.05. OS, and EFS were analysed using Kaplan–Meier methodology
and compared between treatment arms using the log-rank test. The
HR was estimated using the Cox proportional-hazards model. Cox
proportional-hazard regression models with stepwise variable selection were performed on OS in the Japan region as sensitivity analyses
to identify relevant prognostic factors for OS and to better understand the treatment effect on OS when adjusting for these factors.
Response rates and transfusion-independence outcomes were compared between treatment arms using the Cochran–Mantel–Haenszel
test and 95% confidence intervals (CIs) were determined using the
Clopper–Pearson Exact method.
For this analysis, baseline characteristics and study outcomes aredescribed for the Japanese subgroup. The data cutoff for the primary
analysis of the study was 15 February 2019; the cutoff for the 6month follow-up analysis presented herein was 15 August 2019.

Results
Patient demographics and baseline characteristics
Between May 2017 and the data cutoff for the 6-month follow-up
analysis of 15 August 2019, 211 patients were enrolled across 21
countries. Of the 211 patients,14 sites in Japan enrolled 27 (12.8%)
patients who received venetoclax plus LDAC (n = 18) or placebo
plus LDAC (n = 9). The median age was 81 (range: 60–89) years
for patients treated with venetoclax plus LDAC and 78 (range: 71–85) years for those treated with placebo plus LDAC. Demographics
and baseline characteristics of the Japanese subgroup are shown in
Table 1. Treatment arms were balanced in terms of patient age (85%of patients ≥75 years; venetoclax plus LDAC vs placebo plus LDAC:
83.3% vs 88.9%), the proportion of patients with de novo AML,
and the prevalence of transfusion dependence. Some imbalanceswere observed between the venetoclax plus LDAC and placebo plus
LDAC arms, including the proportion of patients with AML withmyelodysplasia-related changes (venetoclax plus LDAC vs placebo
plus LDAC: 55.6% vs 44.4%), bone marrow blast count ≥50%
(27.8% vs 11.1%), poor cytogenetic risk (55.6% vs 33.3%), tumour
protein 53 (TP53) mutations (43.8% vs 22.2%), fms-like tyrosine
kinase 3 (FLT3) mutations (18.8% vs 0%), nucleophosmin (NPM1)
mutations (6.3% vs 22.2%) and prior use of hypomethylating agents(HMAs) (16.7% vs 33.3%). More than half of the patients in thevenetoclax plus LDAC treatment arm (n = 10 [55.6%]) and n = 4[44.4%] patients in the placebo plus LDAC arm had ≥2 reasons forineligibility to receive intensive therapies.

Jpn J Clin Oncol, 2021, Vol. 51, No. 9

1375

Table 1. Patient demographics and baseline characteristics
Characteristic
AgeMedian, years (range)≥75 years, n (%)
Male, n (%)
ECOG performance status, n (%)
0
1
2
3
AML type, n (%)
De novo
Secondary
Secondary AML type, n/N (%)
Treatment-related AML
Prior hematologic disorder
Myelodysplasia-related changes, n (%)
Prior treatment with HMAs, n (%)
Bone marrow blast count, n (%)<30%≥30%–<50%≥50%
Cytogenetic risk, n (%)
Favourable
Intermediate
Poor
Somatic mutations, n/N (%)
TP53
FLT3
IDH1/2
NPM1
Baseline hepatic impairment
Baseline renal impairment
Transfusion dependenta at baseline, n (%)
RBC or platelet
RBC
PlateletNumber of reasons for ineligibility to receive intensive therapy, n (%)
1
2
3
≥4

Placebo + LDAC (n = 9)

Venetoclax + LDAC (n = 18)

78 (71–85)
8 (88.9)
4 (44.4)

81 (60–89)
15 (83.3)
12 (66.7)

3 (33.3)
3 (33.3)
2 (22.2)
1 (11.1)

5 (27.8)
10 (55.6)
3 (16.7)
0

6 (66.7)
3 (33.3)

13 (72.2)
5 (27.8)

0/3
3/3 (100.0)
4 (44.4)
3 (33.3)

1/5 (20.0)
4/5 (80.0)
10 (55.6)
3 (16.7)

4 (44.4)
4 (44.4)
1 (11.1)

7 (38.9)
6 (33.3)
5 (27.8)0
6 (66.7)
3 (33.3)

08 (44.4)
10 (55.6)

2/9 (22.2)
0/9
2/9 (22.2)
2/9 (22.2)
3 (33.3)
8 (88.9)

7/16 (43.8)
3/16 (18.8)
3/16 (18.8)
1/16 (6.3)
8 (44.4)
18 (100.0)

7 (77.8)
6 (66.7)
3 (33.3)

14 (77.8)
13 (72.2)
8 (44.4)

5 (55.6)
4 (44.4)008 (44.4)
9 (50.0)
1 (5.6)0a Transfusion dependence defined as transfusion within 56 days before first dose of study drug.

AML, acute myeloid leukaemia; ECOG, Eastern Cooperative Oncology Group; FLT3, fms-like tyrosine kinase 3; HMA, hypomethylating agent; IDH, isocitrate
dehydrogenase; LDAC, low-dose cytarabine; NPM1, nucleophosmin; RBC, red blood cell; TP53, tumour protein 53.

The median treatment duration in the venetoclax plus LDAC andplacebo plus LDAC arms was 2.1 (range: 0.2–23.5) months and 1.9(range: 0.3–14.6) months, respectively. The proportion of patients
who received any post-study treatment and intensive chemotherapy
as post-study treatment was markedly higher in the placebo plus
LDAC arm (77.8 and 55.6%, respectively) than in the venetoclax
plus LDAC arm (27.8 and 16.7%, respectively; Table 2). The mostcommon post-study treatments in the placebo plus LDAC arm werecytarabine (66.7%), aclarubicin hydrochloride (33.3%), and hydroxycarbamide (33.3%); azacitidine, cytarabine, daunorubicin and gemtuzumab ozogamicin (11.1% each) were the most common forpatients in the venetoclax plus LDAC arm. Individual chemotherapy
drugs were noted, but treatment regimens were not.

Overall, as of the data cutoff date, 26 patients in the Japanese
subgroup (venetoclax plus LDAC, n = 17; placebo plus LDAC,n = 9) had discontinued treatment. The primary reasons for study
drug discontinuation were (venetoclax plus LDAC vs placebo plus
LDAC): treatment failure (22.2% vs 44.4%), PD (11.1% vs 33.3%),
physician decision (16.7% vs 11.1%), AE not related to PD (16.7%
vs 0%), withdrawal of consent (11.1% each), morphologic relapse(11.1% vs 0%) and AE related to PD (5.6% vs 0%).

Efﬁcacy
OS outcomes in the Japanese subgroup at the primary analysis
and at the 6-month follow-up analysis are shown in Fig. 1A and B,
respectively. At both analyses, median OS was 4.7 months in the1376

Venetoclax plus LDAC in AML—Japan

Table 2. Summary of post-study treatment
Treatment, n (%)

Placebo + LDAC (n = 9)

Venetoclax + LDAC (n = 18)

Any post-study treatment
Intensive chemotherapy
Aclarubicin/aclarubicin hydrochloride
Cytarabine
Daunorubicin/daunorubicin hydrochloride

7 (77.8)
5 (55.6)
4 (44.4)
4 (44.4)
2 (22.2)

5 (27.8)
3 (16.7)
1 (5.6)
2 (11.1)
3 (16.7)

Treatment regimen was not collected, only individual chemotherapy drug.
LDAC, low-dose cytarabine.

Figure 1. Overall survival (OS) by treatment arm at the primary analysis (A) and 6-month follow-up (B). a Unstratiﬁed Cox proportional hazards model. CI,
conﬁdence interval; HR, hazard ratio; LDAC, low-dose cytarabine; PBO, placebo; VEN, venetoclax.

venetoclax plus LDAC arm, and 8.1 months in the placebo plus
LDAC arm. The HR at the 6-month follow-up was 0.928 (95%
CI: 0.399–2.156). Considering the observed imbalance in baseline
patient characteristics, a stepwise multivariate Cox regression analysis was performed to identify pre-treatment factors associated with
OS. Factors included in the analysis were treatment arm, age, sex,
AML status, bone marrow blast count, ECOG performance status,

cytogenetic risk, prior use of HMAs and mutation status of FLT3,
isocitrate dehydrogenase (IDH) and NPM1. To estimate the adjusted
treatment effect, inclusion of treatment arm was forced into themodel. Based on the stepwise variable selection, cytogenetic risk was
identified as being significantly correlated with OS. At the 6-monthfollow-up, the HR for cytogenetic risk (intermediate vs poor) was
0.264 (95% CI: 0.102–0.685; P = 0.006). The covariate-adjusted HR

Jpn J Clin Oncol, 2021, Vol. 51, No. 9

1377

Figure 2. Rates of complete response (CR) (A), CR + CR with incomplete blood count recovery (CRi) (B), and CR + CRi by initiation of cycle 2 (C). LDAC, low-dose
cytarabine; PBO, placebo; VEN, venetoclax.

for treatment arm (venetoclax plus LDAC vs placebo plus LDAC) was
0.800 (95% CI: 0.337–1.898), which was similar to that observed in
the primary analysis.
Response rates for the Japanese subgroup are summarized inFig. 2. The rates of CR and CR plus CRi were consistently higher
in patients treated with venetoclax plus LDAC (22.2 and 44.4%,
respectively) than in patients treated with placebo plus LDAC (11.1%
each). The proportion of patients achieving CR plus CRi by initiationof cycle 2 was also higher in the venetoclax plus LDAC arm (44.4%)
vs the placebo plus LDAC arm (0%).
Median EFS was numerically higher in the venetoclax plus LDAC
arm vs the placebo plus LDAC arm both at the primary analysis (3.7
vs 2.2 months; HR: 0.565; 95% CI: 0.197–1.624; Fig. 3A) and at the
6-month follow-up (3.7 vs 2.2 months; HR: 0.620; 95% CI: 0.246–
1.566; Fig. 3B).
The proportion of patients with post-baseline transfusion independence was similar across treatment groups in the Japanese subgroup (Fig. 4A). The median time to transfusion independence (RBC
plus platelet) was shorter in the venetoclax plus LDAC arm than in
the placebo plus LDAC arm (50 vs 96 days, respectively; Fig. 4B).
One of 14 (7.1%) patients who were transfusion-dependent atbaseline achieved transfusion independence during treatment withvenetoclax plus LDAC, and 1 of 7 (14.3%) transfusion-dependentpatients assigned to placebo plus LDAC achieved transfusion independence.

SafetyAll patients in the Japanese subgroup experienced at least 1 AE
(Table 3). The most frequently reported AEs (≥40% of patients)of any grade for the venetoclax plus LDAC or placebo plus LDAC
arms, respectively, were nausea (66.7% vs 22.2%), febrile neutropenia (50.0% vs 44.4%), vomiting (50.0% vs 11.1%), decreased
appetite (33.3% vs 44.4%), hypokalemia (33.3% vs 44.4%), thrombocytopenia (27.8% vs 44.4%) and pyrexia (22.2% vs 44.4%).
The most frequently reported grade ≥ 3 AEs in the venetoclax
plus LDAC or placebo plus LDAC arms, respectively, were hematologic: febrile neutropenia (50.0% vs 44.4%) and thrombocytopenia(27.8% vs 44.4%). Grade ≥ 3 pneumonia was reported in 3/18
(16.7%) patients in the venetoclax plus LDAC arm and in 3/9(33.3%) in the placebo plus LDAC arm.
Serious AEs were reported in 9/18 (50.0%) and 3/9 (33.3%)
patients in the venetoclax plus LDAC and placebo plus LDAC

arms, respectively; pneumonia was the most common (22.2% each;
Table 4). TLS was not observed in any patients in the Japanese
subgroup. Fourteen (77.8%) patients died in the venetoclax plus
LDAC arm and 9 (100.0%) in the placebo plus LDAC arm, mainly
because of PD (61.1 and 77.8%, respectively). The rate of deathwithin 60 days of initiating study treatment was similar in both
treatment group (11.1% each).
AEs led to study treatment discontinuation in 6/18 (33.3%)
and 0/9 patients treated with venetoclax plus LDAC and placebo
plus LDAC, respectively, including pneumonia (11.1% vs 0%), andneutropenia, congestive heart failure, acute pancreatitis, fatigue,
decreased appetite, intracranial haemorrhage and organizing pneumonia (5.6% vs 0% for each). Dose interruption and/or reduction
occurred in 11/18 (61.1%) patients in the venetoclax plus LDAC
arm and 4/9 (44.4%) patients in the placebo plus LDAC arm. The
most common AEs (≥10% of patients) leading to dose interruption and/or reduction were (venetoclax plus LDAC vs placebo plus
LDAC): febrile neutropenia (16.7% vs 11.1%), thrombocytopenia(11.1% each), decreased neutrophil count (11.1% vs 0%), upper GI
haemorrhage (11.1% vs 0%), atrial fibrillation (5.6% vs 11.1%),
pneumonia (5.6% vs 11.1%), cellulitis (0% vs 11.1%) and fasciitis(0% vs 11.1%).

Discussion
In this small subgroup of Japanese patients participating in VIALE-C(n = 27), the unadjusted OS appeared comparable though unbalanced
covariates may have obscured the observation of treatment effect. At
a 6-month follow-up analysis, the addition of venetoclax to LDAC
showed a median OS of 4.7 months vs 8.1 months with placebo
plus LDAC. In contrast, median OS of the total VIALE-C study
population at a 6-month follow-up was 8.4 and 4.1 months forvenetoclax plus LDAC and placebo plus LDAC, respectively (31).
Several important factors may have influenced outcomes in theJapanese subgroup. First, the number of patients in the subgroupwas small (venetoclax plus LDAC, n = 18; placebo plus LDAC,n = 9). Second, patients in the venetoclax plus LDAC arm weremore likely to have high-risk features at baseline than patients in theplacebo plus LDAC arm, including a poor-risk cytogenetic profile(55.6% vs 33.3%, respectively), which was shown to correlate with
OS. After adjusting for cytogenetic risk in Japanese patients, theHR for the venetoclax plus LDAC treatment arm was 0.800 (95%

1378

Venetoclax plus LDAC in AML—Japan

Figure 3. Event-free survival (EFS) by treatment arm at the primary analysis (A) and 6-month follow-up (B). a Stratiﬁed by AML state (de novo vs secondary) andage (18–74 vs ≥75 years). AML, acute myeloid leukaemia; CI, conﬁdence interval; LDAC, low-dose cytarabine; PBO, placebo; VEN, venetoclax.

CI: 0.337–1.898). Last, patients in the Japanese subgroup assigned
to the venetoclax plus LDAC arm were less likely to receive poststudy treatment than those in the placebo plus LDAC arm (27.8%
vs 77.8%, respectively), including intensive chemotherapy (16.7%
vs 55.6%, respectively), which may have reduced the ability todetect the effects of study treatment on OS in this subgroup. It isnoteworthy that the decision to administer post-study treatment andthe choice of regimen to be used was at the investigator’s discretion.
The selection of further therapy was dependent upon importantintermediate events during the study, such as treatment failure or PD.
In the Japanese subgroup of the VIALE-C study, treatment failure and
PD were reported as the primary reason for study discontinuation atleast twice as often in the placebo plus LDAC arm (33.3 and 44.4%,
respectively) compared with venetoclax plus LDAC arm (11.1 and22.1%, respectively). The increased use of intensive chemotherapy
as salvage treatment in Japanese patients compared with the total
study population (29.6% vs 12.7%, respectively), and the greater

use amongst Japanese patients randomized to the placebo plus LDAC
arm compared with the venetoclax plus LDAC arm (n = 5/9 [55.6%]
vs 3/18 [16.7%], respectively), may have reduced the ability todetect the effect of study treatment on survival within the Japanese
subgroup.
A broad consensus on criteria for selection of the ideal ‘unfit’
patient with AML for inclusion in clinical trials was lacking andremains the subject of scientific debate. The structure for identifying
patients who would not be suitable for intensive treatment in the
VIALE-C, as well as the VIALE-A study, was based on age ≥ 75 years
or age ≥ 18 to 74 years plus at least 1 criterion associated withlack of fitness for intensive induction chemotherapy (e.g. ECOG
performance status of 2 or 3, particular defined comorbid conditions)(30,31). It is noteworthy that eligibility criteria for VIALE-C include
prior MDS treated with HMAs, whereas VIALE-A excluded pretreated MDS. Thus, VIALE-C included more patients with refractory
MDS than VIALE-A, which may partially explain the difference inJpn J Clin Oncol, 2021, Vol. 51, No. 9

1379

Figure 4. Proportion of patients with post-baseline transfusion independence (A) and median time to post-baseline transfusion independence (B), by treatment
arm. Post-baseline transfusion independence was deﬁned as a period of at least 56 consecutive days without transfusions. LDAC, low-dose cytarabine; PBO,
placebo; RBC, red blood cell; VEN, venetoclax.

median OS between both studies. In a commentary by Löwenberg etal., it was noted that by using these inclusion criteria, a considerable
proportion of the study population for these trials may not have
only been ‘unfit’, but potentially ‘frail’ (e.g. patients had ECOG
performance status of 3). Thus, a proportion of enrolled patientsmight have been too frail to benefit from almost any antileukemic
treatment that introduces toxicities (35).
Despite the imbalances between treatment arms in baseline characteristics (e.g. poor cytogenetic risk, bone marrow blast count≥50%, prior treatment with HMAs, TP53, NPM1, or FLT3 mutation, and AML with myelodysplasia-related changes) and older
median age, the addition of venetoclax to LDAC was associatedwith an increased CR plus CRi rate in Japanese patients (44.4% vs11.1%), and all responses to treatment with venetoclax plus LDAC
were achieved within the first cycle of therapy (vs 0% with placebo
plus LDAC). The transfusion-independence rate was similar in thevenetoclax plus LDAC and placebo plus LDAC arms likely due to thesmall patient numbers. In addition, transfusion prescription was notdefined within the protocol and was at the investigator’s discretion
which could depend on institutional/regional guidelines. However,transfusion independence (RBC plus platelet) was achieved more
rapidly with venetoclax plus LDAC compared with placebo plus
LDAC (median 50 vs 96 days, respectively).
Of note, the differences in rates of post-study treatment received
between treatment arms did not obscure the treatment effect ofvenetoclax plus LDAC when evaluating EFS as a secondary endpoint.
At the 6-month follow-up, the Japanese subgroup reported a median
EFS of 2.2 months in the placebo plus LDAC arm compared with
3.7 months in the venetoclax plus LDAC arm (HR: 0.620; 95% CI:
0.246–1.566), which was an opposite trend relative to the OS observation. It is noteworthy that the analysis of treatment arms showsa separation of EFS curves that implies benefit of venetoclax plus
LDAC over placebo plus LDAC prior to receipt of subsequent salvage

therapy. These data suggest that patients treated with venetoclax plus
LDAC derived clinical benefit from therapy.
The safety profile of venetoclax plus LDAC was consistent withprevious studies of venetoclax in AML, including the total study population of VIALE-C (24,29–31). AEs consisted mainly of hematologic
events, such as febrile neutropenia and thrombocytopenia, and GI
AEs (e.g. grade 1 or 2 nausea and vomiting). No cases of TLS werereported in the Japanese subgroup.
Despite the limitations of the current analysis (e.g. small patientnumbers, imbalances between treatment arms in baseline characteristics, impact of post-study treatment), the data indicate a tolerable
safety profile along with a trend toward beneficial improvements forpatients treated with venetoclax plus LDAC in comparison to placebo
plus LDAC. Treatment with venetoclax plus LDAC was well tolerated
and led to higher CR plus CRi rates in comparison to treatment
with placebo plus LDAC. These results support the consideration ofvenetoclax plus LDAC as a first-line treatment option for Japanesepatients with AML ineligible for intensive chemotherapy.

Data sharing and data accessibility statement
AbbVie is committed to responsible data sharing regarding the clinical trials we sponsor. This includes access to anonymized, individual,
and trial-level data (analysis data sets), as well as other information(e.g. protocols and Clinical Study Reports), as long as the trials
are not part of an ongoing or planned regulatory submission. This
includes requests for clinical trial data for unlicensed products andindications.
These clinical trial data can be requested by any qualified
researchers who engage in rigorous, independent scientific research,
and will be provided following review and approval of a research
proposal and statistical analysis plan and execution of a data sharing

1380

Venetoclax plus LDAC in AML—Japan

Table 3. Adverse events (AEs) reported in ≥15% of patients in the venetoclax plus low-dose cytarabine (LDAC) arm only
AE, n (%)

Any
Hematologic
Febrile neutropenia
Leukopenia
Neutropenia
Thrombocytopenia
Nonhematologic
Back pain
Constipation
Decreased appetite
Decreased weight
Delirium
Diarrhoea
Dry skin
Epistaxis
Fatigue
Hypokalemia
Increased blood bilirubin
Insomnia
Malaise
NauseaOropharyngeal pain
Peripheral edema
Pneumonia
Proctalgia
Pyrexia
Transfusion reaction
Upper GI haemorrhage
Vomiting

Placebo + LDAC (n = 9)

Venetoclax + LDAC (n = 18)

Any grade

Grade ≥ 3

Any grade

Grade ≥ 3

9 (100.0)

8 (88.9)18 (100.0)

17 (94.4)

4 (44.4)00
4 (44.4)

4 (44.4)00
4 (44.4)

9 (50.0)
3 (16.7)
3 (16.7)
5 (27.8)

9 (50.0)
3 (16.7)
3 (16.7)
5 (27.8)

1 (11.1)
3 (33.3)
4 (44.4)
0
1 (11.1)
2 (22.2)0004 (44.4)
02 (22.2)
2 (22.2)
2 (22.2)
1 (11.1)
3 (33.3)
3 (33.3)
04 (44.4)
00
1 (11.1)

0
0
0
0
0
0
0
0
0
3 (33.3)
0
0
0
0
0
0
3 (33.3)
0
1 (11.1)000

3 (16.7)
5 (27.8)
6 (33.3)
3 (16.7)
3 (16.7)
5 (27.8)
3 (16.7)
3 (16.7)
3 (16.7)
6 (33.3)
3 (16.7)
5 (27.8)
3 (16.7)
12 (66.7)
3 (16.7)
4 (22.2)
5 (27.8)
4 (22.2)
4 (22.2)
4 (22.2)
3 (16.7)
9 (50.0)00
1 (5.6)00
0
00
1 (5.6)
3 (16.7)00001 (5.6)0
3 (16.7)
01 (5.6)0
1 (5.6)0

GI, gastrointestinal.

Table 4. Serious adverse events (AEs) reported in all patients
Serious AE, n (%)

Placebo + LDAC (n = 9)

Venetoclax + LDAC (n = 18)

AnyPneumonia
Multiple organ dysfunction syndrome
Enterococcal infection
Upper GI haemorrhage
Congestive cardiac failure
GI haemorrhage
Intracranial haemorrhage
Acute pancreatitis
Acute cholecystitis
Neutrophil count decreased
WBC count decreased

3 (33.3)
2 (22.2)
1 (11.1)
1 (11.1)
0
0
0
0
0
0
0
0

9 (50.0)
4 (22.2)00
2 (11.1)
1 (5.6)1 (5.6)1 (5.6)1 (5.6)1 (5.6)1 (5.6)1 (5.6)GI, gastrointestinal; LDAC, low-dose cytarabine; WBC, white blood cell.

agreement. Data requests can be submitted at any time, and the data
will be accessible for 12 months, with possible extensions considered.
For more information on the process, or to submit a request, visit
the following link: https://www.abbvie.com/our-science/clinical-tria
ls/clinical-trials-data-and-information-sharing/data-and-information-sharing-with-qualified-researchers.html.

Author contributions
Takahiro Yamauchi: investigation and writing (reviewing and editing). Chikashi Yoshida: investigation and writing (reviewing andediting). Kensuke Usuki: investigation and writing (reviewing andediting). Satoru Takada: investigation and writing (reviewing andediting). Itaru Matsumura: investigation and writing (reviewing andJpn J Clin Oncol, 2021, Vol. 51, No. 9
editing). Nobuaki Dobashi: investigation and writing (reviewing andediting). Yasushi Miyazaki: investigation and writing (reviewing andediting). Toshihiro Miyamoto: investigation and writing (reviewing
and editing). Hiroatsu Iida: investigation and writing (reviewing
and editing). Norio Asou: investigation and writing (reviewing andediting). Junya Kuroda: investigation and writing (reviewing andediting). Satoshi Ichikawa: investigation and writing (reviewing andediting). Norio Komatsu: investigation and writing (reviewing andediting). Wellington Mendes: supervision, writing (original draft
preparation) and writing (reviewing and editing). Hideyuki Honda:
formal analysis, data curation and writing (reviewing and editing).
Sumiko Okubo: formal analysis, data curation and writing (reviewing and editing). Misaki Kurokawa: formal analysis, data curation and writing (reviewing and editing). Qi Jiang: formal analysis,
data curation and writing (reviewing and editing). Andrew Wei:
investigation and writing (reviewing and editing). Kenichi Ishizawa:
investigation and writing (reviewing and editing).

Acknowledgements
AbbVie and the authors thank all the trial investigators and thepatients who participated in this clinical trial as well as Sathej
Gopalakrishnan and Jiuhong Zha for their contributions to thispublication. Medical writing support was provided by Mary L.
Smith, PhD, CMPP from Aptitude Health, Atlanta, GA, USA andfunded by AbbVie.

Funding
Venetoclax is being developed in a collaboration between AbbVie
and Genentech. AbbVie sponsored the study, contributed to its
design, collection, analysis and interpretation of the data, and participated in the writing, review and approval of the manuscript. All
authors had access to relevant data and participated in the drafting,review and approval of this manuscript. No honoraria or payments
were made for authorship.

Conﬂict of interest statement
Takahiro Yamauchi has received research support and honoraria
from and has served as an advisor for AbbVie, Astellas Pharma,
Boehringer Ingelheim, Chugai Pharmaceutical Co., Ltd, Gilead Sciences, Inc., Mundipharma, Jansen (research support only), Ono Pharmaceutical, Otsuka Pharmaceutical Co., Ltd, Pfizer, Solasia Pharma,
SymBio, Takeda and Teijin Pharma. Chikashi Yoshida has received
honoraria from AbbVie GK, Astellas Pharma, Bristol-Myers Squibb,
Daiichi Sankyo Co. Ltd, Janssen Pharmaceutical KK, Meiji Seika
Pharma Co., Ltd, Nippon Shinyaku Co., Ltd, Novartis Pharma KK
and Otsuka Pharmaceutical Co., Ltd Kensuke Usuki has received
research funding from AbbVie, Alexion, Astellas Pharma, Chugai
Pharmaceutical Co., Ltd, Daiichi Sankyo Co., Gilead Sciences, Inc.,
Sumitomo Dainippon Pharma and SymBio, and has received honoraria from Novartis. Satoru Takada has nothing to disclose. Itaru
Matsumura has received research funding from Chugai Pharmaceutical Co., Ltd, Eisai, Kyowa Kirin Co. Ltd, Shionogi & Co. Ltd andSumitomo Dainippon Pharma, and has served on a speakers bureau
for Astellas Pharma, Bristol-Myers Squibb, Inc., Daiichi Sankyo
Co. Ltd, Janssen Pharmaceutical KK, Novartis Pharma KK, Otsuka
Pharmaceutical Co., Ltd and Pfizer Japan Inc. Nobuaki Dobashi
from Otsuka Pharmaceutical Co., Ltd. Yasushi Miyazaki has received
honoraria from Astellas Pharma, Celgene, Chugai Pharmaceutical

1381

Co., Ltd, Kyowa Kirin, Nippon Shinyaku, Novartis, Otsuka Pharmaceutical Co., Ltd and Sumitomo Dainippon Pharma, and hasreceived research funding from Chugai Pharmaceutical Co., Ltd andSumitomo Dainippon Pharma. Toshihiro Miyamoto has received
honoraria from AbbVie, Astellas Pharmaceutical Co., Ltd, Astellas
Amgen Pharmaceutical Co., Ltd, Bristol-Myers Squibb, Celgene,
Merch Sharp & Dohme, Otsuka Pharmaceutical Co., Ltd and Takeda
Pharmaceutical Co. Hiroatsu Iida has received research funding from
Chugai Pharmaceutical Co., Ltd, and has received honoraria from
Astellas Pharma, Celgene, Janssen and Novartis. Norio Asou hasreceived research funding from AbbVie; Astellas Pharma, Chugai
Pharmaceutical Co., Ltd, Eisai and Sumitomo Dainippon Pharma;has served on a speakers bureau for Asahi Kasei, Fuji Pharma, Nippon Shinyaku and Sumitomo Dainippon Pharma; and has received
honoraria from Novartis and Nippon Shinyaku. Junya Kuroda hasreceived honoraria from AbbVie, Astellas Pharma, Bristol-Myers
Squibb, Celgene, Chugai Pharmaceutical Co., Ltd, Daiichi SankyoCo., Eisai, Fujimoto Pharmaceutical, Janssen Pharmaceutical KK,
Kyowa Kirin, Nippon Shinyaku, Ono Pharmaceutical, Otsuka Pharmaceutical Co., Ltd, Pfizer, Sanofi, Sumitomo Dainippon Pharma
and Takeda Pharmaceutical; has received research funding: from
AbbVie, Asahi Kasei, Astellas Pharma, Bristol-Myers Squibb, Celgene, Chugai Pharmaceutical Co., Ltd, Daiichi Sankyo Co., Eisai,
Fujimoto Pharmaceutical, Kyowa Kirin, MSD, Nippon Shinyaku,
Ono Pharmaceutical, Otsuka Pharmaceutical Co., Ltd, Pfizer, Sanofi,
Shionogi, Sumitomo Dainippon Pharma, Sysmex, Taiho Pharmaceutical and Takeda Pharmaceutical; and has served as a consultant for
AbbVie, Bristol-Myers Squibb, Celgene, Janssen Pharmaceutical KK
and Sanofi. Satoshi Ichikawa from Chugai Pharmaceutical Co., Ltd.
Norio Komatsu has received research funding from Bristol-Myers
Squibb KK, Chugai Pharmaceutical Co., Ltd, Fujifilm Wako Pure
Chemical Corporation, Fuso Pharmaceutical Industries, Ltd, Kyowa
Hakko Kirin Co., Ltd, Otsuka Pharmaceutical Co., Ltd, Novartis
Pharma KK, Pfizer Japan Inc., PharmaEssentia Japan KK, Shire
Japan KK, Sumitomo Dainippon Pharma and Takeda PharmaceuticalCo., Ltd; has received advisory fees from AbbVie GK, Celgene KK,
Japan Tobacco Inc. (Funding provided was intended only to support
the investigator and does not support the manuscript or any work
involved in producing the manuscript. The manuscript content is
not in any way related to the tobacco industry), Novartis Pharma
KK, Otsuka Pharmaceutical Co., Ltd, PharmaEssentia Japan KK
and Shire Japan KK; has served on a speakers bureau for Novartis Pharma KK, Shire Japan KK and Takeda Pharmaceutical Co.,
Ltd; and served as a member of safety assessment committee inthe M13–834 clinical trial. Wellington Mendes, Hideyuki Honda,
Sumiko Okubo, Misaki Kurokawa and Qi Jiang are employees of
AbbVie and may own stock. Andrew Wei has received researchfunding from AbbVie, Amgen, AstraZeneca, Celgene/BMS, Novartis,
Servier and F.Hoffmann-La Roche; has received honoraria from AbbVie, Amgen, Astellas, AstraZeneca, Celgene/Bristol-Myers Squibb,
Genentech, Janssen, MacroGenics, Novartis, Pfizer and Servier; hasserved on advisory boards for AbbVie, Amgen, Astellas, AstraZeneca,
Celgene/Bristol-Myers Squibb, Genentech, Janssen, MacroGenics,
Novartis, Pfizer and Servier; has served on a speakers bureau forNovartis, AbbVie and Celgene/Bristol-Myers Squibb; has served asa consultant for Servier; is a former employee of the Walter andEliza Hall Institute of Medical Research, which receives royalties
in relation to venetoclax, and is entitled to a fraction of these payments. Kenichi Ishizawa has received research funding from AbbVie,
Bayer, Novartis and SymBio, and has served on a speakers bureau
for Celgene, Chugai Pharmaceutical Co., Ltd, Eisai, Novartis, Ono
Pharmaceutical and Takeda.

1382

Venetoclax plus LDAC in AML—Japan

Statement of prior presentation
Results of this study have been partially presented at the 43rd Annual
Meeting of the Japan Society for Hematopoietic Cell Transplantation
Congress, Virtual, 5–7 March 2021.

Statement on originality of the work
The manuscript represents original work and has not been submitted
for publication elsewhere nor previously published.

Abbreviations
AE, adverse event; AML, acute myeloid leukaemia; BCL2, B-cellleukaemia/lymphoma-2; BH, BCL2 homology; CI, confidence interval; CR, complete remission; CRi, complete remission with incomplete blood count recovery; ECOG, Eastern Cooperative Oncology
Group; EFS, event-free survival; FLT3, fms-like tyrosine kinase 3; GI,
gastrointestinal; HMA, hypomethylating agent; HR, hazard ratio;
IDH, isocitrate dehydrogenase; LDAC, low-dose cytarabine; MDS,
myelodysplastic syndromes; NPM1, nucleophosmin; OS, overall survival; PBO, placebo; QD, once daily; RBC, red blood cell; TLS,
tumour lysis syndrome; TP53, tumour protein 53; VEN, venetoclax;
WBC, white blood cell.

References1.

Miyawaki S. Clinical studies of acute myeloid leukemia in the Japan Adult
Leukemia Study Group. Int J Hematol 2012;96:171–7.
2. Niino M, Matsuda T. Type distribution of myeloid leukemia from Cancer
Incidence in Five Continents Vol. X. Jpn J Clin Oncol 2016;46:394.
3. Tasaki T, Yamauchi T, Matsuda Y, et al. The response to induction therapy
is crucial for the treatment outcomes of elderly patients with acute myeloid
leukemia: single-institution experience. Anticancer Res 2014;34:5631–6.
4. Bhayat F, Das-Gupta E, Smith C, McKeever T, Hubbard R. The incidenceof and mortality from leukaemias in the UK: a general population-based
study. BMC Cancer 2009;9:252.
5. Phekoo KJ, Richards MA, Møller H, Schey SA. South Thames Haematology Specialist Committee. The incidence and outcome of myeloid
malignancies in 2,112 adult patients in southeast England. Haematologica
2006;91:1400–4.
6. Pulte D, Gondos A, Brenner H. Improvements in survival of adults
diagnosed with acute myeloblastic leukemia in the early 21st century.
Haematologica 2008;93:594–600.
7. Ohnishi H, Imataki O, Kawachi Y, Ide M, Kawakami K, Waki M, et al.
Age is an independent adverse prognostic factor for overall survival inacute myeloid leukemia in Japan. World J Hematol 2014;3:105–14.
8. Heuser M, Ofran Y, Boissel N, et al. Acute myeloid leukaemia in adultpatients: ESMO Clinical Practice Guidelines for diagnosis, treatment andfollow-up. Ann Oncol 2020;31:697–712.
9. Kantarjian H, Ravandi F, O’Brien S, et al. Intensive chemotherapy does
not benefit most older patients (age 70 years or older) with acute myeloid
leukemia. Blood 2010;116:4422–9.
10. Kiyoi H, Yamaguchi H, Maeda Y, Yamauchi T. JSH practical guidelines for hematological malignancies, 2018: I. leukemia-1. Acute myeloid
leukemia (AML). Int J Hematol 2020;111:595–613.
11. Grimwade D, Hills RK, Moorman AV, et al. Refinement of cytogeneticclassification in acute myeloid leukemia: determination of prognostic
significance of rare recurring chromosomal abnormalities among 5876
younger adult patients treated in the United Kingdom Medical ResearchCouncil trials. Blood 2010;116:354–65.
12. Estey E. Acute myeloid leukemia and myelodysplastic syndromes in older
patients. J Clin Oncol 2007;25:1908–15.
13. Dombret H, Raffoux E, Gardin C. Acute myeloid leukemia in the elderly.
Semin Oncol 2008;35:430–8.

14. Pollyea DA, Kohrt HE, Medeiros BC. Acute myeloid leukaemia in theelderly: a review. Br J Haematol 2011;152:524–42.
15. He P-F, Zhou J-D, Yao D-M, et al. Efficacy and safety of decitabine intreatment of elderly patients with acute myeloid leukemia: a systematic
review and meta-analysis. Oncotarget 2017;8:41498–507.
16. Dombret H, Seymour JF, Butrym A, et al. International phase 3 studyof azacitidine vs conventional care regimens in older patients with newly
diagnosed AML with >30% blasts. Blood 2015;126:291–9.
17. Kantarjian HM, Thomas XG, Dmoszynska A, et al. Multicenter, randomized, open-label, phase III trial of decitabine versus patient choice, withphysician advice, of either supportive care or low-dose cytarabine for thetreatment of older patients with newly diagnosed acute myeloid leukemia.
J Clin Oncol 2012;30:2670–7.
18. Fischer K, Al-Sawaf O, Fink A-M, et al. Venetoclax and obinutuzumab inchronic lymphocytic leukemia. Blood 2017;129:2702–5.
19. Kumar S, Kaufman JL, Gasparetto C, et al. Efficacy of venetoclax astargeted therapy for relapsed/refractory t(11;14) multiple myeloma. Blood
2017;130:2401–9.
20. Moreau P, Chanan-Khan A, Roberts AW, et al. Promising efficacy andacceptable safety of venetoclax plus bortezomib and dexamethasone in
relapsed/refractory MM. Blood 2017;130:2392–400.
21. Roberts AW, Davids MS, Pagel JM, et al. Targeting BCL2 withvenetoclax in relapsed chronic lymphocytic leukemia. N Engl J Med
2016;374:311–22.
22. Seymour JF, Kipps TJ, Eichhorst B, et al. Venetoclax–rituximab in
relapsed or refractory chronic lymphocytic leukemia. N Engl J Med
2018;378:1107–20.
23. Stilgenbauer S, Eichhorst B, Schetelig J, et al. Venetoclax in relapsed or
refractory chronic lymphocytic leukaemia with 17p deletion: a multicentre, open-label, phase 2 study. Lancet Oncol 2016;17:768–78.
24. Wei AH, Strickland SA Jr, Hou J-Z, et al. Venetoclax combined with
low-dose cytarabine for previously untreated patients with acute myeloid
leukemia: results from a phase Ib/II study. J Clin Oncol 2019;37:1277–84.
25. Del Poeta G, Venditti A, Del Principe MI, et al. Amount of spontaneous
apoptosis detected by Bax/Bcl-2 ratio predicts outcome in acute myeloid
leukemia (AML). Blood 2003;101:2125–31.
26. Lagadinou ED, Sach A, Callahan K, et al. BCL-2 inhibition targets oxidative phosphorylation and selectively eradicates quiescent human leukemia
stem cells. Cell Stem Cell 2013;12:329–41.
27. Teh T-C, Nguyen N-Y, Moujalled DM, et al. Enhancing venetoclax
activity in acute myeloid leukemia by co-targeting MCL1. Leukemia
2018;32:303–12.
28. Niu X, Zhao J, Ma J, et al. Binding of released bim to Mcl-1 is amechanism of intrinsic resistance to ABT-199 which can be overcome bycombination with daunorubicin or cytarabine in AML cells. Clin Cancer
Res 2016;22:4440–51.
29. DiNardo CD, Pratz K, Pullarkat V, et al. Venetoclax combined withdecitabine or azacitidine in treatment-naive, elderly patients with acute
myeloid leukemia. Blood 2019;133:7–17.
30. DiNardo C, Jonas B, Pullarkat V, et al. Azacitidine and venetoclax in previously untreated acute myeloid leukemia. N Engl J Med 2020;383:617–29.
31. Wei AH, Montesinos P, Ivanov V, et al. Venetoclax plus LDAC for
newly diagnosed AML ineligible for intensive chemotherapy: a phase 3
randomized placebo-controlled trial. Blood 2020;135:2137–45.
32. Arber DA, Orazi A, Hasserjian R, et al. The 2016 revision to theWorld Health Organization classification of myeloid neoplasms and acuteleukemia. Blood 2016;127:2391–405.
33. Cheson BD, Bennett JM, Kopecky KJ, et al. Revised recommendations of the International Working Group for Diagnosis, standardizationof response criteria, treatment outcomes, and reporting standards fortherapeutic trials in acute myeloid leukemia. J Clin Oncol 2003;21:4642–9.
34. Döhner H, Estey E, Grimwade D, et al. Diagnosis and management of
AML in adults: 2017 ELN recommendations from an international expert
panel. Blood 2017;129:424–47.
35. Löwenberg B, Huls G. The long road: improving outcome in elderly"unfit" AML? Blood 2020;11;135:2114–5.

Journal of Epidemiology
Original Article

J Epidemiol 2022;32(1):27-33

Impact of the Clinical Trials Act on Noncommercial Clinical Researchin Japan: An Interrupted Time-series Analysis
Ikuyo Tsutsumi1,2, Yusuke Tsutsumi3,4, Chikashi Yoshida2, Takuya Komeno2, and Yuichi Imanaka1
1

Department of Healthcare Economics and Quality Management, Kyoto University Graduate School of Medicine, Kyoto, Japan
Department of Hematology, National Hospital Organization Mito Medical Center, Ibaraki, Japan3
Department of Emergency Medicine, National Hospital Organization Mito Medical Center, Ibaraki, Japan
4
Department of Healthcare Epidemiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
2

Received February 9, 2021; accepted July 5, 2021; released online October 23, 2021

ABSTRACT
Background: The number of new noncommercial clinical studies conducted in Japan declined within the ﬁrst year of theimplementation of the Clinical Trials Act (CTA) on April 1, 2018. This study aimed to examine the impact of the CTA’s
enforcement on the number of new noncommercial clinical studies registered in the Japanese Clinical Trial Registry.
Methods: An interrupted time-series design was used in the analysis, which was conducted from April 2015 to March 2019. We
collected data for studies registered in the Clinical Trial Registry, managed by the University Hospital Medical Information
Network.
Results: In total, 35,811 studies were registered; of these, 16,455 fulﬁlled the eligibility criteria. The diﬀerence in the trend ofmonthly number of new studies after CTA enforcement decreased signiﬁcantly by 15.0 (95% conﬁdence interval [CI], −18.7 to−11.3), and the level decreased by 40.8 (95% CI, −68.2 to −13.3) studies from the pre-enforcement to the post-enforcement
period. Multigroup analyses indicated that the act exerted a signiﬁcant eﬀect on the trend of new clinical studies, particularly
those with smaller sample sizes, interventional study designs, and nonproﬁt funding sponsors.
Conclusions: The number of Japanese noncommercial clinical studies declined signiﬁcantly following implementation of the
CTA. It is necessary to establish a system to promote clinical studies in Japan while ensuring transparency and safety.
Key words: Clinical Trials Act; interrupted time-series analysis; clinical research
Copyright © 2021 Ikuyo Tsutsumi et al. This is an open access article distributed under the terms of Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

INTRODUCTION
Recently, various regulations and laws have been developed forclinical research worldwide because research misconduct andinappropriate relationships between pharmaceutical companies
and researchers have become serious problems. Such “researchscandals” have recently become important ethical issues in Japan
as well. It was exposed that data falsiﬁcation and conﬂicts ofinterest occurred in research conducted in various ﬁelds, resulting
in papers being withdrawn in several clinical studies and serious
confusion in clinical practice.1 Research misconduct can impair
data accuracy and cause disadvantages to research participants
and those who would beneﬁt from the results.
Commercial clinical trials, conducted to obtain national
marketing approval for drugs and medical devices, are legally
regulated by the International Conference on Harmonization
Good Clinical Practice Guideline in Japan. Meanwhile, noncommercial clinical studies, including non-commercial interventional studies and non-interventional studies, are regulated by the
Ethical Guidelines for Medical and Health Research Involving
Human Subjects, which are not legally enforceable.

To improve the conduct of clinical studies by ensuring trust in
clinical studies and thereby promote public health and hygiene,
the Clinical Trials Act (CTA) was established by the Ministry of
Health, Labour and Welfare (MHLW) in Japan and enforcedsince April 1, 2018.2 It aimed to deﬁne procedures for the conductof clinical studies, appropriate provision of the management ofreviews by certiﬁed review boards (CRBs), and systems fordisclosure of information regarding funding or other beneﬁts forclinical studies.3 The CTA mainly regulates the “speciﬁed clinical
trials” which are deﬁned as the noncommercial interventional
studies receiving funds or beneﬁts from manufacturers and thatusing unapproved=oﬀ-label use drugs=medical devices. The
Act also covers noncommercial interventional studies otherthan “speciﬁed clinical trials”, which is not mandatory, but
recommended (duty of eﬀort). The commercial trials that are
conducted to obtain national marketing approval for drugs andmedical devices are exempted from the CTA. The CTA requires
1) contracts and disclosure of information regarding researchfunding; 2) review of the implementation plan and adverse eventsby a CRB authorized by the MHLW; 3) compliance with
implementation standards for monitoring, conﬂict of interest

Address for correspondence. Yuichi Imanaka, Department of Healthcare Economics and Quality Management, Kyoto University Graduate School of Medicine,
Kyoto, Japan (e-mail: imanaka-y@umin.net).

DOI https://doi.org/10.2188/jea.JE20210051
HOMEPAGE http://jeaweb.jp/english/journal/index.html

27

Impact of the Clinical Trials Act in Japan

management, and record preservation; and 4) disclosure of theimplementation plan to the MHLW.
While legal regulations for clinical studies have been
implemented in various countries to ensure transparency,
overregulation can sometimes limit clinical studies, particularly
those of a noncommercial nature.5,6 In the European Union (EU),the number of studies submitted for research grants or ethicalreview has declined by 30% to 50%, while the proportion of
noncommercial studies has decreased from 40% to 14% since
Directive 2001=20=EC was adopted in April 2001 and launchedin May 2004.7,8 Similarly, in Japan, there is the concern thatthe new law could reduce the number of studies conducted atstudy institutes lacking ﬁnancial support, but this has not been
examined previously. This study aimed to clarify the association
between the enforcement of the CTA and the number of studies
newly registered in the Japanese Clinical Trial Registry, using aninterrupted time-series analysis design.

METHODS
Data source
We collected data from the Clinical Trials Registry managed by
the University hospital Medical Information Network Clinical
Trials Registry (UMIN-CTR),9 which is part of the Japan Primary
Registries Network (JPRN). The JPRN consists of the following
three clinical study registration agencies: UMIN-CTR; Japan
Pharmaceutical Information Center Clinical Trial Information10;
and the Clinical Trials Registry operated by the Center for
Clinical Trials of the Japan Medical Association.11 The JPRN is aclinical trial registry that meets the criteria of the International
Committee of Medical Journal Editors and was authorized by
the World Health Organization (WHO) Primary Registry on
August 16, 2008.12 UMIN-CTR welcomes the registration of
all academic clinical studies, including both commercial andnoncommercial clinical trials, and both interventional andnoninterventional studies. However, in practice, pharmaceuticalcompany-led commercial clinical trials are registered in the Japan
Pharmaceutical Information Center Clinical Trial Information,and physician-led commercial and medical device clinical trials
are registered at the Center for Clinical Trials, Japan Medical
Association. Additionally, the newly established clinical study
database, the Japan Registry of Clinical Trials (jRCT), was added
to the JPRN and approved by the WHO Primary Registry on
December 5, 2018. The UMIN-CTR provides open .csv ﬁles,including daily snapshots of studies registered in the database.
We downloaded the ﬁle from the UMIN-CTR website on April 1,
2019.9Outcomes
The primary outcome was the change in the trend, deﬁned as thediﬀerence in changes (slope) in the monthly number of newclinical studies before and after the enforcement of the CTA. The
secondary outcome was the change in the monthly number of
new clinical studies, deﬁned as the diﬀerence in the monthly
number of new clinical studies from the end of the pre-CTA
period to the period immediately following the enforcement of the
CTA. Additionally, the study focused on diﬀerences in the eﬀects
of the CTA on the following factors: sample size (≤100 or >100),
study objectives (malignancy or nonmalignancy), study design(interventional or noninterventional), and type of funding sponsor(for-proﬁt or nonproﬁt).

28 j J Epidemiol 2022;32(1):27-33

Data selection
The inclusion criterion was an anticipated study start date
between April 1, 2015, and March 31, 2019. The exclusion
criteria were the exclusion of Japan from the study region andcommercial trials requiring Investigational New Drug applications to the MHLW, because these trials were applicable tothe International Conference on Harmonization Good Clinical
Practice Guideline rather than the CTA. We included noninterventional studies, such as observational studies and metaanalyses, in this study since one of the study objectives was todetermine whether the number of regulated interventional studies
had been more aﬀected by the Clinical Trials Act compared tounregulated designs.
Statistical analysis
Descriptive analyses were performed based on the baselinecharacteristics of studies before and after the enforcement of
the CTA. Continuous variables are presented as medians(interquartile ranges [IQRs]), and categorical variables arepresented as frequencies and percentages. A Wilcoxon ranksum test was performed to compare continuous variables, andPearson’s chi-squared test was used in between-group comparisons of categorical or binary variables. An interrupted time-series
analysis (ITSA) design13,14 was used to assess the association
between the enforcement of the CTA and changes in the trends
and the monthly number of new studies. The intervention ofinterest was the enforcement of the CTA. The ﬁrst month of theintervention period was set as April 2018, and the analysis period
lasted for 48 months, from April 2015 to March 2019. To create
the time-series dataset, the aggregate number of studies for eachmonth from April 2015 to March 2019 was tabulated according to
the anticipated study start date. We performed ITSA using two
ordinary least-squares regression-based approaches.15
The following regression equation was used in the singlegroup analysis15–18:
Yt ¼ 0 þ 1 Tt þ 2 Xt þ 3 Xt Tt þ t
The following regression equation was used in the multigroup
analysis14–17:
Yt ¼ 0 þ 1 Tt þ 2 Xt þ 3 Xt Tt þ 4 Z
þ 5 ZTt þ 6 ZXt þ 7 ZXt Tt þ t
Yt represents the monthly number of studies measured at time
point t, and Tt represents the time since April 2015. Xt is a dummy
variable representing the enforcement of the CTA (eg, pre-CTA
period was 0, otherwise 1). In the single-group analysis, β0
represents the number of studies in the ﬁrst month of the study(ie, April 2015). β1 represents the slope of the monthly numberof studies (trend) before the implementation of the CTA. β2
represents the change in the monthly number of studies fromthe end of the pre-CTA period (level change) to the period
immediately following the enforcement of the CTA. β3 indicatesthe slope change following the enforcement of the CTA. In themultigroup analysis, β0 to β3 represents the value for the control
group, and β4 to β7 represents the value for the comparison group.
β4 represents the diﬀerence in the number of studies during theﬁrst month of the study between the control group and comparison group (diﬀerence in level) prior to enforcement of the
CTA. β5 represents the diﬀerence in the slopes of the monthly
number of studies between the control group and comparisongroup (diﬀerence in trend) prior to enforcement of the CTA. β6

Tsutsumi I, et al.

Figure 1. Flow diagram of data selection. MHLW, Ministry of
Health, Labour and Welfare; UMIN-CTR, University hospital Medical Information Network.

represents the diﬀerence in levels immediately following
enforcement of the CTA between the control group and comparison group. β7 represents the diﬀerence in slopes (trends) inthe pre- and post-CTA periods between the control group andcomparison group. Calendar month was included as a dummy
variable, to account for seasonality. Newey-West standard errors
were used to deal with autocorrelation and possible heteroskedasticity.15 All statistical tests were two-sided, and thesigniﬁcance level was set at 5%. All analyses were performed
using Stata SE version 14.2 (Stata Corp, College Station, TX,
USA). The requirement for ethics committee approval was
waived because all the data are publicly available online andcomprise only aggregate values, without any personallyidentiﬁable information.

RESULTS
Figure 1 shows the ﬂow chart for data selection. We downloaded
all data for 35,811 studies in the UMIN-CTR on April 1, 2019.
The 502 studies that were not conducted in Japan and the 57 thatinvolved Investigational New Drug applications to the MHLW
were excluded according to the exclusion criteria. Additionally,
18,797 studies in which the anticipated study start date was not
between April 1, 2015, and March 31, 2019, were excluded
from the analysis. Therefore, 16,455 studies ultimately met the
selection criteria.
Table 1 shows the baseline characteristics of studies initiatedbetween April 2015 and March 2019. The proportion ofinterventional studies conducted after the enforcement of the
CTA was lower relative to that of those conducted before theenforcement of the CTA (from 70.8% to 66.6%, P < 0.001).
Regarding disease classiﬁcation, the proportion of studies
involving internal medicine and surgery conducted after theenforcement of the CTA was lower relative to that of those

conducted before (from 43.0% to 40.0% and from 12.6% to 9.3%,
respectively; P = 0.002). Moreover, the proportion of studies
involving malignancy decreased after the law was enforced(from 24.4% to 21.7%, P < 0.001). Conversely, the proportion ofstudies involving healthy people increased after the enforcement(from 22.6% to 29.0%, P < 0.001). Regarding types of fundingorganizations, the most common was self-funded, followed byfor-proﬁt organizations and Japanese governmental oﬃces, both
before and after the enforcement of the CTA.
Figure 2 and Table 2 shows the results of the single-group
ITSA for the monthly number of new studies. During the preenforcement period, the trend in the number of new studies
increased by 1.64 (95% CI, 0.71–2.57, P = 0.001) each month.
In contrast, during the post-CTA period, this trend was expected
to decline signiﬁcantly, by 13.3 (95% CI, −17.1 to −9.63,
P < 0.001) studies every month. The diﬀerence in trends before
and after the enforcement of the CTA was −15.0 (95% CI, −18.7
to −11.3, P < 0.001) studies. Furthermore, there was a signiﬁcantdecline in levels (−40.8; 95% CI, −68.2 to −13.3, P = 0.005)
after the enforcement.
Figure 3 and Table 3 represent the results of the multigroup
ITSA examining the eﬀects of the CTA on various factors such as
sample size, study design, and type of funding sponsor. As shownin Figure 3A, the pre-CTA trend for studies with sample sizes>100 increased by 0.53 (95% CI, 0.22–0.84, P = 0.001) monthly,
which did not show a signiﬁcant diﬀerence from those of studies
with sample sizes ≤100 (0.65; 95% CI, −0.29 to 1.58, P = 0.17).
In contrast, the post-CTA trend for studies with sample sizes>100 decreased by 2.99 (95% CI, −5.33 to −0.65, P = 0.013)
monthly, while studies with sample sizes ≤100 showed asigniﬁcant diﬀerence from those of studies with sample sizes>100 (−7.33; 95% CI, −10.6 to −4.11, P < 0.001). There was no
signiﬁcant diﬀerence in level changes between studies withsample sizes ≤100 and >100 during the period immediately
following the enforcement of the CTA (−15.8; 95% CI, −45.4 to
13.8, P = 0.29).
Moreover, Figure 3B shows the eﬀects of the CTA according
to study design. The pre-CTA trend in noninterventional studies
did not increase signiﬁcantly (0.14; 95% CI, −0.24 to 0.52,
P = 0.47), while interventional studies increased signiﬁcantly by
1.36 (95% CI, 0.54–2.19, P = 0.001) per month. The post-CTA
trend for noninterventional studies decreased by 2.78 (95% CI,−5.21 to −0.35, P = 0.026) per month, while interventional
studies showed a signiﬁcant diﬀerence from that for noninterventional studies (−7.78; 95% CI, −10.5 to −5.05, P < 0.001). There
was a signiﬁcant diﬀerence in level changes between interventional and noninterventional studies during the period immediately following the enforcement of the CTA (−40.6; 95% CI,−65.0 to −16.2, P = 0.001).
Furthermore, Figure 3C represents the results of the ITSA
according to the type of funding sponsor. The pre-CTA trend forstudies funded by for-proﬁt sponsors increased at a rate of 1.10
(95% CI, 0.68–1.52, P < 0.001) studies per month, which did not
diﬀer signiﬁcantly from studies funded by nonproﬁt sponsors(−0.55; 95% CI, −1.44 to 0.34, P = 0.22). In contrast, the postCTA trend for studies funded by for-proﬁt sponsors decreased at
a rate of 1.61 (95% CI, −3.99 to 0.77, P = 0.18) studies permonth, while studies funded by nonproﬁt sponsors showed asigniﬁcant diﬀerence from that for studies funded by for-proﬁtsponsors (−10.1; 95% CI, −12.0 to −8.27, P < 0.001). There wasno signiﬁcant diﬀerence in level changes between studies fundedJ Epidemiol 2022;32(1):27-33 j 29

Impact of the Clinical Trials Act in Japan

Table 1. Baseline characteristics of studies that began between April 2015 and March 2019

Study design, N (%)
Interventional
Observational
Other (eg, meta-analysis)
Not selected
Sample size, median (IQR)a
Category by sample size, N (%)a
≤100
>100
Basic objectives (primary outcome), N (%)
SafetyEﬃcacySafety and eﬃcacy
Bioequivalence
Bioavailability
Pharmacokinetics
Pharmacodynamics
Pharmacokinetics and pharmacodynamics
Other
Disease classiﬁcation by specialty, N (%)
Internal medicine
Surgery
Medicine, other
Dental medicine
Nursing
Healthy people
Not applicable
Disease classiﬁcation by malignancy, N (%)
Malignancy
Other
Category of funding organization, N (%)
For-proﬁt organization
Self-funding
Japanese governmental oﬃce
Nonproﬁt foundation
Local government
Government oﬃces of other countries
Outside Japan
Other

Total
N = 16,455

Before the Act
n = 13,095

After the Act
n = 3,360

11,506 (69.9%)
4,656 (28.3%)
248 (1.5%)45 (0.3%)
50 (25–110)

9,268 (70.8%)
3,574 (27.3%)
208 (1.6%)
45 (0.3%)
50 (25–110)

2,238 (66.6%)
1,082 (32.2%)
40 (1.2%)
0 (0.0%)50 (24–116.5)<0.001+++

12,261 (74.6%)
4,175 (25.4%)

9,769 (74.7%)
3,307 (25.3%)

2,492 (74.2%)
868 (25.8%)

0.52

1,238 (7.5%)
7,165 (43.5%)
4,413 (26.8%)
171 (1.0%)
190 (1.2%)
143 (0.9%)
48 (0.3%)
79 (0.5%)
3,008 (18.3%)

977 (7.5%)
5,622 (42.9%)
3,650 (27.9%)
136 (1.0%)
158 (1.2%)
117 (0.9%)
46 (0.4%)
63 (0.5%)
2,326 (17.8%)

261 (7.8%)
1,543 (45.9%)763 (22.7%)
35 (1.0%)32 (1.0%)26 (0.8%)
2 (0.1%)
16 (0.5%)682 (20.3%)<0.001+++

6,971 (42.4%)
1,961 (11.9%)
6,814 (41.4%)
491 (3.0%)416 (2.5%)
3,926 (23.9%)887 (5.4%)

5,628 (43.0%)
1,649 (12.6%)
5,498 (42.0%)384 (2.9%)317 (2.4%)
2,953 (22.6%)
661 (5.0%)

1,343 (40.0%)
312 (9.3%)
1,316 (39.2%)
107 (3.2%)
99 (2.9%)973 (29.0%)
226 (6.7%)

0.002++<0.001+++0.003++
0.44
0.083<0.001+++<0.001+++

3,927 (23.9%)
12,528 (76.1%)

3,198 (24.4%)
9,897 (75.6%)

729 (21.7%)
2,631 (78.3%)<0.001+++

3,186 (19.4%)
5,388 (32.7%)
1,995 (12.1%)
453 (2.8%)282 (1.7%)
161 (1.0%)
51 (0.3%)
4,939 (30.0%)

2,491 (19.0%)
4,407 (33.7%)
1,518 (11.6%)
357 (2.7%)
234 (1.8%)
121 (0.9%)
38 (0.3%)
3,929 (30.0%)695 (20.7%)981 (29.2%)
477 (14.2%)
96 (2.9%)48 (1.4%)
40 (1.2%)
13 (0.4%)
1,010 (30.1%)<0.001+++

P value

0.97

IQR, interquartile range.
Data for 19 of 13,095 studies were missing in the sample before the act’s enforcement.
+
P < 0.05; ++P < 0.01; +++P < 0.001.
aTable 2. Results of the single-group ITSA for monthly number of new studies
βTrend before the enforcement of the CTA :β1
Trend after the enforcement of the CTA :β1+β3Diﬀerence in trend before and after the enforcement of the CTA :β3
Diﬀerence in level before and after the enforcement of the CTA :β2

1.64
−13.3
−15.0
−40.8

95% CI
0.71
−17.1
−18.7
−68.2

–
–
–
–

P
2.57
−9.63
−11.3
−13.3

0.001++<0.001+++<0.001+++0.005++

CI, conﬁdence interval; CTA, Clinical Trials Act.
+P < 0.05; ++P < 0.01; +++P < 0.001.

by for- and nonproﬁt sponsors during the period immediately
following the enforcement of the CTA (4.71; 95% CI, −14.6 to
24.0, P = 0.63).

DISCUSSION
The results of the single-group ITSA showed that the total
number of new studies declined signiﬁcantly in both trends and

30 j J Epidemiol 2022;32(1):27-33

levels following the enforcement of the CTA in April 2018. The
current data indicated that the enforcement of the CTA exerted astrong negative eﬀect on the number of new clinical studies. The
analysis of various factors, using the multigroup ITSA method,
showed that the trend decreased signiﬁcantly for all types ofstudies after the new legal regulation, especially those withsmaller sample sizes, interventional study designs, and nonproﬁt
funding sponsors. The result suggests that enforcement of theTsutsumi I, et al.

A.

Figure 2. Results of the single-group ITSA for monthly
number of new studies. The points on the ﬁgure
represent the actual monthly number of studies.
The solid lines indicate the predicted monthly
number of studies adjusted by calendar month.
Dotted lines represent trends (slopes) in monthly
numbers of studies. Level change after theenforcement of the Clinical Trials Act (CTA) isdeﬁned as the diﬀerence from the end of thedotted line during the pre-act period to the starting
point of the dotted line during the post-act period.
CI, conﬁdence interval; ITSA, interrupted timeseries analysis.

CTA particularly aﬀected studies with limited human resources
and ﬁnancial support. Prior to implementation, there were no
legal restrictions in Japan on noncommercial clinical studies.
After introduction of the new law, CRBs authorized by the
MHLW reviewed study plans, adverse event reports, and theexact status of conﬂicts of interest of all physicians involvedin the studies.2–4 These improvements seek to increase thetransparency of the procedures involved in clinical studies andﬂow of expenses, which contribute to the prevention of research
misconduct. However, substantial research funding is needed to
disburse expensive commission fees for review by a CRB andmanagement costs, including personnel expenses for following
requirements mandated by the CTA, as compared to the periodprior to its introduction. Therefore, it can be diﬃcult for
researchers who do not have suﬃcient human resources andfunds to conduct new clinical research.
Before the CTA was enacted in 2018, new ethical guidelines
were established in 2015; the Act on the Protection of Personal
Information was revised in 2015 and enacted in 2017. Therefore, it
was necessary to conﬁrm whether the decline in new research inthe past few years was due to the CTA and not these factors. In thisstudy, we used ITSA to show that there was a signiﬁcant decreasein new clinical studies before and after the CTA was implemented.
We also conducted analyses using ITSA, before and after the
implementation of the new ethical guidelines and the revised Act
on the Protection of Personal Information, and conﬁrmed that
there was no signiﬁcant decrease in the number of new clinicalstudies after the implementation of each (data not shown).
Since 2001, each country in the EU has developed its own
directives for clinical studies to maintain the quality of studies
based on Directive 2001=20=EC. Thus, in most countries, theB.

C.

Figure 3. Results of the multigroup ITSA for monthly numberof new studies. (A) ITSA according to sample size.
(B) ITSA according to study design. (C) ITSA
according to type of funding sponsor. The points on
the graph represent the actual monthly number ofstudies. Solid lines indicate the predicted monthly
number of studies adjusted by calendar month.
Dotted lines represent trends (slopes) in monthly
number of studies. Level change after the enforcement of the Clinical Trials Act (CTA) is deﬁned as
the diﬀerence from the end of the dotted line during
pre-act period to the starting point of the dotted line
during post-act period. CI, conﬁdence interval;
ITSA, interrupted time-series analysis.

J Epidemiol 2022;32(1):27-33 j 31

Impact of the Clinical Trials Act in Japan

Table 3. Results of the multigroup ITSA for monthly number of new studies
Sample size ≤100 vs
Sample size >100
βPre-CTA trend for studies with sample sizes >100 :β1
Post-CTA trend for studies with sample sizes >100 :β1+β3Diﬀerence in trend for studies with sample sizes >100 before
and after the enforcement of the CTA :β3
Diﬀerence in level for studies with sample sizes >100 before
and after the enforcement of the CTA :β2
Diﬀerence in Pre-CTA trend between studies with sample sizes≤100 and those >100 :β5Diﬀerence in Post-CTA trend between studies with sample sizes≤100 and those >100 :β5+β 7Diﬀerence in trend before and after the enforcement of the CTA
between studies with sample sizes ≤100 and those >100 :β 7Diﬀerence in level before and after the enforcement of the CTA
between studies with sample sizes ≤100 and those >100 :β6

95% CI

Interventional studies vsNon-interventional studiesPβNon-proﬁt sponsor vsProﬁt sponsor

95% CIPβ

95% CIP

0.53
0.22 – 0.84
−2.99 −5.33 – −0.65

0.001++
0.013+

0.14 −0.24 – 0.52
−2.78 −5.21 – −0.35

0.47
0.026+

1.10
0.68 –−1.61 −3.99 –

1.52
0.77

<0.001+++
0.18−3.52 −5.80 – −1.24

0.003++−2.92 −5.40 – −0.44

0.021+−2.71 −4.89 – −0.53

0.016+−12.9 −33.8 –

8.01

0.22

−0.08 −16.9 –

16.7

0.99

−22.7 −42.4 – −3.12

0.024+−0.29 –

1.58

0.17

1.36

–

2.19

0.001++−0.55 −1.44

0.22

0.65

0.54

0.34

−7.33 −10.6 – −4.11 <0.001+++ −7.78 −10.5 – −5.05 <0.001+++ −10.1 −12.0−8.27 <0.001+++−7.97 −11.3 – −4.62 <0.001+++ −9.14 −12.0 – −6.31 <0.001+++ −9.56 −11.6−7.52 <0.001+++−15.8 −45.4 –

13.8

0.29

−40.6 −65.0 – −16.2

0.001++

4.71

−14.6

24.0

0.63

CI, conﬁdence interval; CTA, Clinical Trials Act.
+P < 0.05; ++P < 0.01; +++P < 0.001.

number of noncommercial studies has decreased, particularly
those funded by nonproﬁt sponsors,19–21 because regulatory
bodies impose highly demanding stipulations and expensive fees
for the submission of studies to ethics committees. There aresimilar concerns in Japan that the CTA may also overly regulateclinical studies regardless of the study subject and magnitude of
the risk to participants. Only a limited number of organizations
with an accessible labor force and adequate ﬁnancial resources
could conduct clinical studies with larger sample sizes, which
may limit the scopes of study domains. Indeed, a Japanese
questionnaire survey showed that the investigators’ desire forsupport systems when conducting clinical studies was signiﬁcantly higher after the implementation of the CTA than before.21
Furthermore, it may cause a decline in the number of new
researchers who conduct innovative clinical research. It isessential to develop a system in which physicians can obtainappropriate support to conduct research. We may be able tofollow the model developed in Italy, where the number of clinicalstudies increased after the introduction of legal regulation.21 Thisis apparently because Italy implemented policy changes that
include waiving of ethical review fees, prompt approval by ethics
review boards, ﬁnancial support for research expenses ormanagement, and alleviation of regulations on investigator-driven
study for noncommercial research.22–24
This study was subject to a few limitations. First, it did notinclude jRCT data analyzed in this study. Although the jRCT was
enforced in April 2018, the actual registration begun in April
2019. In addition, there were only six new studies registered in
the jRCT, but not in UMIN-CTR, within the current analysis
period between April 1, 2015, and March 31, 2019, as reported in
the WHO International Clinical Trials Registry Platform. Thissuggests that exclusion of the jRCT data has little impact on thecurrent results. Second, we could analyze the data for only 1 year
after the enforcement of the CTA. It may be essential to carry out
medium- and long-term analysis for examining the impact of the
CTA on clinical research in Japan. Nevertheless, the current
analysis contributes to identifying the key issues faced by
Japanese clinical research under the new legal regulation, which
can be used to address them quickly. Finally, it is diﬃcult toassess whether the introduction of the CTA can in fact reduce
clinical research with inadequate transparency and reliability,

32 j J Epidemiol 2022;32(1):27-33

which is the original purpose of the legislation, despite thedecrease in newly initiated research found in this study. Further
study from an alternative perspective may be needed to clarify
this issue.
In conclusion, the number of noncommercial clinical studies
decreased 1 year after the implementation of the CTA in Japan.
Establishing a new system to promote clinical research in Japan
while ensuring research transparency and safety is vital.

ACKNOWLEDGMENTS
We would like to thank Editage (www.editage.jp) for English
language editing.
Conﬂicts of interest: None declared.
Funding source: This research did not receive any speciﬁcgrant from funding agencies in the public, commercial, or not-forproﬁt sectors.
Author contributions: Ikuyo Tsutsumi — conceptualization,
data curation, formal analysis, investigation, methodology,
writing, original draft. Yusuke Tsutsumi — methodology,
writing, review & editing. Chikashi Yoshida — conceptualization, review & editing. Takuya Komeno — conceptualization,
review & editing. Yuichi Imanaka — conceptualization, methodology, review & editing, supervision.
REFERENCES
1. Sawano T, Ozaki A, Saito H, Shimada Y, Tanimoto T. Paymentsfrom pharmaceutical companies to authors involved in the Valsartan
scandal in Japan. JAMA Netw Open. 2019;2(5):e193817.
2. Ministry of Health, Labour and Welfare. About the Clinical Trials
Act. https:==www.mhlw.go.jp=stf=seisakunitsuite=bunya=0000163417.
html; 2019 Accessed 12.12.2019. [Japanese].
3. Ministry of Health, Labour and Welfare. Clinical Trials Act(Act No. 16 of April 14, 2017). 2017. https:==www.mhlw.go.jp=
ﬁle=06-Seisakujouhou-10800000-Iseikyoku=0000213334.pdf; 2019
Accessed 12.12.2019. [Japanese].
4. Tashiro S. How the clinical research act will change clinical researchin Japan. Gan To Kagaku Ryoho. 2018;45(7):1011–1016.
5. Morice AH. The death of academic clinical trials. Lancet. 2003;361(9368):1568.
6. The Lancet. Who’s afraid of the European Clinical Trials Directive?
Lancet. 2003;361(9376):2167.

Tsutsumi I, et al.

7. Bosch X. Europe’s restrictive rules strangling clinical research. Nat
Med. 2005;11(12):1260.
8. McMahon AD, Conway DI, Macdonald TM, McInnes GT. The
unintended consequences of clinical trials regulations. PLoS Med.
2009;3(11):e1000131.
9. University hospital Medical Information Network (UMIN). UMIN
Clinical Trials Registry (UMIN-CTR). https:==www.umin.ac.jp=ctr=
index.htm; 2013 Accessed 17.06.2020.
10. Japan Pharmaceutical Information Center. Japic Clinical Trials
Information. https:==www.clinicaltrials.jp=cti-user=common=Top.jsp;
2019 Accessed 12.08.2019. [Japanese].
11. The Japan Medical Association Center for Clinical Trials. JMACCT
Clinical Trials Registry. https:==dbcentre3.jmacct.med.or.jp=
JMACTR=Default_Eng.aspx; 2019 Accessed 12.08.2019.
12. World Health Organization. International Clinical Trials Registry
Platform (ICTRP). https:==www.who.int=ictrp=network=primary=en=;
2019 Accessed 11.09.2019.
13. Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented
regression analysis of interrupted time series studies in medication
use research. J Clin Pharm Ther. 2002;27(4):299–309.
14. Bernal JL, Cummins S, Gasparrini A. Interrupted time series
regression for the evaluation of public health interventions: atutorial. Int J Epidemiol. 2017;46(1):348–355.
15. Linden A. Conducting interrupted time-series analysis for singleand multiple-group comparisons. Stata J. 2015;15(2):480–500.

16. Linden A, Adams JL. Applying a propensity score-based weighting
model to interrupted time series data: improving causal inference inprogramme evaluation. J Eval Clin Pract. 2011;17(6):1231–1238.
17. Simonton DK. Cross-sectional time-series experiments: somesuggested statistical analyses. Psychol Bull. 1977;84(3):489–502.
18. Simonton DK. Erratum to Simonton. Psychol Bull. 1977;84(6):1097.
19. Frewer LJ, Coles D, Champion K, et al. Has the European clinical
trials directive been a success? BMJ. 2010;340:c1862.
20. Hartmann M, Hartmann-Vareilles F. The clinical trials directive:
how is it aﬀecting Europe’s noncommercial research? PLoS Clin
Trials. 2006;1(2):e13.
21. Chuma M, Takechi K, Yagi K, et al. Academic investigators’interest in promoting speciﬁed clinical trials: questionnaire survey
before and after implementation of the Clinical Trial Act. J Med
Invest. 2021;68(1.2):71–75.
22. Hartmann M. Impact assessment of the European clinical trials
directive: a longitudinal, prospective, observational study analyzing
patterns and trends in clinical drug trial applications submitted since2001 to regulatory agencies in six EU countries. Trials. 2012;13:53.
23. Filibeck U, Addis A, Tomino C, Martini N. European clinical trials
directive: the Italian position. Lancet. 2004;363(9421):1651–1652.
24. Gazzetta Uﬃciale. GU Serie Generale n.43 del 22-02-2005. https:==www.gazzettauﬃciale.it=eli=gu=2005=02=22=43=sg=pdf 2019; 2019
Accessed 12.11.2019. [Italian].

J Epidemiol 2022;32(1):27-33 j 33

Japanese Journal of Clinical Oncology, 2022, 52(1)29–38
https://doi.org/10.1093/jjco/hyab170
Advance Access Publication Date: 5 November 2021
Original Article

Original Article

Venetoclax plus azacitidine in Japanese patientswith untreated acute myeloid leukemia
ineligible for intensive chemotherapy
Kazuhito Yamamoto 1 ,*, Atsushi Shinagawa2 , Courtney D. DiNardo3 ,Keith W. Pratz4 , Kenichi Ishizawa5 , Toshihiro Miyamoto6 ,Norio Komatsu7 , Yasuhiro Nakashima8 , Chikashi Yoshida9 ,Noriko Fukuhara10 , Kensuke Usuki11 , Takahiro Yamauchi12 ,Noboru Asada13 , Norio Asou14 , Ilseung Choi15 , Yasushi Miyazaki16 ,Hideyuki Honda17 , Sumiko Okubo18 , Misaki Kurokawa17 , Ying Zhou19 ,Jiuhong Zha19 , Jalaja Potluri19 , and Itaru Matsumura20
1

Department of Hematology and Cell Therapy, Aichi Cancer Center, Nagoya, Aichi, Japan, 2 Department of Internal
Medicine, Hitachi General Hospital, Hitachi, Ibaraki, Japan, 3 Department of Leukemia, Division of Cancer Medicine,
University of Texas MD Anderson Cancer Center, Houston, TX, USA, 4 Leukemia Program, Division of Medicine,
University of Pennsylvania, Philadelphia, PA, USA, 5 Department of Third Internal Medicine, Yamagata University
Hospital, Yamagata, Japan, 6 Department of Medicine and Biosystemic Science, Kyushu University Graduate Schoolof Medical Sciences, Fukuoka, Japan, 7 Department of Hematology, Juntendo University School of Medicine, Tokyo,
Japan, 8 Department of Hematology, Graduate School of Medicine, Osaka City University, Osaka, Japan,
9Department of Hematology, National Hospital Organization, Mito Medical Center, Ibaraki, Japan, 10 Department of
Hematology, Tohoku University Graduate School of Medicine, Sendai, Japan, 11 Department of Hematology, NTT
Medical Center Tokyo, Tokyo, Japan, 12 Department of Hematology and Oncology, University of Fukui Hospital, Fukui,Japan, 13 Department of Hematology and Oncology, Okayama University Hospital, Okayama, Japan, 14 Department of
Hematology, Saitama Medical University International Medical Center, Hidaka, Saitama, Japan, 15 Department of
Hematology, National Hospital Organization, Kyushu Cancer Center, Fukuoka, Japan, 16 Department of Hematology,
Atomic Bomb Disease Institute, Nagasaki University, Nagasaki, Japan, 17 AbbVie GK, Tokyo, Japan, 18 AbbVie GK,
Osaka, Japan, 19 AbbVie, Inc., North Chicago, IL, USA and 20 Department of Hematology and Rheumatology, Kindai
University Hospital, Osaka, Japan*For reprints and all correspondence: Kazuhito Yamamoto, Aichi Cancer Center, Department of Hematology and Cell
Therapy, 1-1 Kanokoden, Chikusa Ward, Nagoya, Aichi 464-8681, Japan. E-mail: kyamamoto@aichi-cc.jp
Received 11 August 2021; Editorial Decision 30 September 2021; Accepted 15 October 2121

Abstract
Background: The phase 3 VIALE-A trial (NCT02993523) reported that venetoclax-azacitidine signiﬁcantly prolonged overall survival compared with placebo-azacitidine in patients with newly
diagnosed acute myeloid leukemia ineligible for intensive chemotherapy. Herein, efﬁcacy andsafety of venetoclax-azacitidine are analyzed in the Japanese subgroup of VIALE-A patients.
Methods: Eligible Japanese patients were randomized 2:1 to venetoclax-azacitidine (N = 24) orplacebo-azacitidine (N = 13). Primary endpoints for Japan were overall survival and complete
response (CR) + CR with incomplete hematologic recovery (CRi). Venetoclax (target dose 400 mg)
was given orally once daily. Azacitidine (75 mg/m2 ) was administered subcutaneously or intravenously on Days 1–7 of each 28-day cycle.
© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium,provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

29

30

Ven-Aza in the Japanese subgroup of VIALE-A

Results: Median follow-up was 16.3 months (range, 1.0–20.3). Median overall survival was notreached with venetoclax-azacitidine (hazard ratio 0.409 and 95% conﬁdence interval: 0.151, 1.109);
overall survival estimate was higher with venetoclax-azacitidine than placebo-azacitidine at 12 (67and 46%) and 18 months (57 and 31%), respectively. CR and CRi rates were 67% with venetoclaxazacitidine and 15% with placebo-azacitidine. Most common any-grade adverse events werefebrile neutropenia (79 and 39%), thrombocytopenia (54 and 77%), constipation (54 and 54%)and decreased appetite (54 and 38%) in the venetoclax-azacitidine and placebo-azacitidine arms,
respectively. Only 1 patient in the venetoclax-azacitidine arm, and no patients in the placeboazacitidine arm, had grade 4 febrile neutropenia that led to treatment discontinuation.
Conclusions: This Japanese subgroup analysis of VIALE-A demonstrates comparable safety andefﬁcacy outcomes compared with the global study and supports venetoclax-azacitidine as ﬁrstline standard-of-care for Japanese treatment-naive patients with acute myeloid leukemia who are
ineligible for intensive chemotherapy.
Key words: acute myeloid leukemia, venetoclax, azacitidine, VIALE-A, Japan

Introduction
Acute myeloid leukemia (AML) is the most common type of adultleukemia, with the highest incidence worldwide found in the USA,
Western Europe and Australia (1). In Japan, AML is the mostcommon leukemia, accounting for ∼70% of myeloid leukemias(2,3). AML primarily affects older adults, with a third of patients inJapan being diagnosed at ≥75 years of age (4). The 5-year survival
rate was 39.2% among Japanese patients with leukemias diagnosed
between 2006 and 2008 (5). Current treatment for younger adults inJapan consists of intensive induction chemotherapy with idarubicin
or daunorubicin plus cytarabine (2,6). In addition, patients achieving
remission are advised to continue with consolidation therapy
consisting of high-dose cytarabine or non–cross-resistant agents,
and allogeneic stem cell transplant for eligible patients. However,many patients with newly diagnosed AML are not eligible forintensive chemotherapy because of advanced age or the presenceof comorbidities (6–8). Treatment options for these patients arelimited and include low-intensity therapy with the hypomethylating
agents (HMAs) azacitidine or decitabine, and low-dose cytarabine[LDAC; (9,10)].
In Japan, for patients ≥65 years of age who are ineligible forstandard therapy (idarubicin or daunorubicin plus cytarabine) on
the basis of their performance status, comorbidities or cytogenetic
abnormalities, treatment with LDAC is recommended (6). However,outcomes with less intensive regimens such as decitabine, azacitidine
or LDAC remain poor, with expected rates of complete response (CR)or CR with incomplete blood count recovery (CRi) lower than 30%,
and median overall survival (OS) of <1 year (11–13). More effective
and well-tolerated treatment options are needed for patients with
AML ineligible for intensive chemotherapy.
Venetoclax is a selective inhibitor of B-cell leukemia/lymphoma-2(BCL2) that has been studied in several hematologic malignancies
as monotherapy or in combination with other agents (14–21).
Two large phase 1b/2 studies have assessed venetoclax-based
therapy in combination with low-intensity regimens in older
patients with previously untreated AML (21,22). When combinedwith azacitidine or decitabine, venetoclax therapy resulted in
a CR + CRi rate of 67% and a median OS of 17.5 months(22). A subsequent confirmatory phase 3 placebo-controlled trial(VIALE-A; NCT02993523) compared the efficacy and safety ofvenetoclax plus azacitidine (venetoclax-azacitidine) with placebo
plus azacitidine (placebo-azacitidine) in treatment-naive patientswith AML ineligible for standard induction therapy (23). The
venetoclax-azacitidine combination regimen significantly increased
OS [14.7 and 9.6 months, respectively; hazard ratio (HR), 0.66; 95%
confidence interval (CI): 0.52, 0.85 and P < 0.001] and the CR + CRi
rate (66 and 28%; P < 0.001) compared with the placebo-azacitidine
regimen (23).
The biologic rationale for combining venetoclax with azacitidine
in AML is based on their complementary mechanisms of action.
The BCL2 family members, including BCL2, BCL-XL and MCL1,
mediate cancer cell survival by sequestering proapoptotic proteins.
BCL2 is upregulated in AML, promotes chemoresistance, enhances
the survival of leukemic progenitor and blast cells and has been associated with poor outcomes (24,25). In preclinical studies, venetoclax
had additive or synergistic effects when combined with azacitidine
in primary cells from patients with AML (26,27), where azacitidine
was shown to downregulate MCL1 protein levels (27).
Venetoclax has been approved in the USA (28), and in >25
other countries for use in combination with azacitidine, decitabineor LDAC in patients with newly diagnosed AML who are ≥75 yearsof age, or those with comorbidities that preclude the use of intensive
induction chemotherapy. Japan has the most aged population worldwide (29), and elderly patients with AML have limited treatment
options. In March 2021, venetoclax in combination with azacitidine
or LDAC was approved in Japan. The Japanese subgroup analyses
aimed to provide evidence that safety and efficacy outcomes in theJapanese subgroup were comparable to the global population. Here,
we present the results of the subgroup analysis of Japanese patientswith AML ineligible for intensive chemotherapy who participated inthe VIALE-A trial comparing venetoclax-azacitidine with placeboazacitidine.

Materials and methods
Study design
VIALE-A (NCT02993523) is a phase 3, randomized, doubleblind, placebo-controlled and multicenter study that assessed
the efficacy and safety of venetoclax-azacitidine compared withplacebo-azacitidine in patients with previously untreated AML
who were ineligible for intensive chemotherapy (23). The dual
primary endpoints for Japan were OS and CR + CRi rates. Key
secondary endpoints included CR + CR with partial hematologic
recovery (CRh) rates, response rates at the start of Cycle 2 andJpn J Clin Oncol, 2022, Vol. 52, No. 1
transfusion-independence rates. The study was conducted inaccordance with the International Council for Harmonization
requirements, Good Clinical Practice guidelines and the Declarationof Helsinki. The study protocol was reviewed and approved by anindependent ethics committee/institutional review board at eachstudy site. All patients provided written informed consent.

31

Safety evaluations were performed throughout the study in all
patients who received at least 1 dose of either venetoclax or placebo(in combination with azacitidine). Adverse events (AEs) were gradedaccording to the National Cancer Institute Common Terminology
Criteria for Adverse Events Version 4.03. AEs were defined as thoseoccurring between the first dose of study drug until 30 days after
discontinuation of treatment.

Patients
Full eligibility criteria have been reported previously (23). In brief,
the study enrolled treatment-naive adult (≥18 years of age) patientswith a confirmed diagnosis of AML [per the World Health Organization classification (30)], who were ineligible for standard induction
chemotherapy because of age (≥75 years) or comorbidities. The
existence of at least one of the following conditions precluded the useof intensive induction chemotherapy: Eastern Cooperative Oncology
Group (ECOG) performance status 2 or 3, history of congestive
heart failure requiring treatment, an ejection fraction ≤50%, chronic
stable angina, diffusion capacity of the lung for carbon monoxide≤65%, forced expiratory volume in 1 second (s) of ≤65%, creatinine
clearance 0.5 ml/s to <0.75 ml/s, moderate hepatic impairment withtotal bilirubin >1.5 to ≤3.0 times the upper limit of normal orother comorbidities considered incompatible with standard therapy.
Patients needed to have a projected life expectancy of ≥12 weeks
to be included in the trial, and those who had received previous
treatment for myelodysplastic syndromes or AML were excluded.

Randomization and treatment
Eligible patients were randomized 2:1 via interactive response technology to either venetoclax-azacitidine or placebo-azacitidine. Randomization was stratified by patient age (<75 and ≥75 years),region (USA, European Union, China, Japan and rest of world) andcytogenetic risk (intermediate and poor).
Venetoclax was given orally once daily. Patients were hospitalized
for monitoring and tumor lysis syndrome (TLS) prophylaxis for the
first 4 days of treatment in Cycle 1, until 24 h after the target doseof venetoclax was reached. This included uric acid-reducing agents,
intravenous hydration and laboratory assessments. To mitigate therisk of TLS, venetoclax dosing started at 100 mg on Day 1 of Cycle
1, increasing to 200 mg on Day 2 and reaching the target dose of
400 mg on Day 3. The 400mg daily dosing was maintained until Day
28 of Cycle 1 and on Days 1–28 in all subsequent cycles. Patientsin the control arm received oral placebo according to the sameschedule. Azacitidine was administered to patients in both study arms
at a dose of 75 mg/m2 subcutaneously or intravenously for the first
7 days of each 28-day cycle. Treatment was continued until diseaseprogression, withdrawal of consent or other protocol-defined criteria
for discontinuation were met.

Assessments
OS was defined as the time from study randomization to death due
to any cause. Bone marrow assessments were performed at screening,at the end of Cycle 1 and every 3 cycles thereafter until 2 successive
samples indicated CR or composite CR (CR or CR + CRi). Details
on the disease evaluation criteria have been reported previously inthe primary publication of this study (23). In brief, responses wereassessed according to the modified International Working Group criteria for AML (31) and disease progression was defined per European
LeukemiaNet criteria (32). Transfusion independence was defined as
at least 56 consecutive days with no transfusion of either red blood
cells (RBCs) or platelets between the first and last day of treatment.

Statistical analyses
Baseline characteristics and treatment outcomes are descriptive forthis subgroup analysis.
The data cutoff for the Japanese subgroup analysis presented here
was 4 January 2020, the same cutoff used for the primary analysis of
VIALE-A. The efficacy analyses were performed on the full analysis
set, which included the intent-to-treat population who underwent
randomization. Safety analyses were performed in all patients who
received at least 1 dose of study drug (venetoclax or placebo, in
combination with azacitidine).
Power and sample size determinations for the global phase3 study have been reported previously (23). OS distribution was
estimated using the Kaplan–Meier methodology, and comparisons
between treatment arms used the log-rank test stratified by age
and cytogenetic risk. The HR between the treatment groups
was estimated with the Cox proportional hazards model withthe same stratification factors. Response rates and transfusion
independence outcomes were compared between treatment arms
using the Cochran–Mantel–Haenszel test, with age and cytogenetic
risk as stratification factors. The 95% CIs were determined with the
Clopper–Pearson exact method.

Results
Patients
Between 6 February 2017 and 31 May 2019, of the 48 patients
screened in Japan from 15 sites, 37 (77%) were randomized to thestudy arms, including 24 patients assigned to venetoclax-azacitidine
and 13 to placebo-azacitidine. A total of 19 patients (10 venetoclaxazacitidine and 9 placebo-azacitidine) discontinued study, all becauseof death. Death was related to disease progression in 84.2% (16/19)
of cases.
Patient demographics and baseline characteristics of the Japanese
subgroup are summarized in Table 1. The proportion of patients≥75 years of age was higher in the venetoclax-azacitidine arm
than in the placebo-azacitidine arm (79 and 69%, respectively).
The venetoclax-azacitidine arm also had a higher proportion ofpatients with ECOG performance status 0 or 1 (75 and 62%) andintermediate-risk cytogenetics (75 and 69%). On the other hand,more patients in the placebo-azacitidine arm had ≥50% blasts inbone marrow (54 and 42%, respectively) and baseline grade 3 or
4 neutropenia (92 and 79%). The main reason for ineligibility toreceive intensive chemotherapy was advanced age (≥75 years) in both
treatment arms (79% with venetoclax-azacitidine and 69% withplacebo-azacitidine). In general, no significant differences in baselinecharacteristics were observed between the venetoclax-azacitidine andplacebo-azacitidine arms.

Efﬁcacy
At a median follow-up of 16.3 months (range, 1.0–20.3) median OS
was not reached (NR) in the venetoclax-azacitidine arm (95% CI:
10.6, NR) and was 8.6 months (95% CI: 2.7, NR) in the placeboazacitidine arm, with a stratified Cox proportional HR of 0.41 (95%32

Ven-Aza in the Japanese subgroup of VIALE-A

Table 1. Patient demographics and baseline characteristics
Characteristic

Venetoclax-azacitidine (n = 24)

Placebo-azacitidine (n = 13)

77.5 (68–85)
19 (79.2)
14 (58.3)

77.0 (67–86)
9 (69.2)
9 (69.2)

20 (83.3)
4 (16.7)

10 (76.9)
3 (23.1)

3/4 (75.0)
1/4 (25.0)

3/3 (100)0/3

18 (75.0)
6 (25.0)

8 (61.5)5 (38.5)

7 (29.2)
7 (29.2)
10 (41.7)
9 (37.5)

4 (30.8)
2 (15.4)
7 (53.8)
5 (38.5)

18 (75.0)
6 (25.0)

9 (69.2)
4 (30.8)

6/21 (28.6)
2/23 (8.7)
0/14
2/14 (14.3)

4/13 (30.8)
2/12 (16.7)
6/9 (66.7)
0/9

19 (79.2)
3 (12.5)
16 (66.7)
8 (33.3)
7 (29.2)

12 (92.3)
3 (23.1)
9 (69.2)
5 (38.5)
4 (30.8)2 (8.3)0
8 (33.3)

2 (15.4)
1 (7.7)7 (53.8)

Age
Median, years (range)≥ 75 years, n (%)
Male, n (%)
AML type, n (%)
De novo
Secondary
Secondary AML type, n/N (%)
Prior MDS or CMML
Treatment-related AML
ECOG performance status, n (%)
0 or 12 or 3
Blast count, n (%)< 30%≥ 30% to <50%
≥ 50%
AML with myelodysplasia-related changes, n (%)
Cytogenetic risk, n (%)
Intermediate
Poor
Somatic mutations, n/N (%)
IDH1 or IDH2
FLT3
NPM1
TP53
Baseline Grade 3 or 4 cytopenias, n (%)
Neutropenia
Grade 3
Grade 4
AnemiaThrombocytopenia
Baseline transfusion dependence,a n (%)
RBCs
Platelets≥2 reasons for ineligibility to receive intensive therapy,b n (%)
aBaseline transfusion dependence defined as RBC or platelet transfusion within 8 weeks prior to the first dose of study drug or randomization.
Patients could report more than 1 reason.
Abbreviations: AML, acute myeloid leukemia; CMML, chronic myelomonocytic leukemia; ECOG, Eastern Cooperative Oncology Group; FLT3, fms-liketyrosine kinase 3; IDH, isocitrate dehydrogenase; MDS, myelodysplastic syndrome; NPM1, nucleophosmin; RBC, red blood cell; TP53, tumor protein 53.

b

CI: 0.15, 1.11; Fig. 1). The estimated OS was 67% (95% CI: 44.3,
81.7) with venetoclax-azacitidine and 46% (95% CI: 19.2, 69.6)
with placebo-azacitidine at 12 months, and 57% (95% CI: 33.9,74.1) and 31% (95% CI: 6.5, 60.2), respectively, at 18 months.
Response rates and event-free survival (EFS) are shown in Fig. 2.
Rates of CR and CR + CRi were higher in the venetoclax-azacitidine
arm than in the placebo-azacitidine arm, with 67% of patientsassigned to venetoclax-azacitidine and 15% of patients assigned toplacebo-azacitidine achieving CR + CRi (Fig. 2a). The median time
to first response (CR or CRi) was 1.2 months (range, 0.8–2.9) for
venetoclax-azacitidine and 3.1 months (range, 3.0–3.2) for placeboazacitidine. Half of the patients in the venetoclax-azacitidine arm
achieved CR + CRi by the start of Cycle 2 compared with nopatients in the placebo-azacitidine arm. The addition of venetoclax
to azacitidine also resulted in a significant improvement in EFS[16.3 months (95% CI: 7.9, NR)] compared with 3.4 months (95%
CI: 1.5, 14.5) with placebo-azacitidine (HR, 0.229; 95% CI: 0.088,
0.596 and P = 0.001; Fig. 2b).

Sixteen patients (67%; 95% CI: 45, 84) receiving venetoclaxazacitidine and 2 patients (15%; 95% CI: 2, 45) receiving placeboazacitidine achieved post-baseline RBC and platelet transfusion
independence while on treatment (Fig. 3). The median durationof RBC and platelet transfusion independence was 122 days
(range, 57–557) for venetoclax-azacitidine, and 328 days (range,
312–344) for placebo-azacitidine. RBC transfusion independence
was achieved in 75% of patients in the venetoclax-azacitidinearm (95% CI: 53.3, 90.2) and 23% in the placebo-azacitidinearm (95% CI: 5.0, 53.8). Platelet transfusion independence wasachieved in 79% of patients in the venetoclax-azacitidine arm(95% CI: 57.8, 92.9) and 31% in the placebo-azacitidine arm(95% CI: 9.1, 61.4). The median number of post-baseline RBC
transfusions was 11 (range, 2–47) and 8 (range, 2–45) in thevenetoclax-azacitidine and placebo-azacitidine arms, respectively,
with a median number of post-baseline platelet transfusions of
11 (range, 1–69) and 15 (range, 1–66) for each arm, respectively.
One of 2 patients who were RBC transfusion dependent at baselineJpn J Clin Oncol, 2022, Vol. 52, No. 1

33

Figure 1. OS by treatment arm. a Stratiﬁed by age (18 to <75, ≥75 years) and cytogenetic risk (intermediate, poor). Aza, azacitidine; CI, conﬁdence interval; HR,hazard ratio; NR, not reached; OS, overall survival; Pbo, placebo; Ven, venetoclax.

became transfusion independent during treatment with venetoclaxazacitidine, whereas neither of the 2 transfusion-dependent patientsassigned to placebo-azacitidine achieved transfusion independence.
Only 1 patient was dependent on platelet transfusions at baseline
and remained transfusion dependent during treatment with placeboazacitidine.

SafetyMedian duration of exposure to venetoclax/placebo was longer inthe venetoclax-azacitidine arm than in the placebo-azacitidine arm[12.3 months (range, 0.9–20.0) and 1.8 months (range, 0.5–13.9)],
respectively. Patients received a median of 8 treatment cycles (range,
1–19) with venetoclax-azacitidine and 2 treatment cycles (range, 1–15) with placebo-azacitidine. The most frequent reason for discontinuation of study drug within the placebo-azacitidine arm was diseaseprogression.
All patients reported at least 1 AE of any grade. Table 2 summarizes AEs reported in ≥20% of patients in either treatment arm. The
most common Grade ≥3 AEs (venetoclax-azacitidine and placeboazacitidine, respectively) were mainly hematologic, including febrileneutropenia (79 and 39%), thrombocytopenia (50 and 77%), neutropenia (38 and 23%), leukopenia (33 and 31%) and anemia (21and 15%; Table 2). Grade ≥3 pneumonia was observed in 21% of
patients in the venetoclax-azacitidine arm and 15% of patients inthe placebo-azacitidine arm. The most common nonhematologic AEs(any grade; venetoclax-azacitidine and placebo-azacitidine, respectively) included decreased appetite (54 and 31%), constipation (54and 54%), diarrhea (46 and 46%), vomiting (42 and 23%) andnausea (38 and 31%; Table 2).

Serious AEs were reported in 67% of patients treated withvenetoclax-azacitidine and 31% of patients treated withplacebo-azacitidine; those occurring in ≥5% of patients in thevenetoclax-azacitidine arm are summarized in Table 3. Serious
AEs (venetoclax-azacitidine and placebo-azacitidine, respectively)
included febrile neutropenia (42 and 0%), neutropenia (17 and 0%),pneumonia (13 and 8%) and leukopenia (8 and 0%). All patientswere hospitalized to receive TLS prophylaxis and for monitoring,
per protocol; no cases of TLS were reported in either treatment arm.
To prevent the occurrence of clinically relevant infections, 79% of
patients in the venetoclax-azacitidine arm and 77% of patients inthe placebo-azacitidine arm received anti-infection prophylaxis.
AEs led to treatment discontinuation in 4 patients: 2 (8%)
patients in the venetoclax-azacitidine arm (1 patient with Grade
4 febrile neutropenia and 1 with celiac artery occlusion leading
to death), and 2 (15%) patients in the placebo-azacitidine arm[1 patient with Grade 3 fatigue and Grade 4 neutropenia and 1
patient with Grade 4 acute kidney injury (AKI)]. AEs led to venetoclax/placebo dose interruptions in 16 (67%) patients receiving
venetoclax-azacitidine most commonly due to febrile neutropenia(n = 9), neutropenia (n = 6) and infection (n = 6), and in 4 (31%)
patients treated with placebo-azacitidine due to febrile neutropenia
and thrombocytopenia (n = 1), neutropenic enterocolitis and heart
failure (n = 1), AKI (n = 1) and pneumonia (n = 1).
Death within 30 days of starting study treatment occurred in
1 (4%) patient treated with venetoclax-azacitidine (due to celiac
artery occlusion) and in no patients treated with placebo-azacitidine.
There were no deaths due to febrile neutropenia in either arm of thestudy.

34

Ven-Aza in the Japanese subgroup of VIALE-A

Figure 2. Rates of CR and CR+CRi (a), and investigator-assessed event-free survival (b). a Stratiﬁed by age (18 to <75, ≥75 years) and cytogenetic risk (intermediate,poor). Aza, azacitidine; CI conﬁdence interval; CR, complete response; CRi, CR with incomplete blood count recovery; HR, hazard ratio; Pbo, placebo; Ven,
venetoclax.

Discussion
This analysis in the Japanese subgroup of the VIALE-A trial inpatients with untreated AML ineligible for intensive chemotherapy
demonstrated similar efficacy and safety outcomes as were seen in
the overall population, despite the shorter median follow-up period
for the Japanese subgroup analysis [16.3 vs 20.5 months (23)].
Importantly, no AEs specific to Japanese patients were observed.

Specifically, the median OS in the Japanese subgroup (NR withvenetoclax-azacitidine and 8.6 months with placebo-azacitidine) was
consistent with that in the overall population, where the median OS
was 14.7 and 9.6 months, respectively. The composite CR + CRi
rates for the Japanese subgroup (67 and 15%, respectively), werealso consistent with the response outcomes in the overall population(66 and 28%, respectively; P < 0.001), which included patients fromthe USA, Europe, China, Japan and the rest of the world.

Jpn J Clin Oncol, 2022, Vol. 52, No. 1

35

Table 2. Adverse events reported in ≥20% of patients in either arm
Venetoclax-azacitidine (n = 24)

Placebo-azacitidine (n = 13)

AE, n (%)

Any grade

Grade ≥3

Any grade

Grade ≥3

Any
Hematologic events
Thrombocytopenia
Neutropenia
Febrile neutropenia
Grade 3
Grade 4
AnemiaLeukopenia
Disseminated intravascular coagulation
Nonhematologic events
Pneumonia
NauseaConstipation
Diarrhea
Vomiting
Stomatitis
Decreased weight
Increased alanine aminotransferase
Hypokalemia
Pyrexia
Fatigue
Decreased appetite
Insomnia
Malaise24 (100)
23 (95.8)
13 (54.2)
9 (37.5)
19 (79.2)

13 (100)
11 (84.6)
10 (76.9)
3 (23.1)
5 (38.5)5 (20.8)
8 (33.3)
1 (4.2)24 (100)
23 (95.8)
12 (50.0)
9 (37.5)
19 (79.2)
18 (75.0)
1 (4.2)5 (20.8)
8 (33.3)
1 (4.2)3 (23.1)
4 (30.8)
3 (23.1)

12 (92.3)
11 (84.6)
10 (76.9)
3 (23.1)
5 (38.5)5 (38.5)
02 (15.4)
4 (30.8)06 (25.0)
9 (37.5)
13 (54.2)
11 (45.8)
10 (41.7)
10 (41.7)
6 (25.0)
5 (20.8)
6 (25.0)
7 (29.2)
1 (4.2)13 (54.2)
6 (25.0)
8 (33.3)

5 (20.8)
1 (4.2)0
1 (4.2)00
1 (4.2)2 (8.3)4 (16.7)
00
1 (4.2)00

2 (15.4)
4 (30.8)
7 (53.8)
6 (46.2)
3 (23.1)
2 (15.4)
1 (7.7)1 (7.7)4 (30.8)
4 (30.8)
1 (7.7)4 (30.8)
1 (7.7)1 (7.7)2 (15.4)
00
1 (7.7)00
0
1 (7.7)
3 (23.1)01 (7.7)1 (7.7)00Abbreviation: AE, adverse event.

Table 3. Serious adverse events (any grade) occurring in ≥5% of patients
Serious AE, n (%)

Venetoclax-azacitidine (n = 24)

Placebo-azacitidine (n = 13)

AnyFebrile neutropenia
Neutropenia
Leukopenia
Pneumonia

16 (66.7)
10 (41.7)
4 (16.7)
2 (8.3)3 (12.5)

4 (30.8)0001 (7.7)Abbreviation:AE, adverse event.

Also, the higher rate of response for patients in the venetoclaxazacitidine arm in the Japanese subgroup analysis, compared withthose in the placebo-azacitidine arm, correlated with a greater incidence of post-baseline transfusion independence (RBC + platelet: 67
and 15%, respectively). Responses were also reached earlier withvenetoclax-azacitidine compared with placebo-azacitidine (mediantime to first response 1.2 and 3.1 months, respectively), with half ofthe patients in the venetoclax-azacitidine arm achieving a responseby the start of Cycle 2.
The difference in median EFS between treatment groups
was more marked in the Japanese subgroup (16.3 months withvenetoclax-azacitidine and 3.4 months with placebo-azacitidine)than in the overall population (9.8 and 7.0 months, respectively).
Therefore, these results support that the addition of venetoclax to
HMA therapy improves the currently dismal outcomes achievedin elderly Japanese patients with AML treated with HMAs alone(11–13).

The median duration of exposure to venetoclax treatment was
longer in the Japanese subgroup (12.3 months) compared withthe overall population (7.6 months). Japanese patients randomized
to placebo-azacitidine however, received a median of 2 treatment
cycles (median duration of exposure 1.8 months), compared with a
median of 4.5 treatment cycles for the overall population. Due tothe double-blind design of this trial, early treatment discontinuation
was at investigator discretion on the basis of lack of response, and aJapanese-specific signature cannot be confirmed.
However, the safety profile of venetoclax-azacitidine in theJapanese subgroup was consistent with safety data from theoverall study population and with previous reports on the use ofvenetoclax in AML (22), with the most common AEs associatedwith venetoclax-azacitidine being hematologic or gastrointestinal.
The incidence of grade 3 or 4 febrile neutropenia was higher inJapanese patients across treatment groups (79% with venetoclaxazacitidine and 39% with placebo-azacitidine) compared with the

36

Ven-Aza in the Japanese subgroup of VIALE-Aof patients in both studies experienced serious AEs (67%). Of note,
the CR rates in Japanese patients in the VIALE-A study are higher
than the reported rates with LDAC, the current standard of care in
Japan for elderly patients with poor-risk cytogenetics (6,8).
The small number of patients analyzed (n = 37) in this investigation of a geographic population is a limitation of this study. Nevertheless, the data indicate that venetoclax-azacitidine was effectivewith an expected (similar to that of the overall population) andmanageable safety profile in the Japanese subgroup of VIALE-A.
The combination of venetoclax and azacitidine could be considered a first-line standard of care for Japanese patients with previously
untreated AML who are not candidates for intensive induction
therapy.

Acknowledgments
Figure 3. Proportion of patients with post-baseline transfusion independence.
Transfusion independence was deﬁned as a period of 56 days or more withouttransfusion. RBC, red blood cell.

overall population (42% with venetoclax-azacitidine and 19%
with placebo-azacitidine). Notably, rates of infection and treatment
discontinuation or death due to febrile neutropenia were similar in
the Japanese and overall populations (23). These results suggest thatfebrile neutropenia was manageable with appropriate monitoringand intervention, including with venetoclax dose interruptions
and the use of granulocyte-colony stimulating factor (G-CSF) per
investigator discretion (28).
Some key differences in baseline characteristics were noted
between the overall population and the Japanese subgroup, including
a preponderance of patients ≥75 years of age (Japanese subgroupand overall population, 76 and 61%, respectively), with ECOG
performance status of 0 or 1 (70 and 55%), with intermediaterisk cytogenetics (73 and 63%), and with baseline Grade 3 or 4
neutropenia (84 and 69%).
Venetoclax pharmacokinetics in patients with AML have been
described using a population pharmacokinetics model (33). Although
Asian patients had 67% higher relative bioavailability of venetoclax compared with non-Asian patients, the range of venetoclax exposures (area under the plasma concentration-time curve at
steady state) was similar in the two groups. Covariate analysis toevaluate the relationship between venetoclax exposure and safetyin patients with previously untreated AML identified that Asianpatients were more likely to have treatment-emergent Grade ≥3
neutropenia regardless of treatment with placebo or venetoclax in
combination with an HMA. However, the predicted net effect ofvenetoclax on treatment-emergent Grade ≥3 neutropenia was similarboth for Asian and non-Asian patients (33).
Importantly, the safety and efficacy results with venetoclaxazacitidine described here were also generally similar to thosereported in a phase 1/2 trial in Japan that enrolled 6 elderly(≥75 years) patients with untreated relapsed/refractory AML[NCT02265731; (34)]. With a median exposure time to venetoclax
of 12.3 months in this study and 10.3 months in the phase 1/2
study, the majority of patients in both studies experienced at least
1 Grade ≥3 treatment-emergent AE (phase 3, 100%; phase 1/2,83%), of which the most common were hematologic (phase 3:
febrile neutropenia [79%], thrombocytopenia [50%], neutropenia[38%]; phase 1/2: lymphopenia [67%], febrile neutropenia [67%],
thrombocytopenia [50%] and leukopenia [50%]). Equal proportions

AbbVie and the authors thank all the trial investigators and the patients who
participated in this clinical trial. Medical writing support was provided by
Rebecca Crepeau, PhD from Aptitude Health, Atlanta, GA, USA, and funded
by AbbVie.

Funding
Venetoclax is being developed in a collaboration between AbbVie and Genentech. AbbVie and Genentech funded this study(NCT02993523) and participated in the study design, research,
analysis, data collection, interpretation of data, reviewing andapproval of the publication. All authors had access to relevant
data and participated in the drafting, review and approval of thispublication. No honoraria or payments were made for authorship.

Conﬂict of interest statements
Kazuhito Yamamoto has received honoraria from Chugai, Eisai,
Mundipharma and Takeda, and has received research funding
from AbbVie, Celgene, Chugai, Eisai, Incyte/IQVIA, SymBio andZenyaku. Atsushi Shinagawa and Ilseung Choi declare that they
have no conflicts of interest. Courtney D. DiNardo has received
institutional research support from AbbVie, Agios, Bayer, Calithera,
Celgene, Bristol-Myers Squibb, Cleave and Daiichi Sankyo, andhas served as a consultant and on advisory boards for AbbVie,
Agios, Celgene, Bristol-Myers Squibb, Daiichi Sankyo, ImmuneOnc, Novartis, Takeda and Notable Labs. Keith W. Pratz has
served in a consulting/advisory role for AbbVie, Astellas Pharma,
Boston BioMedical, Bristol-Myers Squibb, Celgene and Jazz
Pharmaceuticals, and has received research funding from AbbVie,
Agios, Daiichi Sankyo and Millennium. Kenichi Ishizawa has received
research funding from Novartis, AbbVie, Bayer and SymBio, and has
served on a speakers’ bureau for Celgene, Chugai, Eisai, Novartis,
Ono Pharmaceutical and Takeda. Toshihiro Miyamoto has received
honoraria from AbbVie, Amgen KK, Astellas Pharma, Bristol-Myers
Squibb, Celgene, Merck Sharp & Dohme, Otsuka Pharmaceuticaland Takeda. Norio Komatsu has received research funding from
Bristol-Myers Squibb KK, Chugai, Fujifilm Wako Pure Chemical
Industries, Fuso Pharmaceutical, Kyowa Kirin, Novartis Pharma
KK, Otsuka Pharmaceutical, Pfizer Japan, PharmaEssentia JapanKK, Shire Japan KK, Sumitomo Dainippon Pharma and Takeda,
and has received advisory fees from AbbVie GK, Celgene KK, Japan
Tobacco Inc., Novartis KK, Otsuka Pharmaceutical, PharmaEssentia
Japan KK and Shire Japan KK, and has served on speakers’ bureausJpn J Clin Oncol, 2022, Vol. 52, No. 1
for Novartis KK, Shire Japan KK and Takeda, and as a memberof safety assessment committee in the M13–834 clinical trial.
Yasuhiro Nakashima has received research funding from AbbVie
GK, Amgen KK, Astellas Pharma, Celgene and Novartis, and hasreceived honoraria from Amgen KK. Chikashi Yoshida has received
honoraria from AbbVie GK, Astellas Pharma, Bristol-Myers Squibb,
Daiichi Sankyo, Janssen Pharmaceutical KK, Meiji Seika Pharma,
Nippon Shinyaku, Novartis KK and Otsuka Pharmaceutical. Noriko
Fukuhara has received research funding from AbbVie, Bayer, Eisai,
Gilead Sciences, Ono Pharmaceutical and Solasia Pharma, and hasreceived honoraria from Chugai and Kyowa Kirin. Kensuke Usuki
has received research funding from AbbVie, Apellis Pharmaceuticals,
Astellas-Amgen-Biopharma, Astellas Pharma, Bristol-Myers Squibb,
Celgene, Chugai, Daiichi Sankyo, Gilead, Janssen Pharmaceuticals,
Kyowa Kirin, Mundipharma, Nippon Boehringer Ingelheim, Nippon
Shinyaku, Novartis Pharma KK, Ono Pharmaceutical, Otsuka
Pharmaceutical, Pfizer, Sumitomo Dainippon Pharma, SymBio and
Takeda, and has served on a speakers’ bureau for Astellas Pharma,
Bristol-Myers Squibb, Celgene, Daiichi Sankyo, Eisai, Kyowa Kirin,
Merck Sharp & Dohme, Nippon Shinyaku, Novartis Pharma KK,
Ono Pharmaceutical, Otsuka Pharmaceutical, PharmaEssentia,
SymBio, Takeda and Yakult. Takahiro Yamauchi has received
research funding from AbbVie, Astellas Pharma, Boehringer
Ingelheim, Chugai, Janssen Pharmaceutical, Nippon Shinyaku, Ono
Pharmaceutical, Otsuka Pharmaceutical, Pfizer, Solasia Pharma,
Sumitomo Dainippon Pharma and Teijin Pharma. Noboru Asada
has received honoraria from AbbVie, Astellas Pharma, Celgene,
Chugai, Eisai, Kyowa Kirin, Novartis, Otsuka and Sanofi. Norio
Asou has received research funding from AbbVie, Astellas Pharma,
Chugai, Eisai and Sumitomo Dainippon Pharma, and has served
on a speakers’ bureau for Asahi Kasei, Fuji Pharma, Nippon
Shinyaku and Sumitomo Dainippon Pharma, and has received
honoraria from Nippon Shinyaku and Novartis. Yasushi Miyazaki
has received honoraria from Astellas Pharma, Celgene, Chugai,
Kyowa Kirin, Nippon Shinyaku, Novartis, Otsuka Pharmaceuticaland Sumitomo Dainippon Pharma, and has received research funding
from Chugai and Sumitomo Dainippon Pharma. Hideyuki Honda,
Sumiko Okubo, Misaki Kurokawa, Ying Zhou, Jiuhong Zha andJalaja Potluri are employees of AbbVie and may own stock. Itaru
Matsumura has received research funding from Chugai, Eisai,
Kyowa Kirin, Shionogi and Sumitomo Dainippon Pharma, and has
served on a speakers’ bureau for Astellas Pharma, Bristol-Myers
Squibb, Daiichi Sankyo Janssen Pharmaceutical, Novartis, Otsuka
Pharmaceutical and Pfizer.

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and trial-level data (analysis data sets), as well as other information(e.g. protocols and Clinical Study Reports), as long as the trials
are not part of an ongoing or planned regulatory submission. This
includes requests for clinical trial data for unlicensed products andindications.
These clinical trial data can be requested by any qualified
researchers who engage in rigorous, independent scientific research
and will be provided following review and approval of a research
proposal and Statistical Analysis Plan (SAP) and execution of a Data
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time and the data will be accessible for 12 months, with possible
extensions considered. For more information on the process, or to37

submit a request, visit the following link: https://www.abbvie.com/ou
r-science/clinical-trials/clinical-trials-data-and-information-sharing/data-and-information-sharing-with-qualified-researchers.html.

References
1.

Deschler B, Lübbert M. Acute myeloid leukemia: epidemiology and etiology. Cancer 2006;107:2099–107.
2. Miyawaki S. Clinical studies of acute myeloid leukemia in the Japan Adult
Leukemia Study Group. Int J Hematol 2012;96:171–7.
3. Niino M, Matsuda T. Type distribution of myeloid leukemia from cancer
incidence in five continents Vol. X. Jpn J Clin Oncol 2016;46:394.
4. Cancer Information Service. Cancer Statistics in Japan; Table Download.
2. Incidence (National Estimates). https://ganjoho.jp/en/professional/stati
stics/table_download.html (4 August 2021, date last accessed).
5. Cancer Information Service. Cancer Statistics in Japan; Table Download.
5. Survival. https://ganjoho.jp/en/professional/statistics/table_download.
html (4 August 2021, date last accessed).
6. Kiyoi H, Yamaguchi H, Maeda Y, Yamauchi T. JSH practical guidelines
for hematological malignancies, 2018: I. Leukemia–1. Acute myeloid
leukemia (AML). Int J Hematol 2020;111:595–613.
7. Kantarjian H, Ravandi F, O’Brien S, et al. Intensive chemotherapy does
not benefit most older patients (age 70 years or older) with acute myeloid
leukemia. Blood 2010;116:4422–9.
8. Tasaki T, Yamauchi T, Matsuda Y, et al. The response to induction therapy
is crucial for the treatment outcomes of elderly patients with acute myeloid
leukemia: single-institution experience. Anticancer Res 2014;34:5631–6.
9. Heuser M, Ofran Y, Boissel N, et al. Acute myeloid leukaemia in adultpatients: ESMO clinical practice guidelines for diagnosis, treatment andfollow-up. Ann Oncol 2020;31:697–712.
10. National Comprehensive Cancer Network. NCCN Clinical Practice
Guidelines in Oncology (NCCN Guidelines® ). Acute Myeloid Leukemia.
Version 3.2021. 2020. https://www.nccn.org/professionals/physician_gls/
pdf/aml.pdf (4 August 2021, date last accessed).
11. He PF, Zhou JD, Yao DM, et al. Efficacy and safety of decitabine intreatment of elderly patients with acute myeloid leukemia: a systematic
review and meta-analysis. Oncotarget 2017;8:41498–507.
12. Dombret H, Seymour JF, Butrym A, et al. International phase 3 studyof azacitidine vs conventional care regimens in older patients with newly
diagnosed AML with >30% blasts. Blood 2015;126:291–9.
13. Kantarjian HM, Thomas XG, Dmoszynska A, et al. Multicenter, randomized, open-label, phase III trial of decitabine versus patient choice, withphysician advice, of either supportive care or low-dose cytarabine for thetreatment of older patients with newly diagnosed acute myeloid leukemia.
J Clin Oncol 2012;30:2670–7.
14. Konopleva M, Pollyea DA, Potluri J, et al. Efficacy and biologicalcorrelates of response in a phase II study of venetoclax monotherapy
in patients with acute myelogenous leukemia. Cancer Discov 2016;6:
1106–17.
15. Fischer K, Al-Sawaf O, Bahlo J, et al. Venetoclax and obinutuzumabin patients with CLL and coexisting conditions. N Engl J Med
2019;380:2225–36.
16. Kumar S, Kaufman JL, Gasparetto C, et al. Efficacy of venetoclax astargeted therapy for relapsed/refractory t(11;14) multiple myeloma. Blood
2017;130:2401–9.
17. Moreau P, Chanan-Khan A, Roberts AW, et al. Promising efficacy andacceptable safety of venetoclax plus bortezomib and dexamethasone in
relapsed/refractory MM. Blood 2017;130:2392–400.
18. Roberts AW, Davids MS, Pagel JM, et al. Targeting BCL2 withvenetoclax in relapsed chronic lymphocytic leukemia. N Engl J Med
2016;374:311–22.
19. Seymour JF, Kipps TJ, Eichhorst B, et al. Venetoclax-rituximab in
relapsed or refractory chronic lymphocytic leukemia. N Engl J Med
2018;378:1107–20.
20. Stilgenbauer S, Eichhorst B, Schetelig J, et al. Venetoclax in relapsed
or refractory chronic lymphocytic leukaemia with 17p deletion: a38

Ven-Aza in the Japanese subgroup of VIALE-A

multicentre, open-label, phase 2 study. Lancet Oncol 2016;17:
768–78.
21. Wei AH, Strickland SA Jr, Hou JZ, et al. Venetoclax combined with
low-dose cytarabine for previously untreated patients with acute myeloid
leukemia: results from a phase Ib/II study. J Clin Oncol 2019;37:1277–84.
22. DiNardo CD, Pratz K, Pullarkat V, et al. Venetoclax combined withdecitabine or azacitidine in treatment-naive, elderly patients with acute
myeloid leukemia. Blood 2019;133:7–17.
23. DiNardo CD, Jonas BA, Pullarkat V, et al. Azacitidine and venetoclax in previously untreated acute myeloid leukemia. N Engl J Med
2020;383:617–29.
24. Del Poeta G, Venditti A, Del Principe MI, et al. Amount of spontaneous
apoptosis detected by Bax/Bcl-2 ratio predicts outcome in acute myeloid
leukemia (AML). Blood 2003;101:2125–31.
25. Lagadinou ED, Sach A, Callahan K, et al. BCL-2 inhibition targets oxidative phosphorylation and selectively eradicates quiescent human leukemia
stem cells. Cell Stem Cell 2013;12:329–41.
26. Bogenberger JM, Delman D, Hansen N, et al. Ex vivo activity of BCL-2
family inhibitors ABT-199 and ABT-737 combined with 5-azacytidine in
myeloid malignancies. Leuk Lymphoma 2015;56:226–9.
27. Tsao T, Shi Y, Kornblau S, et al. Concomitant inhibition of DNA
methyltransferase and BCL-2 protein function synergistically induce mitochondrial apoptosis in acute myelogenous leukemia cells. Ann Hematol
2012;91:1861–70.

28. VENCLEXTA® (venetoclax tablets). [prescribing information]. North
Chicago, IL: AbbVie Inc.; 2020.
29. United Nations. Department of Economic and Social Affairs. World
Population Ageing. 2017. https://www.un.org/en/development/desa/popu
lation/publications/pdf/ageing/WPA2017_Highlights.pdf (4 August 2021,
date last accessed).
30. Arber DA, Orazi A, Hasserjian R, et al. The 2016 revision to theWorld Health Organization classification of myeloid neoplasms and acuteleukemia. Blood 2016;127:2391–405.
31. Cheson BD, Bennett JM, Kopecky KJ, et al. Revised recommendations of the international working group for diagnosis, standardizationof response criteria, treatment outcomes, and reporting standards fortherapeutic trials in acute myeloid Leukemia. J Clin Oncol 2003;21:4642–9.
32. Döhner H, Estey E, Grimwade D, et al. Diagnosis and management of
AML in adults: 2017 ELN recommendations from an international expert
panel. Blood 2017;129:424–47.
33. Eckert D, Brackman D, Menon R, et al. Venetoclax exposure-efficacy
and exposure-safety relationships in subjects with treatment-naïve acute
myeloid leukemia who are ineligible for intensive chemotherapy. Blood
2020;136:52. https://doi.org/10.1182/blood-2020-136425.
34. Taniguchi S, Yamauchi T, Choi I, et al. Venetoclax in combination withazacitidine in Japanese patients with acute myeloid leukaemia: phase 1
trial findings. Jpn J Clin Oncol 2021;51:857–64.

Articles

Low-dose dasatinib in older patients with chronic myeloid
leukaemia in chronic phase (DAVLEC): a single-arm,
multicentre, phase 2 trial
Kazunori Murai, Hiroshi Ureshino, Takashi Kumagai, Hideo Tanaka, Kaichi Nishiwaki, Satoshi Wakita, Koiti Inokuchi, Toshihiro Fukushima,
Chikashi Yoshida, Nobuhiko Uoshima, Toru Kiguchi, Masayuki Mita, Jun Aoki, Satoshi Kimura, Kaori Karimata, Kensuke Usuki, Joji Shimono,
Yoshiaki Chinen, Junya Kuroda, Yasufumi Matsuda, Kensuke Nakao, Takaaki Ono, Katsumichi Fujimaki, Hirohiko Shibayama, Chisaki Mizumoto,Tomoharu Takeoka, Katsuhiro Io, Takeshi Kondo, Masatomo Miura, Yousuke Minami, Takayuki Ikezoe, Jun Imagawa, Ayako Takamori,Atsushi Kawaguchi, Junichi Sakamoto, Shinya Kimura

Summary
Lancet Haematol 2021;
8: e902–11
See Comment page e867
Department of Hematology,
Iwate Prefectural Central
Hospital, Morioka, Japan(K Murai MD); Division of
Hematology, Respiratory
Medicine and Oncology,
Department of Internal
Medicine (H Ureshino MD,
Prof Sh Kimura MD), and
Education and Research Centerfor Community Medicine(Prof A Kawaguchi PhD), Facultyof Medicine, Saga University,
Saga, Japan; Department of
Hematology, Ome Municipal
General Hospital, Ome, Japan(T Kumagai MD); Department
of Hematology, Hiroshima City
Asa Hospital, Hiroshima, Japan(H Tanaka MD); Division ofClinical Oncology and
Hematology, Department ofInternal Medicine, The Jikei
University Kashiwa Hospital,
Kashiwa, Chiba, Japan(K Nishiwaki MD); Department
of Hematology, Nippon
Medical School, Tokyo, Japan(S Wakita MD,
Prof K Inokuchi MD);
Department of Hematology
and Immunology, Kanazawa
Medical University, Kanazawa,
Japan (T Fukushima MD);
Department of Hematology,
National Hospital Organization
Mito Medical Center,
Ibarakimachi, Japan(C Yoshida MD); Department of
Hematology, Japanese Red
Cross Kyoto Daini Hospital,
Kyoto Japan (N Uoshima MD);
Department of Hematology,
Chugoku Central Hospital,
Fukuyama, Japan(T Kiguchi MD); Department of
Hematology, Shirakawa Kosei
General Hospital, Shirakawa,Japan (M Mita MD);
Department of Hematology,
Yokohama City University

e902

Background BCR-ABL1 tyrosine kinase inhibitors (TKIs) are commonly initiated in older patients with chronic
myeloid leukaemia in the chronic phase at standard doses. However, because of their safety profile in this population,
appropriate therapy has not been established. We aimed to investigate whether a lower than standard dose of dasatinib
was an appropriate therapy for older patients with chronic myeloid leukaemia in the chronic phase.
Methods DAsatinib, Very Low-dose, for Elderly CML-CP patients (DAVLEC) was a multicentre, single-arm, phase 2
trial done in 25 Japanese hospitals. We enrolled patients older than 70 years with newly diagnosed chronic myeloid
leukaemia in the chronic phase, ECOG performance status 0–2, and no previous treatment for CML other thanhydroxyurea within 4 weeks. Second-generation TKI dasatinib was given orally at a starting dose of 20% of thestandard dose (20 mg/day). If the treatment was assessed as optimal response at 3 months, 6 months, and 9 months
and adverse events were grade 2 or better (according to the NCI Common Toxicity Criteria v 4.0), the same dose wascontinued. If response was suboptimal and adverse events were grade 2 or better, the dose was increased by 20 mg/day.
Once a dose reduction had been made because of a grade 3 or worse adverse event, there were no further dose
increases. Treatment was discontinued if assessed as failure (disease progression to the accelerated phase or acutephase). The primary endpoint was the achievement of major molecular response at 12 months, assessed using a
per-protocol analysis. This trial is registered at with the UMIN clinical trial registry, UMIN000024548, and hascompleted its planned observation period.
Findings Between Nov 1, 2016, and Oct 30, 2019, 52 patients received first-line dasatinib therapy at 20 mg/day. The
median age at diagnosis was 77·5 years (73·5–83·0). 35 (67%) patients were male and 17 (33%) were female. 31 (60%)
of 52 patients reached major molecular response at 12 months (one-sided 95% CI 48–71), with a median follow-up of
366 days (IQR 353–372). Grade 3–4 adverse events were reported in 12 (23%) patients. Neutropenia was the mostfrequent grade 3–4 adverse event, occurring in three (6%) patients. No treatment-related deaths were observed.
Interpretation Low-dose dasatinib at 20mg/day is worthy of consideration as a starting dose for older patients with
newly diagnosed chronic myeloid leukaemia in the chronic phase. However, this dose needs to be further studied ina larger cohort and with a more ethnically diverse population.
Funding Bristol-Myers Squibb.
Copyright © 2021 Published by Elsevier Ltd. All rights reserved.

Introduction
BCR-ABL1 tyrosine kinase inhibitors (TKIs) improves
the life expectancy of patients with chronic myeloid
leukaemia close to that of the general population.1
Furthermore, some patients can discontinue TKIs
without relapse.2 The DASISION trial showed thatpatients newly diagnosed with CML in the chronic
phase treated with the second-generation TKI dasatinib(100 mg/day) had significantly higher and faster rates
of major molecular response than had patients treatedwith imatinib.3 We reported that almost half ofpatients with chronic myeloid leukaemia in the chronic

phase who had a sustained deep molecular response
for at least 1 year could discontinue dasatinib without
relapse 4,5More than 20% of patients with CML are older than
70 years.6 Most older patients have comorbid healthconditions and polypharmacy problems. Furthermore,comorbidity at diagnosis is strongly negatively correlatedwith overall survival in patients with CML patients.7
TKIs are commonly initiated in older patients atstandard dose. However, because of the safety concernsof TKIs in this population, the optimal therapy for older
patients has not been established. The first-generation
www.thelancet.com/haematology Vol 8 December 2021

Articles

Research in context
Evidence before this study
We searched PubMed and ClinicalTrials.gov, using the following
search terms: “chronic myeloid leukaemia”, “ABL tyrosine kinase
inhibitor (TKI)”, “dasatinib”, “elderly”, “older”, “reduced” or“dose”, for articles published between Jan 1, 2001, andDec 1, 2020, in any language. We identified ten articles
suggesting the appropriate dosage of dasatinib in older
patients with chronic myeloid leukaemia in the chronic phase.
These studies suggested that dasatinib dosing should be
reduced in older patients, but findings were mainly based on
retrospective studies and dose reduction was at most
50 mg/day, half the standard dose in prospective
studies. No prospective studies examined the effect of
20 mg/day, 20% of the standard dose, in older patients withchronic myeloid leukaemia in the chronic phase.
Added value of this study
The first-generation TKI imatinib, associated with fewer
cardiovascular complications and cheaper than secondgeneration TKIs, is often used for older patients with chronic
myeloid leukaemia in the chronic phase. Because patientstreated with second-generation TKIs including dasatinib hadsignificantly improved overall survival and higher treatmentfree remission rate than did patients treated with imatinib,

TKI imatinib, associated with fewer cardiovascularcomplications than second-generation TKIs, is often
used for older patients.8 Even imatinib, however, raisedsafety issues, such as renal dysfunction and anaemia.9
Patients treated with second-generation TKIs had
significantly improved overall survival than did patientstreated with imatinib.10 Generally, a slightly lower dose of
anticancer agent is selected than the dose that causes
dose limiting toxicity. However, many molecularly
targeted agents do not reach dose limiting toxicity and,in such cases, whether the dose selected in a clinical trial
is truly optimal might be unclear. A reduced dose of
50 mg/day dasatinib was active in patients with chronic
myeloid leukaemia in the chronic phase in a singlecentre, phase 2 trial, suggesting that lower doses might
be also effective.11 A single-centre, retrospective study
suggested that 20% of the standard dose of dasatinib(≤20 mg/day) was active and well tolerated for older
patients with chronic myeloid leukaemia in the chronic
phase.12 However, evidence from single-centre results is
insufficient. Additionally, in these studies the method oftreatment was chosen on a case-by-case basis by eachattending physician, therefore, insufficient evidence
exists to generalise this finding to the treatment of older
patients with CML.
Thus, we prospectively investigated the activity andsafety of low-dose dasatinib in older patients with newly
diagnosed chronic myeloid leukaemia in the chronic
phase.
www.thelancet.com/haematology Vol 8 December 2021

dasatinib used at an appropriate dose was expected to provide agreater benefit than that of imatinib in older patients withchronic myeloid leukaemia in the chronic phase when used at an
appropriate dose. In this study, dasatinib was given orally at
20% of the standard dose (20 mg/day). This starting dose wasthen optimised on the basis of activity and occurrence ofadverse events. At 12 months, 31 (60%) patients had major
molecular response, 14 (27%) a molecular response (MR) 4.0,
and seven (13%) a MR4.5. Grade 3–4 adverse events wereobserved in 12 (23%) of 52 patients and pleural effusion wasobserved only in four patients. These findings showed that thetreatment strategy of increasing or decreasing the dasatinib
dose from a starting dose of 20 mg/day while monitoring
therapeutic effects and adverse events was active and welltolerated in older patients with chronic myeloid leukaemia inthe chronic phase.
Implications of all the available evidence
Our findings together with previous studies on reducing thedose of dasatinib suggest that the standard dose (100 mg/day)
is not absolute and dasatinib should be used with anappropriate reduced dose in older patients with chronic
myeloid leukaemia in the chronic phase.

Methods

Study design and patients
We did a multicentre, single-arm, phase 2 study at
25 hospitals in Japan (appendix p 73). Patients were
eligible if they were aged 70 years or older and had newly
diagnosed chronic myeloid leukaemia in the chronic
phase and did not received previous CML therapy otherthan hydroxyurea for a maximum of 1 month. Other
eligibility criteria were an Eastern Cooperative Oncology
Group (ECOG) performance status of 0–2, and adequateorgan function. We adopted the European LeukemiaNet(ELN) 2013 criteria to define accelerated phase and blast
phase CML.13 To make the protocol as useful as possible
for real-world clinical practice, the type of transcript was
not examined because the frequency of non-b2a2 or
b3a2 is low (approximately 1–2%) in Japanese patients14
and the examination of transcript type is not covered bynational insurance in Japan. Exclusion criteria were: (1)a history of treatment with TKI or interferon; (2) ahistory of the following significant or uncontrollable
cardiovascular disorders or their complications includ­ing obvious pleural effusion, myocardial infarction
within 6 months, angina pectoris within 3 months,
congestive cardiac failure within 3 months, congenital
QT prolong­ation syndrome, or QTc interval prolongation
by 500 ms or more on 12-lead electrocardiogram; (3)second cancer; (4) hypersensitivity to dasatinib; and (5)being deemed ineligible for the study by the attending
physician.

Medical Center, Yokohama,Japan (J Aoki MD); Department
of Hematology, Fukushima
Medical University, Fukushima,
Japan (Sa Kimura MD,
Prof T Ikezoe MD); Department
of Hematology, Heart Life
Hospital, Nakagusuku, Japan(K Karimata MD); Department
of Hematology, NTT Medical
Center Tokyo, Japan(K Usuki MD); Department of
Hematology, Asahikawa City
Hospital, Asahikawa, Japan(J Shimono MD); Division of
Hematology and Oncology,
Department of Medicine,
Kyoto Prefectural University of
Medicine, Kyoto, Japan(Y Chinen MD, Prof J Kuroda MD);
Department of Hematology
and Oncology, University of
Fukui, Fukui, Japan (Y Matsuda
MD); Department of
Hematology/Oncology, Shiga
General Hospital, Moriyama,
Japan (K Nakao MD); Divison of
Hematology, Hamamatsu
University School of Medicine,
Hamamatsu, Japan (T Ono MD);
Department of Hematology,
Fujisawa City Hospital,
Fujisawa, Japan(K Fujimaki MD); Osaka
University Graduate School of
Medicine, Department of
Hematology and Oncology,
Suita, Japan (H Shibayama MD);
Division of Haematology and
Immunology, Otsu Red Cross
Hospital, Otsu, Japan(C Mizumoto MD,
T Takeoka MD); Department of
Hematology, Kansai ElectricPower Hospital, Osaka, Japan(K Io MD); Blood Disorders
Center, Aiiku Hospital,
Sapporo, Japan (T Kondo MD);
Department of Pharmacy,
Akita University Hospital,
Akita, Japan (Prof M Miura PhD);
Department of Hematology,
National Cancer Center
Hospital East, Kashiwa, Japan(Y Minami MD); Department of
Haematology and Oncology,
Research Institute for
Radiation Biology andMedicine, Hiroshima
University, Hiroshima, Japan(J Imagawa MD); ClinicalResearch Center, Saga
University Hospital, Saga,Japan (A Takamori PhD); Tokai
Central Hospital,
Kakamigahara, Japan(Prof J Sakamoto MD)

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Correspondence to:Prof Shinya Kimura, Division of
Hematology, Respiratory
Medicine and Oncology,
Department of Internal
Medicine, Faculty of Medicine,
Saga University,
Saga, 849-8501, Japanshkimu@cc.saga-u.ac.jp
See Online for appendix

This study was done according to the Declaration of
Helsinki and was approved by Saga University and eachparticipating hospital. All partici­
pants provided written
informed consent. The study protocol is given in the
appendix (pp 11–71).

Procedures
Patients received once-daily dasatinib started at a dose of
20 mg (Bristol-Myers Squibb, Princeton, NJ, USA). If thetreatment effect was assessed as an optimal response(according to ELN criteria) at 3 months, 6 months, and9 months, and adverse events were grade 2 or better(according to the NCI Common Toxicity Criteria for
Adverse Events v 4.0), then treatment was continued
without changing the dose. If the treatment effect wasassessed as warning and adverse events were grade 2 or
better, then the daily dose of dasatinib was increased by
20 mg per day. Once a dose reduction had been madebecause of a grade 3 or worse adverse event, the patient
continued treatment at the same dose without further
dose increases, even if the treatment effect was assessed
as warning. If the treatment effect was assessed as
failure, including disease progression to the accelerated

phase or acute phase, then the protocol treatment was
discontinued, and a more appropriate treatment wasprovided at the discretion of the attending physician.
Administration of dasatinib was suspended if
haematological toxicity of grade 3 or higher or nonhaematological toxicity of grade 3 or higher wereobserved during all treatment periods. Treatment was
resumed after recovery from toxicity by reducing thedaily dose of dasatinib by 20 mg. If 20 mg/day dasatinib
caused toxicity, then the frequency of administration was
reduced to alternate-day administration of 20 mg, and if
this regimen resulted in adverse events of grade 3 orworse, the protocol treatment was discontinued.
Quantification of BCR-ABL1 mRNA in peripheral
blood was measured using the BCR-ABL1 mRNA inter­national scale (IS) at a central laboratory (Bio Medical
Laboratories, Tokyo, Japan) using a commerciallyavailable measurement kit, ODK1201 (Otsuka
Pharmaceutical, Tokyo, Japan).15 ABL1 was used ashousekeeping gene. A complete cytogenetic response
MR2·0 was defined as a BCR-ABL1 IS 1%, major
molecular response (or MR3·0) as BCR-ABL1 IS 0·1% orless, MR4·0 as BCR-ABL1 IS 0·01% or less, and MR4·5

56 patients recruited

4 excluded
1 withdrawal of consent before start of treatment
1 ineligible case
2 no data

52 included in full-analysis set
52 included in per-protocol set
52 included in safety analysis set

39 optimal at 3 months

33 optimal at
6 months

4 warning at
6 months

11 warning at 3 months

2 discontinued
at 6 months

3 optimal at
6 months

2 unknown at 3 months

5 warning at
6 months

3 failure at
6 months

1 optimal at
6 months

1 discontinued
at 9 months

1 optimal at
9 months

30 optimal at
9 months

3 warning at
9 months

4 optimal at
4 months

3 optimal at
9 months

3 optimal at
9 months

1 warning at
9 months

22 optimal at
12 months*

1 optimal at
12 months

1 optimal at
12 months

3 optimal at
12 months

3 optimal at
12 months¶

1 discontinued
at 12 months

8 warning at
12 months

2 warning at
12 months‡

3 warning at
12 months§

1 unknown at
6 months

1 optimal at
12 months*||

Figure 1: Trial profile
52 patients received 20 mg/day dasatinib as first-line therapy. *Continued treatment with dasatinib 20 mg/day alone. †One patient had optimal response at 3 months, but this patient did not achieve
early molecular response. ‡One patient was unable to increase the dose of dasatinib at 9 months because of an adverse event. This patient received dasatinib 20 mg/day during the clinical trial.
§One patient was unable to increase the dose of dasatinib at 6 months because of an adverse event. This patient received dasatinib 20 mg/day during the clinical trial. ¶One patient was unable toincrease the dose of dasatinib at 6 months because of a medical condition. This patient received dasatinib 40 mg/day between 6 months and 12 months. ||One patient had missing data at 3 months.
This patient reached major molecular response at 12 months with treatment with dasatinib 20mg.

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Articles

as BCR-ABL1 IS 0·0032% or less. Treatment activity wasassessed at 3 months, 6 months, 9 months, and12 months after treatment initiation (appendix p 1), asper ELN 2013 recommendations,6,13 and was centrally
reviewed. Because ELN 2013 does not stipulate criteria at
9 months, the criteria for assessing treatment effects at
6 months were followed at this timepoint. For adverse
event monitoring, chest X-ray, electrocardiogram, andperipheral blood tests were done at 1 month, 3 months,
6 months, 9 months, and 12 months, and adverse eventswere assessed continuously from the time of signed
informed consent until final follow-up date. Blood cellcounts and immunophenotypic examinations were done
as previously described.4
We assessed the halving time of BCR-ABL1 IS at
3 months. It might be more accurate to use BCR as acontrol gene to calculate halving time16 However, only
ODK1201, which uses ABL1 as a control gene, has been
approved for use under national insurance inJapan. This kit was reported to accurately measure
up to IS 144·6867%.15 Therefore, we adopted the method
reported by Fava and colleagues to calculate halving timeusing ABL as a control gene.17
Plasma samples were collected 2 h and 24 h after
dasatinib administration at 1 month and 12 months forpharmacokinetic analysis to identify the minimum andmaximum plasma concentrations of dasatinib. Dasatinib
concentrations were measured by liquid chromatographtandem mass spectrometry analysis at Akita University,
Akita, Japan.18
Failure to conduct drug administration, laboratory
tests, and assessment of toxicity and activity incompliance with the protocol was regarded as protocol
deviation. Protocol deviations falling under several of
the following categories were regarded as a violation:(1) those affecting the assessment of the endpoints of thestudy, (2) those attributable to the investigator or study
site, (3) those that were intentional or systematic,(4) those that were hazardous or considered tremendous
deviation, (5) those that were clinically inappropriate.
Deviations were defined as protocol deviations that were
not considered violations or acceptable deviations.
Acceptable deviations were defined as protocol deviations
within the acceptable range defined for each study before
or after the study between the study group or research
representative or research secretariat and the data centre.
Deviations in the acceptable range defined in advance
were not listed in the monitoring report.

Outcomes
The primary endpoint of this study was achievementof major molecular response (MR3·0) at 12 months,
as in the DASISION study.3 Secondary endpoints were:(1) achievement of deep molecular response (MR4·0
or MR4·5) 12 months after treatment initiation;(2) incidence of all-grade adverse events or adverse eventsof grade 3 or 4; (3) treatment discontinuation due towww.thelancet.com/haematology Vol 8 December 2021

Participants (n=52)
Age at enrolment, years

77·5 (73·5–83·0)

Sex
Male

35 (67%)

Female17 (33%)

ECOG performance status0

43 (83%)1

8 (15%)2

1 (2%)

Patients who received hydroxycarbamide

7 (4%)

Duration of hydroxycarbamide administration,days

0 (0–11·5)

BCR-ABL1 international scale

102·12%(88·73–126·00)

Sokal
Low
IntermediateHigh

2/49 (4%)
39/49 (80%)
8/49 (16%)

HasfordLow

12/49 (25%)

Intermediate

35/49 (71%)

High

2/49 (4%)

EUTOS
Low

45/51 (88%)

High

6/51 (12%)

Comorbidity
Yes

33 (63%)

Past medical history*Yes

38 (73%)

Medication for other conditions†Yes

47 (90%)

Data are n (%), n/N%, or median (IQR). All participants were Japanese.
ECOG=Eastern Cooperative Oncology Group. EUTOS=European Treatment Outcome
Study. *Patients who were not currently receiving treatment but had previous
disease (appendix p 2). †Patients who were taking some medication forcomorbidities (appendix p 2).

Table 1: Baseline patient characteristics

adverse events during the 12-month treatment period;(4) treatment discon­tinuation due to disease progression
or treat­ment failure during the 12 month treatment
period; (5) association of CML prognostic scoring systems
including Sokal score, Hasford score, and European
Treatment and Outcome Study (EUTOS) score;(6) association of halving time of BCR-ABL1 levelswith
activity 3 months after treatment initiation; (7) associationof the plasma concentration of dasatinib with treatment
activity and adverse events; and (8) association of
peripheral blood T cell and natural killer cell profiles withtreatment activity and adverse events. No post-hoc or
exploratory endpoints were considered in this study.

Statistical analysis
The initial sample size in this study was designed asfollows. The major molecular response rate at 1 year ine905

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BCR-ABL1 IS <10·0%

BCR-ABL1 IS <1·0%

MMR

6 months

9 months

MR4·0

MR4·5

100

Patients (%)

80
60
40
20
0
Number of cases
BCR-ABL1 IS <10·0%
BCR-ABL1 IS <1·0%
MMR
MR4·0
MR4·5

3 months

12 months

Time since treatment initiation
38
25
6
0
0

46
37
19
11
4

44
41
21
12
5

44
44
31
14
7

Figure 2: Cumulative treatment response in eligible patients
Error bars indicate one-sided 95% CIs. At 12 months, the estimated rate of complete cytogenetic response was 85%(95% CI 76–93), of MMR was 60% (48–71), of MR4·0 was 27% (17–37), and of MR4·5 was 14% (6–21).
BCR-ABL1 IS=BCR-ABL1 international scale. MMR=major molecular response. MR4·0=BCR-ABL1 IS 0·01% or lower.
MR4·5=BCR-ABL1 IS 0·0032% or lower.

the DASISION trial for first-line dasatinib at 100 mg per
day was 46%.3 On the basis of these data, we set thethreshold major molecular response for older patients at
48%. We expected a slight improvement in the major
molecular response rate and set the expected major
molecular response at 53% as an alternative proportion.
In the DASISION trial, the major molecular response
rate with imatinib was 28% at 12 months, therefore, thenon-inferiority margin was 10%, based on half of the
major molecular response rate difference (38% [48%minus 10%] as null proportion). Under this condition, thenumber of patients required for normal approximationof binomial distribution with a significance level of 5%
and power of 90% was calculated as 95. Additionally,taking dropouts due to protocol violations into
consideration, the target number of patients to beenrolled was set at 100; however, no protocol deviations
occurred.
However, the final sample size was recalculated due
to ethical considerations, given that 1 year after starting
this study, 13 (72%) of 18 patients had reached major
molecular response. We used the Lee and Liu method19
to calculate the predicted probabilities on thebasis of these results. The predicted probability of
non-inferiority of the major molecular response of the
new treatment over the major molecular response of
the standard treatment (38%) was assumed to be atleast 95% when the expected number of patientswas 50. This predicted probability value was greaterthan 0·99. This simulation led the effective­ness andsafety committee to suggest that the number of patients
recruited should be reduced to 50 for ethical reasonsand this protocol amendment (Aug 15, 2019) was
e906

approved by the Saga University institutional review
board.
The primary and secondary outcomes and safety wereassessed in all patients who completed dasatinib
treatment for 12 months. Patients for whom datanecessary for analysis could not be obtained or who
withdrew consent before treatment were excluded. For
the primary endpoint, non-inferiority of major molecularresponse rate at 12 months and its one-sided 95% CI,
estimated with the normal approximation, was con­sidered to be established if the lower limit was shown tolie above the non-inferiority threshold of 38%. If thelower limit of the major molecular response was above
not only the non-inferiority but also the superiority
threshold of 48%, superiority was also considered to beestablished. For the baseline variables and secondary
endpoints, Fisher’s exact tests or Mann–Whitney tests
were done. The significance level for the primary
non-inferiority hypothesis test indicated one-sided 0·05
and for all other secondary endpoints indicatedtwo-sided 0·05. Statistical analyses were done using
SAS (version 9.4) and R (version 3.2.3). The study was
registered in with the UMIN clinical trial registry,
UMIN000024548.

Role of the funding source
The funder of the study had no role in study design, datacollection, data analysis, data interpretation, or writing of
the report.

Results
Between Nov 1, 2016, and Oct 30, 2019, 56 patients with
newly diagnosed chronic myeloid leukaemia in thechronic phase were assessed for eligibility(figure 1). The
first patient was enrolled on April 12, 2017. Median followup was 366 days (IQR 353–372).
Four (7%) patients withdrew their participation.
52 patients received 20 mg per day of dasatinib as firstline therapy (figure 1). The median age at diagnosis was
77·5 years (IQR, 73.5–83.0; table 1). 35 (67%) patientswere male and 17 (33%) were female (table 1). Although
43 (83 %) of 52 patients had an ECOG performancestatus of 0, 38 (73%) patients had other illnesses before
CML diagnosis, 33 (64%) patients had comorbidities at orafter diagnosis of CML, and 47 (90%) patients needed totake medication regularly (appendix p 2). 44 (85%) patients
received dasatinib therapy for 12 months (appendix p 3).
At 12 months, 31 (60%) of 52 patients had major
molecular response (one-sided 95% CI 48–71; figure 2).
The calculated major molecular response was higher
than the lower confidence limit of 38%, which was set
according to results from the DASISION trial and took
into account a non-inferiority margin of 10% (p=0·0007).
Another endpoint to test for superiority, the percentageof patients achieving major molecular response (48%),
was higher than the major molecular response rate in the
DASISON trial (p=0·047). Of the 31 patients who reached
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Articles

MMR at 12 months (n=31)

Non-MMR at 12 months (N=21) p value

MMR at 12 months, dose
unadjusted (n=23)

Non-MMR at 12 months, dose
unadjusted (n=29)

p value

0·0160BCR-ABL1 mRNA international scale
At diagnosis

n=28; 97·40 (76·23–122·18)

n=21; 112·56 (94·69–143·84)

0·0470n=21; 93·63 (74·34–111·60)

n=28; 111·60 (94·55–137·82)

1 monthn=28; 35·00 (20·22–64·89)

n=19; 48·64 (36·35–69·81)

0·1320n=20; 25·51 (11·16–43·30)

n=27; 63·63 (39·60–73·71)

0·00028

3 months

n=30; 0·41 (0·19–3·46)

n=20; 4·26 (2·03–13·03)

0·0020n=22; 0·33 (0·10–0·43)

n=28; 5·89 (2·11–17·12)<0·0001n=27; 11·4 (9·7–19·1)

n=20; 18·3 (16·1–27·9)0·0050n=20; 10·5 (9·5–11·6)

n=27; 19·4 (16·6–35·3)<0·0001

Halving time
0–3 months

Data are n or median (IQR), unless otherwise indicated. Initial dasatinib dose was 20 mg and dosing was adjusted according to the therapeutic effect and side-effects. MMR=major molecular response.

Table 2: Association of response outcomes with BCR-ABL1 mRNA international scale and halving time

major molecular response at 12 months, 23 patients did
not require the initial dose of 20 mg dasatinib to bechanged.
The median BCR-ABL1 IS was 1·47% at 3 months(IQR 0·37–8·39; n=50; appendix p 4). 39 (75%) patients
had an optimal response according to ELN 2013 recomm­endations (figure 1), 38 (73%) patients had BCR-ABL1 IS
less than 10% at 3 months (early molecular response at
3 months), and 25 (48%) patients had BCR-ABL1 IS 1·0%
or less (MR2·0) at 3 months (figure 2).
Regarding secondary endpoints, at 12 months, 14 patients
had MR4.0 (27% [one-sided 95% CI 16·8–37·0) and sevenpatients had MR4.5 (13·5% [5·7–21·3; figure 2). Eight(15%) patients discontinued because of treat­ment failure(n=3), withdrawal of consent (n=2), drug-related adverse
events (n=1), or other reasons (n=2; appendix pp 3, 5). No
patients who discontinued from the study or interrupted
their treatment because of adverse events transformed toaccelerated phase or blast phase.
We found no correlation between the Sokal, Hasford,
and EUTOS risk scores and the major molecularresponse rate at 12 months (appendix p 6). Therefore, we
compared BCR-ABL1 IS at diagnosis, 1 month, and3 months in patients who had major molecular response
at 12 months and in patients who did not have major
molecular response (table 2). BCR-ABL1 IS values at
3 months and at diagnosis were significantly lower inpatients who had major molecular response at 12 months
than in patients who did not have major molecularresponse. We compared BCR-ABL1 IS (at diagnosis,
1 month, and 3 months) and halving time in patients
who reached major molecular response on protocoltreatment (dose-adjusted treatment) versus in those who
did not reach MMR (by dose-adjusted treatment). We
then compared BCR-ABL1 IS (at diagnosis, 1 month, and3 months) and halving time in patients who reachedmajor molecular response on dasatinib 20 mg alone
versus those who did not. We did not compare patients
who had major molecular response at 12 months(dose-adjusted treatment group) with the group using
20 mg/day of dasatinib only. Interestingly, BCR-ABL1
IS was statistically low throughout the study in patients
who had major molecular response at 12 months with
20 mg/day dasatinib (table 2).
www.thelancet.com/haematology Vol 8 December 2021

We used receiver operating characteristic (ROC) curve
analysis to identify the optimal threshold values forpredicting major molecular response at 12 months using
BCR-ABL1 IS. The cutoff value at 3 months for all
treatment methods used in this study and for 20 mg
dasatinib was 0·662% (appendix p 7). Next, we analysed
the relationship between the halving time of BCR-ABL1
IS during the initial 3 months of the study and major
molecular response at 12 months. The median halving timewas 15·7 days and was widely distributed (IQR 10·6–23·7;
n=47; appendix p 4). Moreover, ROC curve analysis
suggested that the optimal halving time for obtaining
major molecular response at 12 months was 12·5 days (thecut-off value at 3 months for all patients) and 12·0 days
(the cut-off value at 3 months for 20 mg dasatinib;
appendix p 7). We attempted to predict major molecularresponse at 12 months using various cut-off values at
3 months (early molecular response, complete cytogenetic
response, BCR-ABL1 IS 0·662%, and halving time
≤12·5 days). All cut-off values seemed to be predictive ofmajor molecular response at 12 months, except for earlymolecular response (appendix p 8); however, these findings
need further validation.
Treatment-related adverse events of all grades werereported in 50 (96%) of 52 patients (table 3). However,eosinophilia, basophilia, increased lactate dehydrogenase,and increased C-reactive protein were thought to be dueto the condition of CML itself, rather than adverse
events of dasatinib. Pleural effusion was observed infour patients but pulmonary hypertension was notobserved. Five patients had median dose interruptions of
7 days (IQR 5–36 days) due to haematological (n=3) andnon-haematological adverse events (n=2). Three patientsrequired dose reductions to 20 mg every other day,one patient discontinued for drug-related toxicity(QT prolongation), and none died of treatment-relatedcauses (appendix p 3, 5).
The time to reach maximum plasma concentrations isapproximately 2 h after dosing and dasatinib has a shorthalf-life (less than 4 h).20 Sampling times were baseline
and 2 h after dasatinib administration. We compared thegroups that reached major molecular response at
12 months and those that did not. Although we foundno statistical difference in maximum plasma concent­e907

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Grade 3

Grade 438 (73%)

11 (21%)

1 (2%)

Leukocytopenia

13 (25%)00

Neutropenia

6 (12%)

3 (6%)

0

Eosinophilia

8 (15%)

00

Basophilia

19 (37%)00Lymphocytosis

1 (2%)

00Anaemia33 (63%)

1 (2%)

1 (2%)

Discussion

Thrombocytopenia

18 (35%)

1 (2%)

0Superficial oedema

2 (4%)

00

Pleural effusion

4 (8%)

0

0

Diarrhoea

1 (2%)

00

Myalgia

2 (4%)

0

0

Musculoskeletal pain

3 (6%)00Rash

3 (6%)

00

Fatigue

8 (15%)

1 (2%)

0Arrhythmia

3 (6%)

1 (2%)

0Long QT syndrome01 (2%)

0

Dyspnoea

4 (8%)

2 (4%)

0

Acute heart failure0

1 (2%)

0

Pneumonia

0

2 (4%)0

Urinary-tract infection

1 (2%)

1 (2%)

0

Cataract0

1 (2%)

0Hypoalbuminemia

24 (46%)00

Increased total bilirubin

4 (8%)

0

0

Increased ALT

15 (29%)00

Increased AST

17 (33%)

1 (2%)

0

Increased LDH

21 (40%)00Increased BUN

22 (42%)00Increased creatinine

13 (25%)00Increased NT-pro BNP

25 (48%)00Elevated C-reactive protein

22 (42%)00

Hyponatremia

8 (15%)

0

0

Potassium abnormality

15 (29%)00Chloride abnormalities

16 (31%)

00

Hypocalcaemia

9 (17%)

00

In patients with chronic myeloid leukaemia in the chronic
phase treated with an initial dose of 20 mg/day dasatinib,
60% of patients had major molecular response at
12 months, and almost half of patients had major
molecular response by 12 months without a dose
escalation. The major molecular response rate of this study
was similar to that obtained in the dasatinib group of the
DASISION trial (100 mg/day).3
Generally,using
quantitative
RT-PCR
of
BCR-ABL1 mRNA, the plot of BCR-ABL1 mRNA
expression results in a biphasic decrease in
TKI-sensitive patients. This biphasic decrease consistsof an initial steep decrease and a second moderatedecrease. The initial steep slope is attributed to a
reduction of proliferating differentiated leukaemic
cells, and the secondary slope results from a slower
reduction of quiescent leukaemic stem cells.21 Halving
time of BCR-ABL1 mRNA from the point of diagnosis
to that of 3 months has a trend similar to this initialdecline of circulating BCR-ABL1-positive leukaemic
cells. Short halving time indicates reduced circulating
CML cells (both proliferating differentiated leukaemic
cells and stem cells). In this study, we found asignificant difference in median halving time at
3 months after initiation of dasatinib between patients
who had major molecular response at 12 months andthose who did not. This finding is consistent withprevious reports.22
Early molecular response has been recognised as an
essential predictor of having a favourable overall survival
and progression-free survival.23 In this study, earlymolecular response was the first checkpoint for increasing
the dasatinib dose; 73% of patients had early molecularresponse, and of these 63% had major molecular response
at 12 months. To increase the sensitivity to predict major
molecular response at 12 months, the optimal cutoff for
BCR-ABL1 IS at 3 months was 0·662% for all treatment
methods and 20 mg/day dasatinib only. Although thetherapeutic doses of dasatinib used in this study differ
from the dose of TKIs used in previous studies, a clinical
trial showed that achieving MR2.0 at 3 months was
essential for achieving a favourable outcome.24 These
results indicate that rapid downregulation of BCR-ABL1
is necessary to achieve a favourable outcome,22 regardlessof the dose of TKI.
Although trial comparison might be done with caution
because of different observation periods and age groups

Adverse events
HaematologicalNon-haematological

Grade 1-2 events occurring in 10% or more patients and all grade 3-4 events areshown. There were no fatal (grade 5) events. ALT=alanine transaminase.
AST=aspartate aminotransferase. LDH=lactate dehydrogenase. BUN=blood urea
nitrogen. NT-pro BNP=N-terminal pro-brain natriuretic peptide.

Table 3: Summary of adverse events during treatment (n=52)

rations 2 h after dasatinib administration at 1 month
between these two groups, we found a statisticaldifference at 2 h after dasatinib administration at
12 months (p=0·011) in patients who had major
molecular response (23·5 ng/mL) and those who did not(8·0 ng/mL). However, in patients who received only
20 mg dasatinib, we found no significant difference inmaximum plasma concentrations 2 h after dasatinib
administration at 12 months between those who had
major molecular response and those who did not(appendix p 9). In terms of trough levels, we found noe908

significant difference in baseline measures at 1 monthand 12 months between patients with grade 3–4 adverse
events and those with non-grade 3–4 adverse events(appendix p 9).
Notably, only one patient developed lymphocytosis(table 3). We found no statistical difference in the lympho­
cyte profile between the major molecular response andnon-major molecular response groups (appendix p 10).

Grade 1–2

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between trials, we compared the results of DAVLEC with
two other studies, DASISSION3 and an Italian real-life
cohort of patients with chronic myeloid leukaemia in thechronic phase older than 65 years who received front-line
dasatinib.25 The compared safety outcomes were pleural
effusion and haemato­
logical toxicity, which are often
associated with dasatinib treatment. Grade 1–2 pleural
effusions accounted for 14% of adverse events in the
DASISION trial,3 14% in the Italian cohort,25 and
8% in DAVLEC; grade 3–4 effusions accounted for
1% of adverse events in the DASISION trial, 5% in
the Italian cohort, and none in DAVLEC; andgrade 3–4 neutropenia accounted for 21% of adverse
events in the DASISION trial and 6% in DAVLEC. These
results suggest that the treatment algorithm used in
DAVLEC was relatively safe.
Because older patients are more likely to have multiple
comorbidities and receive several medications, which
was reflected in the patients enrolled in this study,administering full-dose TKIs might be harmful.7 To
prevent pleural effusion during dasatinib therapy, thetrough plasma concentration of dasatinib should be kept
below 2·5 ng/mL.20 In the present study, the troughconcentration of dasatinib was sufficiently low whenmeasured at 1 month and 12 months of treatment. As aresult, the incidence of adverse events tended to be lower
than in the DASISION trial.
In this study, the median concentration of dasatinib 2 hafter administration was 50 ng/mL or less, which is
generally an active dose of dasatinib in patients who had
major molecular response as well as those who did not.
The median concentration 2 h after administration at
12 months in patients who had major molecular response
was significantly higher than in patients who did not.
The IQR in the major molecular response group was also
wider than in the non-major molecular response group.
These results might mean that dose increases were given
to patients who did not have an optimal response during
treatment.
A high concentration of dasatinib (50–100 nM,
approximately 25–50 ng/mL) transiently inhibits
BCR-ABL1, leading to irreversible apoptosis of CML cells.
Additionally, inter­mediate concentrations of dasatinib(3–25 nM, approximately 1–13 ng/mL) with extended
treatment times produce progressively more cytotoxicity.26
The standard dasatinib dose, 100 mg/day, was determinedfrom a clinical study of patients with chronic myeloid
leukaemia in the chronic phase who were resistant or
intolerant to imatinib.27 No dose-finding studies exist for
dasatinib in newly diagnosed patients with chronic
myeloid leukaemia in the chronic phase. In older patients,
aging or polypharmacy might slow the metabolism of
dasatinib, leading to prolonged BCR-ABL1 inhibition.
Hence, dasatinib could be allowed to act over an extended
time (≥1 day) to achieve an optimal therapeutic response.
In addition to age and polypharmacy, blood dasatinib
concentrations are higher in East Asians including
www.thelancet.com/haematology Vol 8 December 2021

Japanese individuals than in non-East Asians at the same
dose of dasatinib,28 possibly leading to activity with asmaller dose of dasatinib in Japanese patients with CML.
Previous investigations indicated that both pharma­
cokinetic and pharmacodynamic analyses were essential toachieve the best therapeutic effect of dasatinib treatment.29,30
Therefore, the initial dose of dasatinib for newly diagnosed
patients with chronic myeloid leukaemia in the chronic
phase might need to be re-examined.
From the analyses of three large clinical trials,
lymphocytosis develops in a minimum of 32% of patients
after dasatinib treatment initiation.31 However, lympho­
cytosis was only reported in one (2%) patient in this study .
The reason for this finding was not clear but could be
related to an inadequate immune response in older
patients or because the plasma concentration of dasatinib
was low. Therefore, a relationship might not exist between
the immunological effect of dasatinib and whether apatient reaches major molecular response. Alternatively, a
relationship might exist, but this relationship could be
dependent on immune system factors other than theinteraction between TKI and CML cells. On the other
hand, the low lymphocytosis might be associated with thegenerally low incidence of adverse events.32
Our study had several limitations. First, there was nocontrol group for comparison. Second, for reasons
unknown, there was a sex imbalance. If the proportion ofmen and women had been similar, the results might havebeen different. Third, whether very low-dose dasatinib canlead to treatment-free remission, which is another therapy
goal2,4,5 for patients with chronic myeloid leukaemia in thechronic phase, is unclearfrom our data. Fourth, all ourpatients were Japanese, which limits the generalisability of
the findings due to lack of ethnic diversity and also due tothe fact that in the health-care system of Japan patients areoften diagnosed with CML at an earlier stage than in othercountries. Fifth, mutation analysis was not conducted in
the trial, even in poor responders. The protocol did not
specify the detection of mutations because the trial wasdone in line with general clinical practice as much as
possible and in Japan, testing for mutations is self-funded.
Sixth, assessment of patient-reported outcomes, which areimportant to assess patients’ quality of life, were notplanned as part of the study.
In conclusion, the DAVLEC study showed that a
starting dose of dasatinib of 20 mg/day was active andwell-tolerated in older patients with chronic myeloid
leukaemia in the chronic phase. Furthermore, rapid
BCR-ABL1 downregulation was an essential indicator ofa favourable molecular response, regardless of the doseof TKI used. Further long-term follow-up will benecessary to confirm the results of this study.
Contributors
KM, HU, and ShK made substantial contributions to the study
conception, design, analysis, and data interpretation. KM, HU, TKu, HT,
KN, SW, KI, TF, CY, NU, MM, JA, SaK, KK, KU, TKi, JS, YC, JK, YMa,
KN, TO, KF, HS, CM, TT, TKo, YMi, KI, TI, JI, and ShK enrolled
patients and collected data. MM performed pharmacokinetic analysis.
e909

Articles

AT and AK performed the statistical analyses. KM, HU, and ShK wrote
the paper and critically reviewed the drafts and all authors approved thefinal version. KM, HU, AT, AK, and ShK had access to the raw data. ShK
had final responsibility for the decision to submit the study for
publication as a corresponding author. JSh enrolled the patients andcollected data. JSa critically reviewed the draft.
Declaration of interests
KM has received honoraria from Bristol-Myers Squibb, Novartis,
Pfizer, Celgene, Eisai, Sanofi, Janssen, and Otsuka Pharmaceuticals.
TKu received honoraria from Bristol-Myers Squibb, Novartis, Pfizer,
and Otsuka Pharmaceuticals. HT received honoraria from BristolMyers Squibb, Novartis, Pfizer, and Otsuka Pharmaceuticals.
KN received grants from Kyowa-Kirin, and honoraria from Alexion.
CY received honoraria from Bristol-Myers Squibb, Novartis KK, Pfizer
Japan, Otsuka Pharmaceutical, AbbVie GK, Janssen Pharmaceutical
KK, Nippon Shinyaku, and Chugai Pharmaceutical. NU received
honoraria from Eisai and Janssen Pharmaceutical. KU received grants
from Astellas Pharma, AbbVie, Apellis, SymBio, Daiichi-Sankyo,
Novartis, Janssen, Otsuka, Astellas Amgen Biopharma, Takeda,
Nippon-Shinyaku, Bristol-Myers Squibb, Amgen, Alexion, Incyte, Ono,
Kyowa-Kirin, Celgene, Sumitomo-Dainippon, Chugai, Pfizer, Mundi,
Yakult, MSD, Gilead, and Nippon-Boehringer-Ingelheim and honoraria
from Novartis, Bristol-Myers-Squibb, Sanofi, Pfizer, Abbvie, Takeda,
Ono, Kyowa-Kirin, Astellas, Alexion, Eisai, MSD, Otsuka, Celgene,
Daiichi-Sankyo, Nippon-Shinyaku, PharmaEssentia, Yakult, SymBio,
Alexion, and Chugai. JK received grants from Bristol-Myers Squibb,
Sysmex, Celgene, Ono Pharmaceutical, Otsuka Pharmaceutical,
Sanofi, Kyowa Kirin, Sanofi, Chugai Pharmaceutical, Eisai, Dainippon
Sumitomo Pharma, Nippon Shinyaku, Takeda, Shionogi, Asahi Kasei,
Daiichi Sankyo, MSD, Taiho Pharmaceutical, and Abbvie; has received
honoraria from Bristol–Myers Squibb, Janssen Pharmaceutical KK,
Celgene Corporation, Ono Pharmaceutical, Takeda, Sanofi, Kyowa
Kirin, Chugai Pharmaceutical, Eisai, Astellas Pharma, Nippon
Shinyaku, Dainippon Sumitomo Pharma, Symbio, Daiichi Sankyo,
Fujimoto Pharmaceutical, Abbvie, and Otsuka Pharmaceutical; andconsulting fees from Janssen Pharmaceutical KK, Bristol-Myers
Squibb, Sanofi, and Abbvie. TO received grants from Celgene, Kyowa
Hakko Kirin, Chugai Pharmaceutical, TAIHO Phamaceutical; andhonoraria from Celgene, Kyowa Hakko Kirin, Chugai Pharmaceutical,
Novartis, Bristol-Myers Squibb, Pfizer, Otsuka Pharmaceutical, ONO
Pharmaceutical, Takeda Pharmaceutical, Astellas Pharma, Eisai
Pharmaceuticals, Janssen Pharm, Daiichi Sankyo, and Mundipharma.
KF received honoraria from Bristol-Myers Squibb, Novartis, Pfizer, andOtsuka Pharmaceuticals. HS received honoraria from Takeda, Ono,
Novartis, Janssen, Chugai, Eizai, Nippon Shinyaku, Sanofi,
AstraZeneca, Bristol-Myers Squibb, Otsuka, Mundi Pharma andKyowa Kirin; and research funding from Janssen, Ono, Celgene,Novartis, Sanofi, AstraZeneca, AbbVie, and Chugai. TKo received
honoraria from Bristol-Myers Squibb, Novartis, Pfizer, Otsuka
Pharmaceuticals, and Abbvie; and has served in an advisory role forAstellas Pharma and Otsuka Pharmaceutical. YMi received honoraria
from Bristol-Myers Squibb, Novartis, Pfizer, and Astellas; and researchfunding from Ono and CMIC Holdings. ShK has received honoraria
from Bristol-Myers Squibb, Novartis, Pfizer, and Otsuka
Pharmaceuticals; and research funding from Bristol-Myers Squibb,
Pfizer, Otsuka Pharmaceuticals, and Ohara Pharmaceuticals. All otherauthors declare no competing interests.
Data sharing
Individual participant data that underlie the results reported in this
Article, after de-identification, will be made available beginning
9 months and ending 36 months following Article publication. The
protocol will be available in English or Japanese. Proposals should bedirected to shkimu@cc.saga-u.ac.jp. To gain access, data requestors willneed to sign a data access agreement.
Acknowledgments
This study was funded by Bristol-Myers Squibb. The DAVLEC trial was
supported by the Epidemiological and Clinical Research Information
Network, which collected the data. We thank all of the doctors who
enrolled the patients.

e910

References
1
Bower H, Björkholm M, Dickman PW, Höglund M, Lambert PC,
Andersson TM. Life expectancy of patients with Chronic myeloid
leukemia approaches the life expectancy of the general population.
J Clin Oncol 2016; 34: 2851–57.
2
Mahon FX, Réa D, Guilhot J, et al. Discontinuation of imatinib inpatients with chronic myeloid leukaemia who have maintainedcomplete molecular remission for at least 2 years: the prospective,
multicentre Stop Imatinib (STIM) trial. Lancet Oncol 2010;
11: 1029–35.
3
Kantarjian H, Shah NP, Hochhaus A, et al. Dasatinib versus
imatinib in newly diagnosed chronic-phase chronic myeloid
leukemia. N Engl J Med 2010; 362: 2260–70.
4Imagawa J, Tanaka H, Okada M, et al. Discontinuation ofdasatinib in patients with chronic myeloid leukaemia who havemaintained deep molecular response for longer than 1 year (DADI
trial): a multicentre phase 2 trial. Lancet Haematol 2015;
2: e528–35.
5
Kimura S, Imagawa J, Murai K, et al. Treatment-free remission after
first-line dasatinib discontinuation in patients with chronic myeloid
leukaemia (first-line DADI trial): a single-arm, multicentre,
phase 2 trial. Lancet Haematol 2020; 7: e218–25.
6
Hochhaus A, Baccarani M, Silver RT, et al. European LeukemiaNet
2020 recommendations for treating chronic myeloid leukemia.
Leukemia 2020; 34: 966–84.
7
Saussele S, Krauss MP, Hehlmann R, et al. Impact ofcomorbidities on overall survival in patients with chronic myeloid
leukemia: results of the randomized CML study IV. Blood 2015;
126: 42–49.
8
Luskin MR, DeAngelo DJ. How to treat chronic myeloid leukemia(CML) in older adults. J Geriatr Oncol 2018; 9: 291–95.
9
Crugnola M, Castagnetti F, Breccia M, et al. Outcome of very elderly
chronic myeloid leukaemia patients treated with imatinib frontline.
Ann Hematol 2019; 98: 2329–38.
10 Kizaki M, Takahashi N, Iriyama N, et al. Efficacy and safety oftyrosine kinase inhibitors for newly diagnosed chronic-phasechronic myeloid leukemia over a 5-year period: results from theJapanese registry obtained by the New TARGET system.
Int J Hematol 2019; 109: 426–39.
11 Naqvi K, Jabbour E, Skinner J, et al. Long-term follow-up of lower
dose dasatinib (50 mg daily) as frontline therapy in newly
diagnosed chronic-phase chronic myeloid leukemia. Cancer 2020;
126: 67–75.
12 Itamura H, Kubota Y, Shindo T, Ando T, Kojima K, Kimura S.
Elderly Patients with chronic myeloid leukemia benefit from adasatinib dose as low as 20 mg. Clin Lymphoma Myeloma Leuk 2017;
17: 370–74.
13 Baccarani M, Deininger MW, Rosti G, et al. European LeukemiaNet
recommendations for the management of chronic myeloid
leukemia: 2013. Blood 2013; 122: 872–84.
14 Ito T, Tanaka H, Tanaka K, et al. Insertion of a genomic fragmentof chromosome 19 between BCR intron 19 and ABL intron 1a in achronic myeloid leukaemia patient with micro-BCR-ABL (e19a2)
transcript. Br J Haematol 2004; 126: 752–53.
15 Nakamae H, Yoshida C, Miyata Y, et al. A new diagnostic kit,
ODK-1201, for the quantitation of low major BCR-ABL mRNA levelin chronic myeloid leukemia: correlation of quantitation with major
BCR-ABL mRNA kits. Int J Hematol 2015; 102: 304–11.
16 Branford S, Yeung DT, Parker WT, et al. Prognosis for patients with
CML and >10% BCR-ABL1 after 3 months of imatinib depends on
the rate of BCR-ABL1 decline. Blood 2014; 124: 511–18.
17 Fava C, Rege-Cambrin G, Dogliotti I, et al. Early BCR-ABL1
reduction is predictive of better event-free survival in patients with
newly diagnosed chronic myeloid leukemia treated with any
tyrosine kinase inhibitor. Clin Lymphoma Myeloma Leuk 2016;
16 (suppl): S96–100.
18 Miura M, Takahashi N. Routine therapeutic drug monitoring oftyrosine kinase inhibitors by HPLC-UV or LC-MS/MS methods.
Drug Metab Pharmacokinet 2016; 31: 12–20.
19 Lee JJ, Liu DD. A predictive probability design for phase II cancer
clinical trials. Clin Trials 2008; 5: 93–106.
20 Miura M. Therapeutic drug monitoring of imatinib, nilotinib, anddasatinib for patients with chronic myeloid leukemia.
Biol Pharm Bull 2015; 38: 645–54.

www.thelancet.com/haematology Vol 8 December 2021

Articles

21

22

23

24

25
26
27

Fassoni AC, Baldow C, Roeder I, Glauche I. Reduced tyrosine
kinase inhibitor dose is predicted to be as effective as standard dosein chronic myeloid leukemia: a simulation study based on phase III
trial data. Haematologica 2018; 103: 1825–34.
Murai K, Yamaguchi K, Ito S, et al. Rapid reduction in BCR-ABL1
transcript predicts deep molecular response in dasatinib-treatedchronic-phase chronic myeloid leukaemia patients. Eur J Haematol
2018; 100: 27–35.
Marin D, Ibrahim AR, Lucas C, et al. Assessment of BCR-ABL1
transcript levels at 3 months is the only requirement for predicting
outcome for patients with chronic myeloid leukemia treated withtyrosine kinase inhibitors. J Clin Oncol 2012; 30: 232–38.
Jabbour E, Kantarjian HM, O’Brien S, et al. Front-line therapy withsecond-generation tyrosine kinase inhibitors in patients with earlychronic phase chronic myeloid leukemia: what is the optimal
response? J Clin Oncol 2011; 29: 4260–65.
Latagliata R, Stagno F, Annunziata M, et al. Frontline Dasatinib
Treatment in a “Real-Life” Cohort of Patients Older than 65 Years
with Chronic Myeloid Leukemia. Neoplasia 2016; 18: 536–40.
Shah NP, Kasap C, Weier C, et al. Transient potent BCR-ABL
inhibition is sufficient to commit chronic myeloid leukemia cells
irreversibly to apoptosis. Cancer Cell 2008; 14: 485–93.
Shah NP, Kantarjian HM, Kim DW, et al. Intermittent target
inhibition with dasatinib 100 mg once daily preserves efficacy andimproves tolerability in imatinib-resistant and -intolerant chronicphase chronic myeloid leukemia. J Clin Oncol 2008; 26: 3204–12.

www.thelancet.com/haematology Vol 8 December 2021

28

29

30

31
32

Chuah CT, Nakamae H, Shen ZX, Bradley-Garelik MB, Kim DW.
Efficacy and safety of dasatinib versus imatinib in the East Asian
subpopulation of the DASISION trial of newly diagnosed chronic
myeloid leukemia in chronic phase. Leuk Lymphoma 2014;55: 2093–100.
Vainstein V, Eide CA, O’Hare T, Shukron O, Druker BJ. Integratingin vitro sensitivity and dose-response slope is predictive of clinicalresponse to ABL kinase inhibitors in chronic myeloid leukemia.
Blood 2013; 122: 3331–34.
Ishida Y, Murai K, Yamaguchi K, et al. Pharmacokinetics andpharmacodynamics of dasatinib in the chronic phase of newly
diagnosed chronic myeloid leukemia. Eur J Clin Pharmacol 2016;
72: 185–93.
Schiffer CA, Cortes JE, Hochhaus A, et al. Lymphocytosis after
treatment with dasatinib in chronic myeloid leukemia: Effects on
response and toxicity. Cancer 2016; 122: 1398–407.
Nagata Y, Ohashi K, Fukuda S, Kamata N, Akiyama H, Sakamaki H.
Clinical features of dasatinib-induced large granular lymphocytosis
and pleural effusion. Int J Hematol 2010; 91: 799–807.

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https://doi.org/10.1038/s41556-022-00866-3

A single-cell atlas of non-haematopoietic cells
in human lymph nodes and lymphoma reveals alandscape of stromal remodelling
Yoshiaki Abe 1, Mamiko Sakata-Yanagimoto 2,3,4 ✉, Manabu Fujisawa2, Hiroaki Miyoshi5,
Yasuhito Suehara3, Keiichiro Hattori3, Manabu Kusakabe2,3, Tatsuhiro Sakamoto 2,3,
Hidekazu Nishikii2,3, Tran B. Nguyen2, Yohei Owada6, Tsuyoshi Enomoto6, Aya Sawa7, Hiroko Bando8,
Chikashi Yoshida9, Rikako Tabata10, Toshiki Terao 10, Masahiro Nakayama11, Koichi Ohshima5,
Kensuke Usuki12, Tatsuya Oda6, Kosei Matsue10 and Shigeru Chiba 2,3 ✉The activities of non-haematopoietic cells (NHCs), including mesenchymal stromal cells and endothelial cells, in lymphomas
are reported to underlie lymphomagenesis. However, our understanding of lymphoma NHCs has been hampered by unexplained NHC heterogeneity, even in normal human lymph nodes (LNs). Here we constructed a single-cell transcriptome atlas ofmore than 100,000 NHCs collected from 27 human samples, including LNs and various nodal lymphomas, and it revealed 30
distinct subclusters, including some that were previously unrecognized. Notably, this atlas was useful for comparative analyses
with lymphoma NHCs, which revealed an unanticipated landscape of subcluster-specific changes in gene expression andinteraction with malignant cells in follicular lymphoma NHCs. This facilitates our understanding of stromal remodelling inlymphoma and highlights potential clinical biomarkers. Our study largely updates NHC taxonomy in human LNs and analysis of disease status, and provides a rich resource and deeper insights into LN and lymphoma biology to advance lymphoma
management and therapy.

Lymphomas are haematological malignancies that often
develop from LNs1. Despite advances in treatments, most lymphoma subtypes remain incurable. Therefore, new therapeutic approaches are needed, including those that target the tumour
microenvironment2,3. In lymphomas, as in solid cancers4,5, theactivities of NHCs, such as mesenchymal stromal cells (SCs) andendothelial cells, are thought to facilitate lymphomagenesis andtherefore have potential as therapeutic targets2,3. Indeed, some lymphoma subtypes are reported to exhibit unique interactions with
NHCs6–9; however, lymphoma NHC research is far behind that ofsolid cancers10. In particular, follicular lymphoma (FL) cells are considered to actively interact with NHCs to promote emergence andexpansion9,11,12. SC-derived CXCL12 recruits FL cells in cooperation with CXCL13 (produced by follicular dendritic cells (FDCs)),
which contribute to the follicular localization of tumour cells andtheir proliferation9,13. Other FDC-derived molecules, including
BAFF (encoded by TNFSF13B), interleukin-15 and HGF, may haveanti-apoptotic effects on FL cells14–16. Unfortunately, a complete
understanding of the temporal and spatial associations that underlie
these activities is hampered by the heterogeneity of NHCs. In fact,definitive NHC classification has not yet been achieved in humans,even in normal LNs17,18. Moreover, the identification of alterations

in LN NHC (LNNHC) heterogeneity in the context of lymphomas
is barely underway.
Major subsets of NHCs in LNs, as determined by morphology and topological localization, include blood endothelial
cells (BECs), which include high endothelial venules (HEVs),
lymphatic endothelial cells (LECs) and non-endothelial SCs(NESCs)18–20. Examples of NESCs include T-zone reticular cells(TRCs), medullary reticular cells, perivascular cells and follicularSCs (FSCs), such as FDCs and marginal reticular cells (MRCs)18–20.
Although recent investigations of NHC heterogeneity have usedsingle-cell RNA sequencing (scRNA-seq) technology21–26, human
LN BECs and NESCs have yet to be comprehensively analysed atsingle-cell resolution.
To address this issue, scRNA-seq was used in this study to construct an atlas of human NHCs in LNs and lymphoma. We aimed
to identify previously unrecognized NHC heterogeneity in human
LNs and to distinguish NHCs from lymphomas to define the global
influences of lymphoma cells on the NHC niche. This approach canprovide deep insights into lymphoma stromal biology and resources
applicable to future studies of lymphomas and identify potential
stroma-derived biomarkers that may serve as clinical indicators
and/or therapeutic targets.

Department of Hematology, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan. 2Department of Hematology,
Faculty of Medicine, University of Tsukuba, Tsukuba, Japan. 3Department of Hematology, University of Tsukuba Hospital, Tsukuba, Japan. 4Division ofAdvanced Hemato-Oncology, Transborder Medical Research Center, University of Tsukuba, Tsukuba, Japan. 5Department of Pathology, School of Medicine,
Kurume University, Kurume, Japan. 6Department of Gastrointestinal and Hepato-Biliary-Pancreatic Surgery, Faculty of Medicine, University of Tsukuba,
Tsukuba, Japan. 7Department of Breast-Thyroid-Endocrine Surgery, University of Tsukuba Hospital, Tsukuba, Japan. 8Department of Breast and Endocrine
Surgery, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan. 9Department of Hematology, National Hospital Organization, Mito Medical Center,
Higashiibaraki, Japan. 10Division of Hematology/Oncology, Department of Internal Medicine, Kameda Medical Center, Kamogawa, Japan. 11Department ofOtolaryngology, Head and Neck Surgery, University of Tsukuba, Tsukuba, Japan. 12Department of Hematology, NTT Medical Center Tokyo, Tokyo, Japan.
✉e-mail: sakata.mamiko@fb3.so-net.ne.jp; schiba-tky@umin.net
1

Nature Cell Biology | www.nature.com/naturecellbiology

Resource
Results

Transcriptional features of major NHC components. To profile
NHCs in human LNs and lymphomas, we performed scRNA-seq
and data integration of NHCs extracted from LN samples without
tumour cell infiltration (metastasis-free LNs (MFLNs)) from ninepatients with a neoplasm and nodal FL samples from ten patients(Fig. 1a, Extended Data Fig. 1a–c and Supplementary Table 1). Genemutations identified in FL samples are presented in Supplementary
Tables 1 and 2. Graph-based clustering of integrated cells led to theidentification of three major NHC components (BECs, LECs and
NESCs) and three contaminating haematopoietic cell components(lymphocytes, plasma cells and dendritic cells) on the uniform manifold approximation and projection (UMAP) (Fig. 1b). Cell-type
annotation was performed by analysing the expression levels of
canonical gene markers (Fig. 1c) and differentially expressed genes(DEGs) (Fig. 1d and Supplementary Table 3). Clustered NHCs
were uniformly distributed across patients, cohorts, sample collection sites and patient age (Fig. 1b and Extended Data Fig. 1d,e).
Notably, expression of the marker PDPN, which has been used
to isolate LECs in previous scRNA-seq studies22–24, was either
partially decreased or absent in LECs (Extended Data Fig. 1f).
Accordingly, the proportion of LECs among NHCs detected byflow cytometry was slightly smaller than that determined using
scRNA-seq, although we observed concordance in the proportion of each NHC component between both methods (Fig. 1e andExtended Data Fig. 1g).
To identify subclusters within each of these three major NHC
components, we extracted each NHC component in silico and subjected it to re-clustering. Notably, NHCs of MFLNs and FL weresimilarly distributed (Fig. 1f), which is in contrast to observations insolid cancers27,28. Here we first sought to construct a single-cell atlasof NHCs in MFLNs.
Ten subclusters of human LN BECs. We identified arterial,
capillary and venous BECs (Fig. 2a). Venous BECs were most prevalent in MFLNs, followed by capillary and arterial BECs (Fig. 2b). For
this annotation, we used known markers, including GJA4 for arterial BECs, CA4 for capillary BECs and ACKR1 for venous BECs29–31(Fig. 2c,d and Supplementary Table 4).
Unsupervised clustering of BECs further revealed ten transcriptionally distinct subclusters: large arteries (ABECs); arteries surrounding the LN capsule (caBECs); arterioles (aBECs); tip cells(tBECs); capillary BECs (cBECs); transitional BECs between capillary BECs and activated HEVs (C-aHEVs); activated HEVs (aHEVs);
homeostatic HEVs (hHEVs); CXCL10+ HEVs (CXCL10-HEVs);
and large veins (VBECs) (Fig. 2e,f). Each subcluster exhibited morethan 100 DEGs that helped clearly distinguish the groups (Fig. 2g–i
and Extended Data Fig. 2a).
ABECs, aBECs, tBECs, cBECs and VBECs had plausible counterparts with similar gene expression profiles in mouse tissues,
including LNs25,32, or in other human tissues28 (SupplementaryNote). Although mice exhibit one HEV cluster in LNs25, human
LN HEVs were composed of three subclusters (aHEVs, hHEVs and
CXCL10-HEVs). aHEVs were characterized by a prominent expression of G0S2 (Fig. 2h, Extended Data Fig. 2b and Supplementary

NaTurE CELL BiOLOgy
Table 5), which is upregulated following induction of cell-cycle
progression33, and SELE (Fig. 2h,i and Extended Data Fig. 2a,b),
which is upregulated by inflammation34. By contrast, hHEVs
expressed SELE at low levels (Fig. 2i and Extended Data Fig. 2a,b).
Notably, C-aHEVs and aHEVs both expressed stress-related genes,
including those associated with heat shock proteins, NF-κB activation, JNK activation and shear stress35 (Extended Data Fig. 2b
and Supplementary Table 6), which suggests that these subclusters
respond to active cell deformation or damage.
We next performed trajectory analysis on integrated MFLN BEC
data using the Monocle 3 pipeline36. We were able to identify all BEC
subclusters in a cell object generated using Monocle 3 (Extended
Data Fig. 2c). Trajectory of the arterial component flowed from
ABECs to aBECs and cBECs, finally reaching tBECs (Fig. 2j).
Similarly, trajectory of the venous component initially traced HEV
subclusters (aHEVs and hHEVs), then proceeded to capillary subclusters (C-aHEVs and cBECs) and finally to tBECs (Fig. 2j). Thesefindings support the idea that tBEC migration in LNs generates newcapillary BECs37.
Gene ontology (GO) analysis revealed that factors involved inblood vessel development are enriched in ABECs, caBECs, aBECs
and tBECs (Fig. 2k and Supplementary Table 7), which is in agreement with their arterial or tip cell characteristic. Leukocyte migration and cellular extravasation signatures were most enriched in
aHEVs (Fig. 2k). Molecules associated with apoptosis were enrichedin C-aHEVs and aHEVs (Fig. 2k). Moreover, as reported in mice25,
CXCL10-HEVs expressed molecules associated with interferon andcytokine signalling (Fig. 2k).
Immunofluorescence (IF) analysis of BECs stained with GJA5,
SSUH2 or INSR identified them as large arterial BECs in LNs(ABECs), arterial BECs outside LNs (caBECs) and arterioles(aBECs), respectively (Extended Data Fig. 2d–f). We also detected
tBECs as cells stained positive for LY6H or PGF in the tips of
PLVAP+ cBECs (Fig. 2l and Extended Data Fig. 2g). HEVs strongly
expressing SELE (aHEVs) were frequently observed in interfollicular regions (IFRs) (Fig. 2l), which indicates that IFRs may
serve as niches that play pivotal roles in promoting the influx of
immune cells into LNs. Moreover, staining for PLVAP, HES1 andthe HEV marker MECA-79 revealed that PLVAP+HES1+ capillaries(C-aHEVs) and MECA-79+HES1+ HEVs (aHEVs) (Extended Data
Fig. 2b) were localized near each other (Extended Data Fig. 2h).
Notably, CXCL10-HEVs were frequently observed in IFRs and werelocalized exclusively in the vicinity of aHEVs (Fig. 2l and Extended
Data Fig. 2i). These findings, together with the GO analysis, suggestthat rare CXCL10-HEVs may activate cellular trafficking of adjacent HEVs through cytokine signalling, which results in the heterogeneity of human HEVs.
In summary, our single-cell atlas of LN BECs identified three,
three and four transcriptionally distinct subclusters in arterial, capillary and venous BECs, respectively, which demonstrates the uniqueheterogeneity of these cells in humans (Extended Data Fig. 2j–l).
Eight subclusters of human LN LECs. A human LEC atlas22[,23recently proposed the following six LEC subclusters: subcapsularsinus (SCS) ceiling LECs (cLECs; LEC I); SCS floor LECs (fLECs;

Fig. 1 | Single-cell survey of NHC components in LNs and FL. a, Study overview of the experimental and analytical workflows. FACS, fluorescenceactivated cell sorting; MACS, magnetic-activated cell sorting; SSC, side scatter. b, UMAP plots of stroma-enriched cells from nine human MFLN samples
and ten FL samples, coloured by cell type (top). Major NHC components from MFLN samples and FL samples are shown separately (bottom left andbottom right, respectively). c, Expression levels of marker genes used to identify cell types. Red arrowheads show cells expressing the indicated marker
genes. d, Heatmap showing the expression of top-ranking marker genes for each major NHC component. Key genes are indicated on the left. e, Correlationof the proportions of BECs, LECs and NESCs among stroma-enriched cells, as evaluated using flow cytometry (FCM) analysis and scRNA-seq, coloured
according to patient cohort. Circles indicate biologically independent samples (n = 9 MFLN, n = 10 FL). ρ denotes Spearman’s rank correlation coefficient.
***P = 4.0 × 10−6 (BEC), ***P = 8.0 × 10−6 (LEC), ***P = 2.5 × 10−6 (NESC) (two-sided Spearman’s rank correlation test). f, UMAP plots of LN BECs, LECs and
NESCs after re-clustering analysis shown according to patient cohort. Statistical source data are provided.
Nature Cell Biology | www.nature.com/naturecellbiology

Resource

NaTurE CELL BiOLOgyab
UMAP 2

Human sample collection
LEC

UMAP 1

Plasma cell
MFLN

Lymphocyte

FL

BEC
LEC

Dendritic cell

Single-cell isolation of NHCs

NESC

CD45

BEC

SSC

Enzymatic
digestion

MACS

FACS

NESC

scRNA-seqn = 113,160 cells
MFLN

Single-cell atlas construction, validation,
and application to lymphoma analyses
IF

Cell–cellinteraction

DEG

A-B
C-D
E-F
G-H

Low
abc

PTCL

High

tDLBCLFL
Application to analysis of lymphoma
NHCs

PECAM1

JAM2

PROX1

n = 44,144 cellsdn = 52,638 cells

e

High

ACKR1
PLVAP
AQP1
CD74
VWF
RGCC
PECAM1Low

90
scRNA-seq (%)

MFLNAtlas construction
and validationc

ClinicalTesting otherrelevance lymphomas

Expression

Atlas

FL

BEC
ρ = 0.94***50
y = 1.0× – 2.6
R 2 = 0.94

10
10

50

CD83

BEC
MFLN

Nature Cell Biology | www.nature.com/naturecellbiology

MFLN

y = 1.1× + 0.4
R 2 = 0.99
020

ρ = 0.93***30

0

y = 1.0× – 3.9
R 2 = 0.89
0

30

60

ig

MFLN

NESC
FL

40

NESC

60

Cohort

LEC
FL

20

FCM (%)

H

Lo

f

LEC
ρ = 0.97***0

scRNA-seq (%)

DCN
CXCL14
CCL19
COL1A2
TAGLN
ACTA2
FBLN1
COL3A1
Expression

BE
C
LE
N C
ES
C

PTPRC

NTS
TFF3
PROX1
CD9
ACKR4
NUDT4

w

SDC1

CR2

h

ACTA2

scRNA-seq (%)

40
DCN

90

MFLN

FL

FL

Resource
LEC II); particular SCS cLECs that cover medullary regions(LEC III); capillary LECs in surrounding tissues (LEC IV); valve
LECs (LEC V); and LECs of medullary and cortical sinuses (LEC VI).
Accordingly, we performed unsupervised clustering of MFLN
LECs, DEGs and trajectory analyses, and IF staining to compare
results across studies (Fig. 3a–c, Extended Data Fig. 3a–l andSupplementary Tables 8 and 9). A detailed report of our findings
is included in the Supplementary Note. In brief, we initially identified seven LEC subclusters: cLECs; bridge LECs (bLECs); fLECs
and perifollicular sinus LECs (pfsLECs); collecting vessel LECs (collectLECs); medullary sinus LECs (msLECs); LECs on the upstreamside of valves (Up-valves); and LECs on the downstream side ofvalves (Down-valves) (Fig. 3a–c and Extended Data Fig. 3a–c).
Additional subclustering analysis divided the single fLEC andpfsLEC subcluster into fLECs and pfsLECs23 (Extended Data Fig.
3d–f). Furthermore, IF staining revealed PAI1+, MFAP4+, PTX3+
and MARCO+ LECs (the latter three noted as LEC III, LEC IV
and LEC VI, respectively22) as bLECs, collectLECs, msLECs andpfsLECs, respectively (Extended Data Fig. 3i–l). Our analysis therefore identified a total of eight LEC subclusters and unify data fromrecent reports22–24 (Fig. 3d,e).
Twelve subclusters of human LN NESCs. NESCs were divided into
the following 12 subclusters: SCs at capsule adventitia (advSCs);
SFRP4+ SCs (SFRP4-SCs); SFRP2+ SCs (SFRP2-SCs); SCs enriched
for tumour necrosis factor (TNF) signalling (TNF-SCs); C7+ SCs(C7-SCs); AGT+ SCs (AGT-SCs); TRCs; pericytes (PCs); smoothmuscle cells (SMCs) with high or low ATF3 expression (ATF3hi
or ATF3lo SMCs); MRCs; and FDCs (Fig. 4a,b and Extended Data
Fig. 4a). TRCs, PCs, MRCs and FDCs were annotated on thebasis of conventional taxonomy18. The results that contributed
to the annotation of these known subclusters are included in the
Supplementary Note.
DEG analysis revealed that NESC subclusters exhibited morethan 100 DEGs each (Fig. 4c). advSCs showed the highest CD34
expression among NESCs (Fig. 4d) and are considered the humancounterpart of mouse CD34+ SCs observed at adventitia of the LN
capsule21. Both SFRP4-SCs and SFRP2-SCs shared SFRP2 expression and were discriminated by higher SFRP4 expression in theformer (Fig. 4d,e). SFRP4-SCs also showed relatively high INMT
expression (Supplementary Table 10), which suggests that they are
the counterpart of mouse Inmt+ SCs observed exclusively at medullary cords21. TNF-SCs were specifically characterized by PTX3
expression, and C7-SCs by abundant C7 expression (Fig. 4d,e).
AGT-SCs expressed AGT and high levels of the apolipoprotein
genes APOE and APOC1 (Fig. 4d,e). ATF3hi and ATF3lo SMCs both
expressed muscle-specific MYH11 and PLN (Fig. 4d,e), but differed in the expression of genes associated with cellular responses tostress or mechanical stimuli (Fig. 4f and Supplementary Table 11).
Notably, TNFSF13B, which encodes B-cell-activating factor belonging to the TNF family (BAFF) and is thought to define FDCs20,
was expressed by both MRCs and FDCs but at higher levels by
MRCs (Fig. 4d,e).

NaTurE CELL BiOLOgy
Trajectory analysis revealed that MRCs were connected to
TNF-SCs and C7-SCs (Fig. 4g and Extended Data Fig. 4b), whichindicates that the latter two subclusters might differentiate into
MRCs. Additional analysis showed a continuous trajectory from
SMC subclusters to PCs, TRCs, MRCs and finally to FDCs inhuman LNs (Fig. 4h and Extended Data Fig. 4c), which is consistent with findings in mice of fibroblastic reticular cells in the splenic
white pulp38.
GO analysis revealed that advSCs expressed high levels of genes
involved in the formation of elastic fibres and the extracellular matrix(ECM)39 (Fig. 4i and Supplementary Table 12). In agreement withthe preferential localization of mouse Inmt+ SCs at the medulla21,
their human counterparts, SFRP4-SCs, expressed high levels ofgenes involved in ECM formation (Fig. 4i and Supplementary
Table 12). TNF-SCs expressed genes associated with TNF signalling(IL6 and CCL2) (Fig. 4i and Supplementary Tables 10 and 12), whichsuggests that they function in the chemotaxis of CCR2-expressing
T cells, monocytes and dendritic cells to antigen sites40. C7-SCs
expressed genes related to chemotaxis regulation (Fig. 4i), including
CXCL12 (Fig. 4d), which supports transendothelial T-cell migration
across HEVs41. Top DEGs for AGT-SCs included APOE, AGT and
LPL, which participate in remodelling of protein–lipid complexes
and plasma lipoprotein particles (Fig. 4i and Supplementary Tables
10 and 12), which suggests that AGT-SCs may participate in lipid
metabolism or transport.
IF staining was performed to identify the localization of eachsubcluster in LNs (Extended Data Fig. 4d–o). Fibroblasts positive for decorin (encoded by DCN), a strong marker of advSCs,
SFRP4-SCs, SFRP2-SCs, TNF-SCs and C7-SCs (Extended Data Fig.
4d), were widely distributed in the adventitia, IFRs and medulla(Extended Data Fig. 4e). FBN1+ SCs (advSCs) (Fig. 4d) wereobserved at the capsule adventitia, as observed in mice21 (Extended
Data Fig. 4f). SFRP2+ SCs (SFRP2-SCs and SFRP4-SCs) werepreferentially distributed in the medulla (Extended Data Fig. 4g).
PTX3+ SCs (TNF-SCs) were observed in IFRs (Extended Data
Fig. 4h). C7-SCs were most frequent in the outer cortex, excluding
follicles (Extended Data Fig. 4i), which is consistent with their proposed role in facilitating immune cell migration. AGT+ cells were
found on outer regions of the IFRs, frequently situated between SCSs
and HEVs (Extended Data Fig. 4j). SMCs were observed as α-smoothmuscle actin+ (encoded by ACTA2), MYH11+ or PLN+ cells (Fig. 4d)around not only arterial BECs but also some HEVs (Extended Data
Fig. 4k,l). ATF3 was positive in some SMCs around HEVs in the
IFRs (aHEVs), as well as around arteries (Extended Data Fig. 4l).
In line with the DEG analysis between SMC subclusters, ATF3+
SMCs were also marked by HSP70 (encoded by HSPA1A) expression (Extended Data Fig. 4m), which probably reflects cell damage
induced by blood flow42 and/or immune cell trafficking.
To summarize, we identified 12 NESC subclusters, thereby showing unanticipated heterogeneity, linked to the distribution of other
NHC subsets and LN niches (Extended Data Fig. 4p–r).
We accomplished a single-cell atlas of NHC components in human LNs (Extended Data Fig. 4p). Additional

Fig. 2 | A single-cell atlas of human LN BECs. a, UMAP plot of MFLN BECs coloured according to classification of arterial, capillary and venous BECs.
b, The proportions of arterial, capillary and venous BECs in MFLN samples. c, Expression levels of arterial, capillary and venous BEC marker genes.
d, Heatmap showing the expression of top-ranking marker genes of arterial, capillary and venous BECs. Key genes are indicated on the left. e, UMAP plotof ten MFLN BEC subclusters identified by unsupervised clustering. f, Prevalence of each BEC subcluster in MFLN samples. g, Number of DEGs per BEC
subcluster. h, Heatmap showing the expression of top-ranking marker genes for each BEC subcluster. Key genes are indicated on the left. i, Violin plots
representing the expression of top marker genes for each BEC subcluster. j, Single-cell BECs ordered according to pseudotime developmental stages. Dark
winding lines in the cell object indicate putative developmental trajectories. Cell regions are assigned to BEC subclusters based on subcluster-defining
gene expression levels. k, GO enrichment analysis of DEGs for each BEC subcluster. l, IF staining of PLVAP (white) and LY6H (red) (top left) to identify
tBECs; MECA-79 (green), SELE (white) and CXCL10 (red) (top right) to identify aHEVs and CXCL10-HEVs; and MECA-79 (green), SELE (white) andCD31 (red) (bottom) to discriminate aHEVs (arrowheads) from hHEVs. Dashed lines indicate follicles. Scale bars, 50 μm (grey) or 200 μm (white).
Representative images from one of three independent experiments are shown.
Nature Cell Biology | www.nature.com/naturecellbiology

d

MFLN
Arterial

26.4%

40.0%

Capillary

Venous
UMAP 2GJA4
SEMA3GHEY1
ACE

Arterial

VenousCapillary

33.6%
n = 16,579 cells

UMAP 1

c

CA4

ACKR1
Expression

GJA4

High
Low

hHEV

g

288

Expression

CA4
CDKN1A
SELE
SELP

aHEV
VBEC
hHEV
CXCL10-HEV

4
4
5
4
5
5
6
4
7
5

j

Pseudotime
0

20
tBEC

cBEC

aBEC

C-aHEVaHEV
CXCL10-HEV

caBEC

n = 16,579 cellsl
DAPI MECA-79
SELE CXCL10tBEC

aHEV andCXCL10-HEVDAPI PLVAP LY6H

DAPI MECA-79
SELE CD31
aHEV and hHEV

–log10(P )Blood vessel development
Regulation of cell adhesion0
15
Muscle structure development
ECM organization
Leukocyte migration
Response to growth factor
Focal adhesion
Muscle cell differentiation
Notch signalling pathway
ECM proteoglycans
Laminin interactions
Cellular extravasation
Smooth muscle cell differentiation
Cytokine-mediated signalling pathway
Attenuation phase
Apoptosis
Regulation of innate immune response
Interferon signalling
Regulation of inflammatory response
Regulation of IκK/NFκB signalling

IFR
Follicle
T-zone

SELE

2

hHEV

VBEC

k

Nature Cell Biology | www.nature.com/naturecellbiology

3

Component 1

ABEC

AB
E
ca C
BE
aB C
EC
tB
E
cB C
E
C
-a C
H
E
aH V
EV
C
XC h
L1 HE
0- V
H
E
VB V
EC

Lo

w

ig
h

210
216

106

AB
ca EC
BE
aB C
E
tB C
E
cB C
C E
-a C
H
a EV
C
XC HE
L1 hH V
0- EV
H
VBEV
EC

Expression

PGF

IL6

CXCL10CXCL11
CXCL9
CLU
IL6
VCAM1

0

EDNRB

CXCL10

MADCAM1
SELP

161

192

203

200

100

SSUH2

G0S2
SELE
CH25H

326
273

278

Number of DEGs

300

GJA5

tBEC
cBEC
C-aHEV

AB
ca EC
BE
aB C
EC
tB
E
c C
C BE
-a C
H
E
aH V
C
XC h EV
L1 HE
0- V
H
E
VB V
EC

cBEC
20.9%

C-aHEV
11.7%

i

H

aHEV
12.4%

CXCL10-HEV
n = 16,579 cells

ABEC
caBEC
aBEC

INSR
EDNRB
KDR
PGF
CXCR4
LY6H
CA4
PLVAP
CD36
HSPA1ACDKN1A
HES1

tBEC
1.0%

ABEC

aHEV

SSUH2
S100A4

aBEC
11.5%

hHEV
17.1%

C-aHEV
VBEC

High

5.3%

UMAP 1caBEC

IGFBP3
GJA5

ABEC
9.6% caBEC

aBEC

cBEC

Low

CA4
RGCC
CD36
PLVAP
CD300LG
ACKR1
MADCAM1
CH25H
SELE
SELP
VCAM1

h
VBEC
10.1%
CXCL10-HEV
0.5%

tBEC

High

Low

f

e
ExpressionbArt
C eriaap l
il
Ve larynousa

UMAP 2

Resource

NaTurE CELL BiOLOgy

Resource

NaTurE CELL BiOLOgy

msLEC

ACKR4

SERPINE1

BMP2
Expression

Down-valve

MFLN

c

UMAP 2a

UMAP 1Up-valve
cLEC
bLEC
fLEC and pfsLEC
collectLEC
msLEC
Up-valve
Down-valve

cLEC

fLEC and pfsLEC

bLEC

n = 7,897 cells

b

CXCL5

MARCO

MFAP4

PTX3

CLDN11

GJA4

High
Low

collectLEC

Down-valve 7.2%
Up-valve 4.0%

cLEC 21.1%

msLEC 16.2%

bLEC 21.0%
collectLEC 17.1%
fLEC and pfsLEC
13.5%

d

e
Efferent
collecting
vesselSCS

Takeda et al.
(human)Lymphatics
MedullaCortex

Perifollicular
sinus

cLEC

Afferent
collecting
vessel

Xiang et al.
(mice)

Fujimoto et al.
(mice)

cLEC

cLEC

cLEC

LEC I: cLEC

Medullarysinus
Follicle

The present study(human)

bLEC
Lymph
flow

bLEC
fLEC

LEC II: fLEC

fLEC

fLEC

fLEC

MARCO-LEC

msLEC

Egress sinus
LEC

LEC VI

pfsLECLEC III:
Med-cLEC

collectLEC

LEC IV

msLEC

msLEC

Up-valve
Down-valve

Up-valve
Down-valve

valvepfsLEC
collectLEC
Up-valve
Down-valve

LEC V

msLEC

Fig. 3 | A single-cell atlas of human LN LECs. a, UMAP plot of MFLN LEC subclusters identified by unsupervised clustering. b, The prevalence of each LEC
subcluster in MFLN samples. c, Expression levels of marker genes for each LEC subcluster. d, Schematic showing the topological localization of eight LEC
subclusters in the LN. e, Comparison of subclusters identified here with those previously characterized (Takeda et al.22, Xiang et al.23 and Fujimoto et al.24).
Bar heights of the previous studies are adjusted to cell numbers (belonging to each subcluster) identified in this study.

basic profiles of the atlas are described in the Supplementary
Note, Extended Data Figs. 5a–c and 6a,b and Supplementary
Tables 13 and 14.
Remodelling of NHC proportions in FL. Using this atlas, we nextsought to explore alterations in FL NHCs at subcluster levels bycomparing them with MFLN counterparts (Fig. 5a and Extended
Data Fig. 7a). Overall, the proportion of BECs were markedly
increased in FL relative to MFLNs, whereas the proportion of LECs
decreased (Fig. 5a). Moreover, the proportion of arterial subclusters
were increased in FL BECs (Fig. 5a). In FL NESCs, the proportionof FDCs was greatly increased (Fig. 5a). Notably, MRCs were also

greatly increased in FL, whereas advSCs, SFRP4-SCs, SFRP2-SCs
and TNF-SCs were decreased (Fig. 5a).
Subcluster-specific transcriptional changes in FL stroma. We next
performed multistep DEG analyses in NHC subclusters of MFLNs
and FL by monitoring differences in gene expression between mesenteric LNs (mLNs) and peripheral LNs (pLNs) (Extended Data
Fig. 6a,b, Supplementary Tables 13 and 14 and SupplementaryNote). We observed the greatest differences in MRCs, followed by
TRCs, SMC subclusters, PCs and FDCs (Fig. 5b and Supplementary
Tables 15–17). Figure 5c shows the expression levels of the top
three DEGs upregulated in FL NHC subclusters in comparison
Nature Cell Biology | www.nature.com/naturecellbiology

Resource

NaTurE CELL BiOLOgyd

UMAP 1

AGT-SCTNF-SCPC

SFRP4-SCCR2–log10(adjusted P value)

MYH11
ACTA2
PLN
TNFSF13B
VCAM1

w

H

Lo

SF ad
v
SFRP SC
R 4-S
P2 C
TN -S
F C
C -SC
AG 7-S
T- C
S
TRC
AT
C
F
AT 3h PF3 i S C
lo MC
SM
M C
R
FDC
C

120

FDCSP
CXCL13
CR2
ExpressionhPseudotime
AGT-SC

20

TRC
PC

C7-SC

0

MRC

advSC

FDC

FDC

UMAP 1

ATF3hi SMC
UMAP 2

UMAP 2

SFRP2-SCTNF-SCn = 12,019 cells

30

ATF3lo SMC

MRC

5
7
7
6
6
4
4
8
4
5
4
5

SF ad
SFRP vSC
R 4-S
P C
TN 2-S
F C
C -S
AG 7-SC
T- C
S
TRC
AT
ATF3h PC
F3 i S C
lo MC
SM
M C
R
FD C
C

f

Upregulated in
ATF3lo SMC

NDUFA4L2
HIGD1B

491

525
545
589
152
230
134
205
320

363
206

0

HIGD1BTNFSF13B

CCL19
CCL8
CCL21

SF ad
v
SFRP SC
R 4-S
P2 C
TN -S
F C
C -SC
AG 7-S
T- C
S
TRC
AT
C
ATF3h PF3 i S C
lo MC
SM
M C
R
FDC
C
Pseudotime

CCL19

APOE
APOC1
AGT

FDC 0.7%
MRC 2.1%
advSC 10.0%
ATF3lo SMC 5.6%
SFRP4-SCATF3hi SMC 9.7%
12.2%
PC 4.8%
TRC 4.5%
SFRP2-SC
AGT-SC 4.1%
17.0%
C7-SC 16.2%
TNF-SC 12.9%

g

AGT

PLN

C7
CXCL12
COL15A1

ATF3hi SMC
ATF3lo SMC
MRC
FDC

C7-SC
AGT-SC
TRC
PC

PTX3
COL15A1

Low

ATF3lo SMC
n = 16,883 cells

advSC
SFRP4-SCSFRP2-SCTNF-SCSFRP2PTX3TNFAIP6
CCL2

h

advSC

Number of DEGs

TNFAIP6SFRP4PLA2G2A
SFRP2SFRP4-SC

600
500
400
300
200
100
0

SFRP4
WISP2
NOV

ATF3hi
SMC

SFRP2
-SC

c

PCOLCE2

TRC

C7-SC

b

FBN1
PCOLCE2
CD34
High

MRC

e

Expression

MFLN

ig

FDC

UMAP 2an = 5,339 cells

UMAP 1

Upregulated in
ATF3hi SMC
JUNB
ATF3
SERTAD1
SOCS3

200

FOS

CDKN1A
MAFF
IRF1
HSPA1A

100

CCL2
CARMN

0
–2

0

2

Log2(fold change)i–log10(P )Elastic fibre formation
0
15
Naba’s core matrisome
TNF signalling pathway
Positive regulation of chemotaxis
Protein–lipid complex remodelling
Plasma lipoprotein particle remodelling
Leukocyte migration
Dendritic cell migration
Muscle structure development
Smooth muscle contraction
Blood vessel development
Cytokine-mediated signalling pathway
Chemokine-mediated signalling pathway
Regulation of leukocyte chemotaxis
Antigen processing and presentation
Immunoglobulin mediated immune response
B-cell-mediated immunity

Fig. 4 | A single-cell atlas of human LN NESCs. a, UMAP plot of MFLN NESC subclusters identified by unsupervised clustering. b, The prevalence of each
NESC subcluster in MFLN samples. c, Number of DEGs per NESC subcluster. d, Heatmap showing the expression of top-ranking marker genes for each
NESC subcluster. Key genes are indicated on the left. e, Violin plots representing top marker genes for each NESC subcluster. f, Volcano plot of upregulatedor downregulated genes between ATF3hi and ATF3lo SMCs. Significance was determined as an adjusted P < 0.05 (two-sided Wilcoxon rank-sum test with
Bonferroni correction) (blue dots) and log2 fold-change of ≥1 (red dots). Larger dots indicate log2 fold-change of ≥2. Key genes are labelled. g,h, Pseudotime
developmental stages of single cells in advSCs, SFRP4-SCs, SFRP2-SCs, TNF-SCs, C7-SCs, MRCs and FDCs (g) or in SMC subclusters, PCs, TRCs, AGT-SCs,
MRCs and FDCs (h). Dark winding lines in the cell objects indicate putative developmental trajectories. Cell regions are assigned to each subcluster basedon subcluster-defining gene expressions. i, GO enrichment analysis of DEGs for each NESC subcluster.

to their MFLN counterparts. In MRCs, CXCL13 was most markedly upregulated, and GO terms related to lymphocyte migration
were enriched (Fig. 5c, Extended Data Fig. 7b,c and Supplementary
Tables 17 and 18), which suggests that MRCs, in addition to FDCs,
Nature Cell Biology | www.nature.com/naturecellbiology

function in the accumulation of malignant B cells13. The expression of TNFSF13B was significantly enhanced in FL NESC subclusters, including SFRP4-SCs and AGT-SCs (Extended Data Fig. 7b).
IL15 and HGF expression levels also tended to be increased inResource
Major NHC components
0

BEC
LEC
NESC

MFLN

0.5

1.0

FL

b

0

ABEC
caBEC
aBEC
tBEC
cBEC
C-aHEVaHEV
hHEV
CXCL10-HEV
VBEC
MFLN

cLEC
bLEC
fLEC and pfsLEC
collectLEC
msLEC
Up-valve
Down-valve

1.0

BEC

MFLN

advSC
SFRP4-SCSFRP2-SCTNF-SC
C7-SC
AGT-SCTRC
PC
ATF3hi SC
ATF3lo SC
MRC
FDC

0.5

1.0

FL

LEC

MFLN

FL

NESC

5

600
Number of DEGs

NS

0

0.5

FL

NESC

NS

52

1
42

500
400
300
200

70

100

47

70

86

51

22

38

32

51

6

15

77

37

26

1

1
35

10

3

1

0 202 203

18
22

4

7

9 4
12 1

21

3
1 2

20

R
C
FD
C

1.0

NS

0

0.5

LEC

BEC

*

SC

a

NaTurE CELL BiOLOgy

3

17

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**

3

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e

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MLEC

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4

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***

***

HLA-DRA HLA-DPB1SELE
CYP1B1
HSPA1A

RCAN1
CD74

6

5

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Up-

C

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***

***

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VBE

6

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4

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4

7

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SRGN

***

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POSTN

5

6

5

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4

4

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***

hHE

msL

CCL19

4

AGT

***

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5

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POSTN

***

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5

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7

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and

CXCL9

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ACE

***

6

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***

5

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LY6H
RGS16
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HSPA1ADNAJB1

5

***

7

4

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5

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6

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6

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***

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6

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7

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adv

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4

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***

5

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6

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***

COMP

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CCL26

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CD74
SH3BP5 HLA-DRA

***

SC

NESC

4

C
cLE

5

7

***

NR4A2

***

C

caB

SAT1

4

GJA4

***

MMP2

BEC

7

***

6

5

LEC

ABE

C3

EC

C

5

FL

EV
10-H

Fig. 5 | Compositional and transcriptional changes in FL stroma. a, Differences between MFLN and FL NHC compositions based on major NHC
components, and BEC, LEC and NESC subclusters. *P = 0.010 (two-sided Chi-squared test). NS, not significant. b, Number of DEGs upregulated in FL NHC
subclusters compared to MFLN counterparts. c, Violin plots of the top three DEGs upregulated in FL NHC subclusters compared to MFLN counterparts.
**P < 0.01, ***P < 0.001 (two-sided Wilcoxon rank-sum test with Bonferroni correction). Exact P values are provided in Supplementary Tables 15–17.
Statistical source data are provided.
Nature Cell Biology | www.nature.com/naturecellbiology

NaTurE CELL BiOLOgy
some FL NESC subclusters, although this finding was not significant (Extended Data Fig. 7b). Notably, in some NESC subclusters,
we observed marked upregulation of genes relevant to solid cancers but previously not associated with lymphomagenesis. Amongthem, POSTN, which encodes periostin (a protein secreted bycancer-associated fibroblasts (CAFs) and promotes the formation of cancer stem cell, perivascular and premetastatic niches43),
was substantially upregulated in TRCs and PCs and in SMC subclusters of FL (Fig. 5c, Extended Data Fig. 7b and Supplementary
Table 17). The expression of EGFL6, which encodes EGFL6 (a member of the EGF-like superfamily that reportedly promotes tumour
cell growth by stimulating angiogenesis44,45), was highly upregulated in TRCs, SMC subclusters and MRCs (Fig. 5c, Extended Data
Fig. 7b and Supplementary Table 17). CAFs positive for fibroblast
activation protein (FAP) are associated with an immunosuppressive
environment, which hampers immunotherapy46–48. Intriguingly, FAP
was most upregulated in FSCs (MRCs and FDCs) (Extended Data
Fig. 7b and Supplementary Table 17), which indicates that FSCs
may create an immunological environment that favours malignant
cells in FL.
In FL BECs, GJA4 was upregulated in arterial subclusters,
ABECs and aBECs (Fig. 5c and Supplementary Table 15), a pattern that is reflective of arterial vessel development29. Other genes
involved in blood vessel development or ECM organization wereupregulated in almost all subclusters (Extended Data Fig. 7c andSupplementary Tables 15 and 18). FL HEV subclusters showed
high SELE expression (Fig. 5c and Supplementary Table 15), which
is suggestive of inflammation and HEV activation49,50. Indeed, FL
HEV subclusters expressed genes that regulate cellular adhesion
and migration (Extended Data Fig. 7c and Supplementary Tables
15 and 18). Notably, expression of the tip cell markers LY6H, PXDN,
PGF and LOX was markedly upregulated in FL tBECs (Fig. 5c andSupplementary Table 15), which suggests that they are involved in
the acceleration of angiogenesis. The significant decrease in the proportion of LECs in FL suggests that there is widespread lymphatic
damage. IF staining confirmed that the LEC density was lower in
FL compared with that in MFLNs (Extended Data Fig. 7d,e). Many
FL LEC subclusters also showed upregulation of heat shock genes
as well as CD74, which reportedly functions in wound healing51(Fig. 5c, Extended Data Fig. 7c and Supplementary Tables 16 and18). CD74 overexpression was confirmed in FL LECs by IF staining(Extended Data Fig. 7f,g).
Landscape of intercellular interactions in FL stroma. To assess
the NHC–malignant B-cell crosstalk underlying FL growth, we
performed scRNA-seq of cryopreserved CD45+ cells from nine
FL samples (FL 2–FL 10) and extracted gene expression profiles ofmalignant B-cell clusters in silico from each (Extended Data Figs. 1cResource
and 8a–d). We then performed intercellular ligand–receptor interaction analyses between FL NHC subclusters and malignant B cells
using CellPhoneDB52. Thereafter, we extracted significant interactions that were considered upregulated in FL NHC subclusters
relative to the corresponding MFLN subclusters.
We identified a total of 58 interactions, including some previously uncharacterized in FL (Fig. 6a). In BECs, we noted that overexpression of JAG1, which is reportedly observed in B-cell lymphoma
BECs and associated with aggressive lymphoma phenotypes53, waslimited to only larger arterial BEC subclusters (ABECs and caBECs)(Fig. 6a). Interactions mediated through adhesion molecules,
including the SELE–CD44 interaction54,55, were activated mainly
in HEV subclusters (C-aHEVs, aHEVs and hHEVs) (Fig. 6a),which suggests that these HEV subclusters may contribute to thehaematogenous expansion of FL cells54,56. Interactions that promote cancer cell death and mediated by TNFSF10 were markedly
upregulated in several LEC subclusters57 (Fig. 6a), which suggeststhat LECs may antagonize lymphoma development. In NESCs,
interactions associated with TNF signalling, cell adhesion, PDGF
signalling and chemokine signalling were differentially activatedamong subclusters (Fig. 6a). Notably, overexpression of CXCL12,
which reportedly supports FL cell migration, adhesion and activation58, was observed in advSCs (Fig. 6a). Moreover, interactions via
BAFF were upregulated, even in medullary SCs (SFRP4-SCs), whichsuggests that stromal remodelling in FL supports the extrafollicular expansion of malignant B cells59. In advSCs and medullary SC
subclusters, interactions mediated by stroma-derived CD70 wereenhanced (Fig. 6a). Interactions mediated through PDGFRB, which
promotes cell migration and angiogenesis60, were enhanced in
TRCs and PCs (Fig. 6a), which suggests that during FL expansion,mechanisms other than CCR7–CCL19/CCL21 signalling may drive
the homing of malignant B cells to the T-cell zone61. Instead, theCCR7–CCL19 interaction was extended to non-TRC SCs (TNF-SCs
and PCs) (Fig. 6a). Consistent with the DEG analyses of MFLNs
and FL, the CXCL13–CXCR5 axis9,13 was activated in MRCs and
FDCs (Fig. 6a).
Enhanced CD70–CD27 interaction across FL stroma. Based on
the above interactome analysis results, we next sought to explore aninteraction that can potentially be targeted in lymphoma. We carefully surveyed candidate interactions from the perspective of novelty in the field. We noted that the CD70–CD27 interaction in solidand haematological cancers has attracted increasing attention62–65,
whereas interactions mediated by stroma-derived CD70 have rarely
been investigated. Accordingly, we focused on the CD70–CD27
interaction for functional validation to verify the usefulness of
our atlas-based analyses and to propose a potential mechanism inthe stroma relevant to FL progression. Initially, we confirmed that

Fig. 6 | Dissection of stroma–malignant B-cell interactions in FL. a, Enhanced interactions across FL NHC subclusters and malignant B cells (Bmalignant).
Circle size indicates the negative log10 of adjusted P values (Methods). Circles are coloured when a stroma-derived factor is upregulated in relevant
FL subclusters. b, IF staining for MECA-79 (cyan), DCN (red) and CD70 (green) in MFLN and FL samples. Scale bars, 200 μm. Representative images
from one of three independent experiments are shown. c, Proportions of CD70+ area in medullary and adventitia regions of MFLN (n = 3) and FL(n = 3) samples. Circles represent biologically independent samples. Bars indicate the median. **P = 0.0095 (two-sided unpaired t-test). d, Bindingof FL CD19+CD10+ cells to CD70-Fc protein with an anti-CD27 blocking antibody or isotype human IgG. The histograms represent three independent
experiments (FL 13) with the count in arbitrary units. e, Blocking of FL CD19+CD10+ cell binding to CD70-Fc protein after treating cells with an anti-CD27
blocking antibody (n = 3) or isotype mouse IgG1 (n = 3) in CD27+ FL samples (FL 11–FL 14). Proportions of cells bound to CD70-Fc protein were adjusted
by subtracting nonspecific binding observed with human IgG. CD70-Fc protein binding to cells treated with isotype mouse IgG1 was set to 100% ineach experiment. Circles represent independent experiments. Bars indicate the median. **P = 0.0022, ***P = 7.3 × 10−4 (FL 11), ***P = 2.2 × 10−4 (FL 12),***P = 7.6 × 10−4 (FL 13) (two-sided paired t-test). f, Representative malignant B-enriched cell (FL 14) adhesion to medullary regions of FL in the presenceof an isotype mouse IgG1 or anti-CD27 antibody. Orange dots indicate adherent cells. Yellow dashed lines indicate medullary regions. Scale bars, 200 μm.
g, Blocking of malignant B-enriched cell (FL 11, FL 13 and FL 14) adhesion to FL medullary regions (per mm2) after treating cells with an anti-CD27 blocking
antibody (n = 3) or isotype mouse IgG1 (n = 3). Adhesion of cells treated with isotype mouse IgG1 was set to 100% in each experiment. Circles represent
independent experiments. Bars indicate the median. *P = 0.041 (FL 11), *P = 0.027 (FL 14), **P = 0.0050 (two-sided paired t-test). Statistical source data
are provided.
Nature Cell Biology | www.nature.com/naturecellbiology

101

102

Protein binding(PE-anti-Fc)

103

104

0

57.3%

CD70-Fc +Mouse IgG1

11.7%

CD70-Fc +CD27 blocking

CD70-Fc +CD27 blocking

MedullaAdventitia
MFLN

f
Mouse IgG1

100

50

CD27 blocking

14

Adventitia

13

10

FL

CD70

FL

1

b

11

Human IgG

**

G

e

Upregulated in FL stroma

FL

5.0%
DCN

Ig

***
***
***

0 ≥1 ≥2 ≥3sed– log10(P )ou

MECA790.5 1.0

M

Interaction
score
0Adherent cells/medulla (adjusted, %)MedullaCD70+ area
/adventitia and medulla (%)

Overlaym CD
ou 7
se 0-FIg c +
G
1
FL
11
FL
12
FL
13
FL
14

PODXL–SELL
CXCL12–CXCR4
JAG1–CD46
CD34–SELL
MET–LGALS9TNFRSF14–MIF
TNFRSF14–LTA
PLXND1–SEMA4A
ICAM1–AREG
VCAM1–a4b1 complex
HLA-F–LILRB1MDK–SORL1
NRP1–VEGFBICAM1–aLb2 complex
ICAM1–ITGALICAM4–aLb2 complex
SELE–CD44
SELE–GLG1SELP–CD24
CD74–MIF
CD74–COPA
HLA-C–FAM3C
TNFSF10–TNFRSF10B
TNFSF10–TNFRSF10A
TNFSF10–RIPK1
JAG1–NOTCH1
FN1–a4b1 complex
FN1–a4b7 complex
TNC–a4b1 complex
CSF1–SLC7A1
VCAM1–a4b7 complex
ACKR4–CCL5
HGF–CD44
CD70–CD27
CD70–TNFRSF17
PLA2G2A–a4b1 complex
MRC2–LGALS9
CD70–TNFRSF13B
BMP7–SLAMF1
CD44–LGALS9TNFSF13B–HLA-DPB1
LRP1–LGALS9TNFSF13B–TNFRSF13C
TNFSF13B–CD40
PLXNC1–SEMA7A
TNFSF13B–TFRC
TNFSF13B–TNFRSF17PDGFRB–PDGFDACKR1–CCL17
ACKR1–CCL5
CD47–LGALS9
CCL19–CCR7
OGN–HLA-DRB1
NECTIN2–TIGIT
ICAM1–SPN
NRG1–HLA-DPB1
NRG1–LSRCXCL13–CXCR5Interactions(NHC–Bmalignant)

NHC type(– Bmalignant)

Recombinant CD70-Fc
binding (adjusted, %)

MFLN

NESC

ABEC
caBEC
aBEC
tBEC
cBEC
C-aHEVaHEV
hHEV
CXCL10-HEV
VBEC
cLEC
bLEC
fLEC and pfsLEC
collectLEC
msLEC
Up-valve
Down-valve
advSC
SFRP4-SCSFRP2-SCTNF-SC
C7-SC
AGT-SCTRC
PC
ATF3hi SMC
ATFlo SMC
MRC
FDC
LEC

FL

a

BEC

Count

Resource
NaTurE CELL BiOLOgy

Yes No

c

**

5

0
FL

g

*

*
**

100

50

0

CD27 blocking

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Resource

NaTurE CELL BiOLOgyaExpression

High

LOX

P = 0.006
HR = 1.9

1.0

P < 0.001
HR = 2.1

0.5

0.5
n = 135n = 4500n = 108n = 72

0

10

0

20

10

Probability of overall
survival

TDO2
1.0

20

0.5

1.0

P = 0.002
HR = 1.9

0.5
n = 156n = 24

0
0

10

20

Overall survival (years)010

HR (95% CI)

P value<0.001

3.12 (1.96–4.96)<0.0012.35 (1.46–3.80)

ASCT, not performed

0.61 (0.26–1.41)

0.24

–

–

tBEC

LY6H expression, high1.86 (1.18–2.93)

0.006

1.98 (1.17–3.34)

0.010

tBEC

LOX expression, high2.07 (1.37–3.13)<0.0012.07 (1.31–3.26)

0.001

PTGIS expression, high1.63 (1.07–2.46)

0.020

1.11 (0.65–1.91)

0.68

TRC

TDO2 expression, high2.09 (1.19–3.76)

0.009

1.95 (1.05–3.62)

0.033

TRC

REM1 expression, high1.92 (1.25–2.95)

0.002

1.63 (1.01–2.64)

0.044

PIEZO2 expression, high2.03 (1.12–3.68)

0.019

1.57 (0.79–3.10)

0.19

CHI3L1 expression, high2.23 (1.35–3.70)

0.001

1.57 (0.91–2.71)

0.10

20

Overall survival (years)
LY6Hc

P value

IPI, high and intermediaten = 129n = 510Multivariate Cox regression

HR (95% CI)

Representative
NHC subcluster

REM1

P = 0.008
HR = 2.1

Univariate Cox regression

Variables

LOX

MECA-79 PLVAP LY6H MECA-79 PLVAP LY6H
Number of cells (per mm2)80*

MECA-79 PLVAP LOX

MECA-79 PLVAP LOX

80
Number of cells (per mm2)

Probability of overall
survival

LY6H
1.0

b

Low

60
40
20

FL

MFLN FL

MFLN

TDO2

20

FL

MFLN FL40*

FL

CCL19 REM1

CCL19 REM1

30

5
REM1+ area (%)

CCL19 TDO2

20
10
0

MFLN

40

REM1

Number of cells (per mm2)

CCL19 TDO2

60

0

0
MFLN

*

MFLN FL

MFLN

FL

*

4
3
2
1
0
MFLN FL

Fig. 7 | Identification of stroma-derived prognostic markers in FL. a, Kaplan–Meier curves showing the overall survival of patients newly diagnosed withFL (n = 180) based on the expression level of LY6H, LOX, TDO2 and REM1. Statistical analysis was performed using the two-sided log-rank test. HR, hazard
ratio. b, Univariate and multivariate Cox regression analyses predicting overall survival (n = 180). Statistical analysis was performed using two-sided Cox
proportional-hazards analysis. Significant gene expression in multivariate analysis is indicated by text shaded in red. Representative NHC subcluster
denotes subclusters in which indicated gene expression is most greatly upregulated in FL. CI, confidence interval. c, Left: images of IF staining for LY6H,
LOX, TDO2 and REM1 in representative MFLN and FL samples. Scale bars, 200 μm. Right: the box plots show the interquartile range (box limits), median(centre line), minimum to maximum values (whiskers), and biologically independent samples (circles) for quantification of cell number (for LY6H, LOX andTDO2) or area (for REM1) positive for each protein in MFLN and FL samples (MFLN, n = 8, 5, 4 and 6; FL, n = 7, 9, 4 and 4 for LY6H, LOX, TDO2 and REM1,
respectively). *P = 0.029 (LY6H), *P = 0.010 (LOX), *P = 0.029 (TDO2), *P = 0.038 (REM1) (two-sided Mann–Whitney U-test). Statistical source
data are provided.

CD70 is overexpressed in FL medullary and adventitial SCs by IF
staining (Fig. 6b,c). We next examined the gene and protein expression levels of the CD70 ligand CD27 in the B cells of FL samples.
Single-cell transcriptomic analysis of FL B cells showed that CD27
was significantly upregulated in malignant B cells compared with
non-malignant B cells (Extended Data Fig. 8e). Consistent with theseresults, flow cytometry analysis of FL haematopoietic cells showedthat the CD19+CD10+ cell population (malignant B-cell enrichedfraction) in 5 out of 8 (62.5%) biologically independent samples
was positive for CD27, and its expression was also significantly
Nature Cell Biology | www.nature.com/naturecellbiology

higher in the CD19+CD10+ population than in the CD19+CD10−
population (non-malignant B-cell fraction) (Extended Data
Fig. 8f,g). Among the five CD27+ FL samples, four (80.0%) showed
unequivocal binding to recombinant human CD70-Fc protein(Fig. 6d). The binding of malignant B-enriched cells to CD70-Fc
protein was significantly inhibited by the treatment of the cellswith an anti-CD27 function-blocking antibody in all four cases(Fig. 6d,e). Next, we performed ex vivo cell adhesion assays using
FL frozen sections and malignant B-enriched cells. The number ofmalignant B-enriched cells adhered to the medullary regions wasResource
significantly decreased following treatment with the anti-CD27
antibody (Fig. 6f,g).
Prognostic implications of stroma-derived markers in FL. Next,
we tested the applicability of our single-cell analysis of NHCs in the
search for clinically relevant factors. To correlate niche-specific or
subcluster-specific alterations in NHCs with survival of patientswith FL, we utilized a bulk microarray dataset of 180 FL biopsy
samples from newly diagnosed patients with available survival
information66.
We narrowed down multivariate analysis candidates to sevengenes (LY6H, LOX, PTGIS, TDO2, REM1, PIEZO2 and CHI3L1)
expressed at minimal levels in FL haematopoietic cells but at highlevels in FL BEC or NESC subclusters compared with MFLN counterparts. We hypothesized that they were probably associated withunfavourable prognosis (Fig. 7a and Extended Data Fig. 9a–e).
In the multivariate analysis, increased expression of the tip cellmarkers LY6H and LOX, as well as TDO2 and REM1, were associated with an unfavourable prognosis, even after adjustment for theinternational prognostic index67 (Fig. 7b).
For each of the four genes, we performed IF staining in MFLN
and FL samples. Cells expressing LY6H, LOX, TDO2 or REM1 wereincreased in FL compared with those in MFLNs (Fig. 7c).
Findings from the additional prognostic analyses are describedin the Supplementary Note, Extended Data Fig. 9f,g and
Supplementary Table 19.
Observation of NHC subclusters across lymphomas. Finally, we
examined whether our single-cell atlas was applicable to different
lymphoma subtypes. We also investigated a more aggressive FL stromal remodelling phenotype. To this end, we performed scRNA-seqof stroma-enriched cells from five nodal peripheral T-cell lymphoma (PTCL) samples and three diffuse large B-cell lymphomatransformed from FL (tDLBCL) samples (Fig. 1a, Extended Data
Fig. 1c and Supplementary Table 20). Detailed findings are describedin the Supplementary Note and Extended Data Fig. 10a–h. In brief,
NHC subclusters were detectable in PTCL and tDLBCL data andwe observed distinct alterations in tDLBCL stroma that probably
represented a terminal form of stromal remodelling in FL.

Discussion

Here we presented a human LNNHC map at single-cell resolutionthat was useful for exploring changes in lymphoma NHCs.
First, we shed light on differences in mouse and human LNNHC
heterogeneity. Overall, our findings suggest that human LNs
harbour unique NHC subpopulations that have not been detected
in murine LNs, which emphasizes the need for further human studies. As in previous studies that used fresh human LN samples22,26,
we used MFLNs from patients with tumours to construct the atlas.
Notably, a detailed analysis of LNNHCs from an individual with abenign tumour indicated that the clustering was comparable between
samples from the individual with a benign tumour and patientswith a malignant tumour (Supplementary Note). This observationsuggests minimal or negligible influence of malignancy-derived
factors on our atlas.
Second, multistep DEG analyses revealed subcluster-specificchanges in FL, including those with a previously unknown function
in lymphoma. We found that upregulation of some of the known
intercellular interactions across FL NHCs and malignant B cells
extended to unanticipated NHC subclusters and, conversely, otherinteractions were enhanced in limited NHC subclusters. Theseobservations largely increase the resolution of our understating ofstromal remodelling in lymphoma. Additionally, these findings may
be of clinical importance, as these may be considered as potential
stroma-derived prognostic factors. Notably, two tip-cell markers
were upregulated in FL and could serve as prognostic factors. LOX

NaTurE CELL BiOLOgy
enzymatic activity is reported to drive tumour angiogenesis by activating PDGFRβ signalling in vascular SMCs, which is consistent
with our DEG analysis that FL SMCs expressed PDGFRB at high levels68. Meanwhile, our observation of LY6H expression in tip cells has
not previously been described in mouse or human endothelial cells.
We also identified TDO2 as a prognostic predictor of FL. TDO2 mayfunction to attract regulatory T cells, antagonize CD8+ T-cell activity and accelerate myeloid cell tolerogenicity69. REM1 overexpressionin TRCs and PCs was also associated with unfavourable FL outcomes. Thus, further analysis of this gene, which has been scarcely
explored, and its relevance to the lymphoma stroma is warranted.
As the enrichment of the FL TRC signature per se was not prognostic (Supplementary Note), qualitative rather than quantitative
alterations in certain NHC subpopulations may affect the chemoresistance and prognosis of FL more precisely. In addition to these
prognostic factors, many upregulated genes with or without a known
pro-tumorigenic function were included in our dataset, which makes
our atlas a powerful discovery tool for additional therapeutic targets.
Third, we found that the CD70–CD27 interaction via
stroma-derived CD70 was enhanced in FL. Although the role ofCD70 has increasingly been investigated in the context of interplays
across various immune cells and cancer cells64,65, lymphoma SCs
have not been explored as a source of CD70. A recent report suggested that CD70 expressed by CAFs supports tumour progression
in solid cancers by facilitating cancer cell migration70. Consistent
with these findings, we confirmed binding between CD70 andmalignant B cells that could be blocked by an antagonist against
the CD70 ligand CD27. CD70 was upregulated in extrafollicular FL
SCs, which suggests that CD70 may facilitate the infiltration of lymphoma cells into extrafollicular regions during tumour progression.
Our analysis therefore proposes stroma-derived CD70 as a potential
biomarker and therapeutic target for FL.
Last, we found that NHC heterogeneities in LNs were detectableeven in aggressive lymphomas, thereby confirming the usefulnessof our NHC atlas to characterize the stroma of various lymphoma
subtypes. In particular, alterations in tDLBCL stroma harmonizedwith those in FL, thereby supporting the findings from the analysis of FL stroma. Furthermore, our findings indicate that extrafollicular SCs, including TRCs and medullary SCs, not only promote
extrafollicular infiltration of FL cells but simultaneously differentiate into FSCs and are finally replaced by FSCs in more advanced
phenotypes. This reflects a unique stromal transition that corresponds to the FSC-dependent growth of FL71.
Limitations of this study include the quantity of samples, which
may not be sufficient to identify all NHC subpopulations or to
precisely determine the correlation between the NHC heterogeneities in the transcriptome data and patient characteristics, such as
genomic alterations. Second, we cannot completely exclude the possibility that our atlas is influenced by unknown factors from a distant malignancy. Third, our study was not designed to analyse otherlymphoma subtypes or non-lymphoma diseases. Finally, further
functional validation is required to confirm our findings relevant toeach NHC subcluster.
In summary, our LNNHC atlas is of value to lymphoma researchers as it largely updates the NHC taxonomy in human LNs in thecontext of lymphoma. This study provides a platform for future
research that aims to deepen our understanding of LN or lymphoma
biology and to improve lymphoma management.

Online content

Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of
author contributions and competing interests; and statements ofdata and code availability are available at https://doi.org/10.1038/
s41556-022-00866-3.
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NaTurE CELL BiOLOgy
Received: 26 February 2021; Accepted: 10 February 2022;
Published: xx xx xxxx

References

1. Swerdlow, S. et al. WHO Classification of Tumours of Haematopoietic andLymphoid Tissues 4th edn (WHO Press, 2017).
2. Scott, D. W. & Gascoyne, R. D. The tumour microenvironment in B cell
lymphomas. Nat. Rev. Cancer 14, 517–534 (2014).
3. Höpken, U. E. & Rehm, A. Targeting the tumor microenvironment ofleukemia and lymphoma. Trends Cancer 5, 351–364 (2019).
4. De Palma, M., Biziato, D. & Petrova, T. V. Microenvironmental regulation oftumour angiogenesis. Nat. Rev. Cancer 17, 457–474 (2017).
5. Sahai, E. et al. A framework for advancing our understanding ofcancer-associated fibroblasts. Nat. Rev. Cancer 20, 174–186 (2020).
6. Ruan, J., Hajjar, K., Rafii, S. & Leonard, J. P. Angiogenesis and antiangiogenic
therapy in non-Hodgkin’s lymphoma. Ann. Oncol. 20, 413–424 (2009).
7. Lenz, G. et al. Stromal gene signatures in large-B-cell lymphomas.
N. Engl. J. Med. 359, 2313–2323 (2008).
8. Medina, D. J. et al. Mesenchymal stromal cells protect mantle cell lymphoma
cells from spontaneous and drug-induced apoptosis through secretion of
B-cell activating factor and activation of the canonical and non-canonical
nuclear factor κB pathways. Haematologica 97, 1255–1263 (2012).
9. Mourcin, F., Pangault, C., Amin-Ali, R., Amé-Thomas, P. & Tarte, K. Stromal
cell contribution to human follicular lymphoma pathogenesis. Front.
Immunol. 3, 280 (2012).
10. Valkenburg, K. C., de Groot, A. E. & Pienta, K. J. Targeting the tumour
stroma to improve cancer therapy. Nat. Rev. Clin. Oncol. 15, 366–381 (2018).
11. Carbone, A. et al. Follicular lymphoma. Nat. Rev. Dis. Primers 5, 83 (2019).
12. Guilloton, F. et al. Mesenchymal stromal cells orchestrate follicular lymphoma
cell niche through the CCL2-dependent recruitment and polarization of
monocytes. Blood 119, 2556–2567 (2012).
13. Husson, H. et al. CXCL13 (BCA-1) is produced by follicular lymphoma
cells: role in the accumulation of malignant B cells. Br. J. Haematol. 119,
492–495 (2002).
14. Tjin, E. P. et al. Functional analysis of HGF/MET signaling and aberrant
HGF-activator expression in diffuse large B-cell lymphoma. Blood 107,760–768 (2006).
15. Lwin, T. et al. Lymphoma cell adhesion-induced expression of B
cell-activating factor of the TNF family in bone marrow stromal cells
protects non-Hodgkin’s B lymphoma cells from apoptosis. Leukemia 23,
170–177 (2009).
16. Epron, G. et al. Monocytes and T cells cooperate to favor normal andfollicular lymphoma B-cell growth: role of IL-15 and CD40L signaling.
Leukemia 26, 139–148 (2012).
17. Link, A. et al. Fibroblastic reticular cells in lymph nodes regulate thehomeostasis of naive T cells. Nat. Immunol. 8, 1255–1265 (2007).
18. Krishnamurty, A. T. & Turley, S. J. Lymph node stromal cells: cartographersof the immune system. Nat. Immunol. 21, 369–380 (2020).
19. Chang, J. E. & Turley, S. J. Stromal infrastructure of the lymph node andcoordination of immunity. Trends Immunol. 36, 30–39 (2015).
20. Fletcher, A. L., Acton, S. E. & Knoblich, K. Lymph node fibroblastic reticular
cells in health and disease. Nat. Rev. Immunol. 15, 350–361 (2015).
21. Rodda, L. B. et al. Single-Cell RNA sequencing of lymph node stromal cells
reveals niche-associated heterogeneity. Immunity 48, 1014–1028.e16 (2018).
22. Takeda, A. et al. Single-cell survey of human lymphatics unveils marked
endothelial cell heterogeneity and mechanisms of homing for neutrophils.
Immunity 51, 561–572.e5 (2019).
23. Xiang, M. et al. A single-cell transcriptional roadmap of the mouse and human
lymph node lymphatic vasculature. Front. Cardiovasc. Med. 7, 52 (2020).
24. Fujimoto, N. et al. Single-cell mapping reveals new markers and functions oflymphatic endothelial cells in lymph nodes. PLoS Biol. 18, e3000704 (2020).
25. Brulois, K. et al. A molecular map of murine lymph node blood vascularendothelium at single cell resolution. Nat. Commun. 11, 3798 (2020).
26. Kapoor, V. N. et al. Gremlin 1+ fibroblastic niche maintains dendritic cellhomeostasis in lymphoid tissues. Nat. Immunol. 22, 571–585 (2021).
27. Lambrechts, D. et al. Phenotype molding of stromal cells in the lung tumor
microenvironment. Nat. Med. 24, 1277–1289 (2018).
28. Goveia, J. et al. An integrated gene expression landscape profiling approach toidentify lung tumor endothelial cell heterogeneity and angiogenic candidates.
Cancer Cell 37, 21–36.e13 (2020).
29. Fang, J. S. et al. Shear-induced Notch–Cx37–p27 axis arrests endothelial cell
cycle to enable arterial specification. Nat. Commun. 8, 2149 (2017).
30. Fleming, R. E. et al. Carbonic anhydrase IV expression in rat andhuman gastrointestinal tract regional, cellular, and subcellular localization.
J. Clin. Invest. 96, 2907–2913 (1995).
31. Thiriot, A. et al. Differential DARC/ACKR1 expression distinguishes
venular from non-venular endothelial cells in murine tissues. BMC Biol. 15,
45 (2017).
Nature Cell Biology | www.nature.com/naturecellbiology

Resource
32. Kalucka, J. et al. Single-cell transcriptome atlas of murine endothelial cells.
Cell 180, 764–779.e20 (2020).
33. Heckmann, B. L., Zhang, X., Xie, X. & Liu, J. The G0/G1 switch gene 2(G0S2): regulating metabolism and beyond. Biochim. Biophys. Acta 1831,
276–281 (2013).
34. Veerman, K., Tardiveau, C., Martins, F., Coudert, J. & Girard, J. P. Single-cell
analysis reveals heterogeneity of high endothelial venules and different
regulation of genes controlling lymphocyte entry to lymph nodes. Cell Rep.
26, 3116–3131.e5 (2019).
35. Bondareva, O. et al. Identification of atheroprone shear stressresponsive regulatory elements in endothelial cells. Cardiovasc. Res. 115,
1487–1499 (2019).
36. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are
revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32,
381–386 (2014).
37. Phng, L. K. & Gerhardt, H. Angiogenesis: a team effort coordinated by Notch.
Dev. Cell 16, 196–208 (2009).
38. Cheng, H. W. et al. Origin and differentiation trajectories of fibroblastic
reticular cells in the splenic white pulp. Nat. Commun. 10, 1739 (2019).
39. Naba, A. et al. The matrisome: in silico definition and in vivo characterizationby proteomics of normal and tumor extracellular matrices. Mol. Cell.
Proteom. 11, M111.014647 (2012).
40. Severino, P. et al. Human lymph node-derived fibroblastic anddouble-negative reticular cells alter their chemokines and cytokines
expression profile following inflammatory stimuli. Front. Immunol. 8,141 (2017).
41. Phillips, R. & Ager, A. Activation of pertussis toxin-sensitive CXCL12(SDF-1) receptors mediates transendothelial migration of T lymphocytes
across lymph node high endothelial cells. Eur. J. Immunol. 32,
837–847 (2002).
42. Xu, Q., Schett, G., Li, C., Hu, Y. & Wick, G. Mechanical stress-induced heatshock protein 70 expression in vascular smooth muscle cells is regulated by
Rac and Ras small G proteins but not mitogen-activated protein kinases. Circ.
Res. 86, 1122–1128 (2000).
43. González-González, L. & Alonso, J. Periostin: a matricellular protein withmultiple functions in cancer development and progression. Front. Oncol. 8,225 (2018).
44. Noh, K. et al. Differential effects of EGFL6 on tumor versus woundangiogenesis. Cell Rep. 21, 2785–2795 (2017).
45. An, J. et al. EGFL6 promotes breast cancer by simultaneously enhancing
cancer cell metastasis and stimulating tumor angiogenesis. Oncogene 38,
2123–2134 (2019).
46. Cremasco, V. et al. FAP delineates heterogeneous and functionally divergent
stromal cells in immune-excluded breast tumors. Cancer Immunol. Res. 6,1472–1485 (2018).
47. Costa, A. et al. Fibroblast heterogeneity and immunosuppressive environment
in human breast cancer. Cancer Cell 33, 463–479.e10 (2018).
48. Kieffer, Y. et al. Single-cell analysis reveals fibroblast clusters linked
to immunotherapy resistance in cancer. Cancer Discov. 10,
1330–1351 (2020).
49. Ley, K. The role of selectins in inflammation and disease. Trends Mol. Med. 9,263–268 (2003).
50. Silva, M., Videira, P. A. & Sackstein, R. E-selectin ligands in the humanmononuclear phagocyte system: implications for infection, inflammation, andimmunotherapy. Front. Immunol. 8, 1878 (2017).
51. Farr, L., Ghosh, S. & Moonah, S. Role of MIF cytokine/CD74 receptor
pathway in protecting against injury and promoting repair. Front. Immunol.
11, 1273 (2020).
52. Vento-Tormo, R. et al. Single-cell reconstruction of the early maternal–fetal
interface in humans. Nature 563, 347–353 (2018).
53. Cao, Z. et al. Angiocrine factors deployed by tumor vascular niche induce B
cell lymphoma invasiveness and chemoresistance. Cancer Cell 25,
350–365 (2014).
54. Wallach-Dayan, S. B. et al. CD44-dependent lymphoma cell dissemination:a cell surface CD44 variant, rather than standard CD44, supports
in vitro lymphoma cell rolling on hyaluronic acid substrate and its
in vivo accumulation in the peripheral lymph nodes. J. Cell Sci. 114,
3463–3477 (2001).
55. Higashi, M. et al. CD44 expression during tumor progression of follicularlymphoma. Oncol. Rep. 22, 1135–1140 (2009).
56. Drillenburg, P. & Pals, S. T. Cell adhesion receptors in lymphoma
dissemination. Blood 95, 1900–1910 (2000).
57. Ashkenazi, A. Targeting death and decoy receptors of the tumour-necrosis
factor superfamily. Nat. Rev. Cancer 2, 420–430 (2002).
58. Pandey, S. et al. IL-4/CXCL12 loop is a key regulator of lymphoid stroma
function in follicular lymphoma. Blood 129, 2507–2518 (2017).
59. Novak, A. J. et al. Genetic variation in B-cell-activating factor is associatedwith an increased risk of developing B-cell non-Hodgkin lymphoma. Cancer
Res. 69, 4217–4224 (2009).

Resource
60. Gerli, M. F. M. et al. Combined Notch and PDGF signaling enhances
migration and expression of stem cell markers while inducing perivascularcell features in muscle satellite cells. Stem Cell Rep. 12, 461–473 (2019).
61. Rehm, A. et al. Cooperative function of CCR7 and lymphotoxin in theformation of a lymphoma-permissive niche within murine secondary
lymphoid organs. Blood 118, 1020–1033 (2011).
62. Jacobs, J. et al. CD70: an emerging target in cancer immunotherapy.
Pharmacol. Ther. 155, 1–10 (2015).
63. Starzer, A. M. & Berghoff, A. S. New emerging targets in cancer
immunotherapy: CD27 (TNFRSF7). ESMO Open 4, e000629 (2020).
64. Yang, Z. Z. et al. TGF-β upregulates CD70 expression and induces exhaustionof effector memory T cells in B-cell non-Hodgkin’s lymphoma. Leukemia 28,
1872–1884 (2014).
65. Al Sayed, M. F. et al. CD70 reverse signaling enhances NK cell function andimmunosurveillance in CD27-expressing B-cell malignancies. Blood 130,
297–309 (2017).
66. Leich, E. et al. Follicular lymphomas with and without translocation t(14;18)
differ in gene expression profiles and genetic alterations. Blood 114,
826–834 (2009).
67. International Non-Hodgkin’s Lymphoma Prognostic Factors Project. A
predictive model for aggressive non-Hodgkin’s lymphoma. N. Engl. J. Med.
329, 987–994 (1993).
68. Baker, A. M. et al. Lysyl oxidase plays a critical role in endothelial cellstimulation to drive tumor angiogenesis. Cancer Res. 73, 583–594 (2013).

NaTurE CELL BiOLOgy
69. Opitz, C. A. et al. The therapeutic potential of targeting
tryptophan catabolism in cancer. Br. J. Cancer 122, 30–44(2020).
70. Jacobs, J. et al. Unveiling a CD70-positive subset of cancer-associatedfibroblasts marked by pro-migratory activity and thriving regulatory T cell
accumulation. Oncoimmunology 7, e1440167 (2018).
71. Béguelin, W. et al. Mutant EZH2 induces a pre-malignant lymphoma
niche by reprogramming the immune response. Cancer Cell 37,
655–673.e11 (2020).
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Methods

Human samples. This study was approved by the Ethics Committee of the
University of Tsukuba Hospital and the review boards of associated institutions
that provided human samples (Kameda Medical Center, NTT Medical CenterTokyo and Mito Medical Center) and conducted according to all relevant ethicalregulations regarding human participants. Written informed consent was obtained
from all participants. The participants were not compensated for taking part in the
study. For scRNA-seq, MFLN samples were prospectively collected from patientswith a neoplasm (n = 9) who had undergone surgical LN dissection between
January and June 2020. Non-sentinel LNs without enlargement (<1 cm) were used.
The collected LNs were verified as malignancy-free via flow cytometry analysis
of pan-cytokeratin negativity (Extended Data Fig. 1b). Nodal FL (n = 10), PTCL
(n = 5) and tDLBCL (n = 3) samples were also prospectively collected between
August 2019 and May 2020. Furthermore, for functional experiments, additional
nodal FL samples (n = 8) were collected between May 2020 and August 2021.
Lymphoma diagnosis of tissue specimens was made pathologically, phenotypically
and/or referring to results of cytogenetic examinations, including fluorescencein situ hybridization analysis by expert haematopathologists.
Single-cell isolation of LNNHCs. After collection, LN or lymphoma samples
were immediately minced and digested for 1 h with RPMI 1640 medium(Sigma-Aldrich, R8758) with 5% fetal bovine serum (FBS) containing 0.2 mg ml–1
collagenase P (Sigma-Aldrich, 11213857001), 0.8 mg ml–1 dispase (Gibco,
17105041) and 0.1 mg ml–1 DNase I (Worthington, LS002139), with continuous
agitation. Cells were then filtered through a 70-μm mesh, and red blood cells werelysed in 1% ammonium-chloride-potassium buffer. Thereafter, haematopoietic
cells and contaminated red blood cells were depleted using human CD45 (130045-801) and CD235a (130-050-501) microbeads according to the manufacturer’s
instructions (Miltenyi Biotec). For MFLN samples, the remaining single-cellsuspension was incubated with phycoerythrin (PE)-anti-CD45 (BioLegend;
1:500) in combination with Alexa Fluor 488-pan-cytokeratin (ThermoFisher
Scientific; 1:500), allophycocyanin (APC)-anti-podoplanin (BioLegend; 1:500)
and PE-cyanin 7 (PE-Cy7)-anti-CD31 (BioLegend; 1:500). For lymphoma samples,PE-anti-CD45 was mixed with fluorescein isothiocyanate (FITC)-anti-CD31(BioLegend; 1:500), APC-anti-podoplanin (BioLegend; 1:500) and
PE-Cy7-anti-CD34 (BioLegend; 1:500). The samples were incubated for 20 min,then 7-AAD viability staining solution (ThermoFisher Scientific, 00-6993-50;
1:1,000) was added and incubated for 10 min in the dark on ice. CD45– live
cells were sorted using a FACSAria II or III (BD Bioscience) after removing
doublets by gating with a FSC-H versus FCS-W plot and a SSC-H versus
SSC-W plot. Flow cytometry data were analysed using FlowJo software (Tree Star,
v.10.7.1). CD45+ cells were cryopreserved in FBS plus 10% dimethylsulfoxide inliquid nitrogen.
Library preparation, sequencing and data pre-processing. Sorted CD45– cells
were converted to barcoded scRNA-seq libraries using Chromium Single Cell 3′
reagent kits (V3) (10X Genomics) according to the manufacturer’s instructions(CG000183 Rev A), aiming for 5,000–8,000 cells per library. Library quality control
and quantification were performed using a KAPA Library Quantification kit for
Illumina platforms (Kapa Biosystems, KK4873) and a 2100 Bioanalyzer HighSensitivity DNA kit (Agilent, 5067-4626). Libraries were sequenced on an Illumina
HiSeq X Ten system with an average depth of 31,439 reads per cell, then mapped tothe human genome (build GRCh38) and demultiplexed using CellRanger pipelines(10X Genomics, v.3.1.0).
Data processing and cell clustering of individual cases. Pre-processed datafrom each sample were further processed and analysed individually using the R
package Seurat (v.3.2.2) on RStudio (v.3.5.0 or v.4.0.2). After removing ribosomal
genes, genes expressed in fewer than 3 cells and cells expressing fewer than 200
genes, we filtered out cells with fewer than 200 unique feature counts (low-quality
cells). Cells with unique feature counts greater than three times the median value(possible doublets) and/or cells with more than twice the median number ofmitochondrial genes (possible apoptotic or lysed cells) were also removed. We thennormalized data using the NormalizeData function and extracted highly variablefeatures using the FindVariableFeatures function. Normalized data underwent a
linear transformation (scaling) and principal component analysis (PCA) basedon variable features using the RunPCA function. Graph-based clustering wasthen performed according to gene expression profiles using the FindNeighbors
and FindClusters functions with default parameters, and results were visualizedusing a nonlinear dimensional reduction UMAP technique running RunUMAP
and DimPlot functions. Cell clusters were annotated based on the expression of
canonical markers, including PECAM1 and JAM2 for BECs, PECAM1 and PROX1
for LECs, ACTA2 for SMCs, CCL19 and CCL21 for TRCs, CR2 for FDCs, DCN
for other NESCs, PTPRC for contaminating lymphocytes, SDC1 for plasma cells,and CCR7 and CD83 (in cells weakly PTPRC+) for dendritic cells22,72,73. MKI67
and TOP2A expression levels were used to identify clusters of an aggressively
proliferative nature. For the FL sample 3, which came from a patient withintra-submandibular gland FL, any distinct clusters negative for all canonicalmarkers and positive for keratin genes (indicating glandular tissue contamination)
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were removed. All other cases were confirmed to consist solely of these majorclusters. We confirmed a negligible presence of ambient RNA contamination insingle-cell NHC data, and found an imperceptible influence of potential RNA
contamination on clustering results in all LN and lymphoma samples by using the
DecontX (in the celda package, v.1.6.1) and SoupX (v.1.5.2) packages (datanot shown)74,75.
Data integration with batch effect collection. We performed canonicalcorrelation analysis76 to identify shared sources of variation across multiple datasets
using the FindIntegrationAnchors function and integrated them using anchorsfrom the IntegrateData function with canonical correlation dimensions of 20.
Integrated data were scaled and underwent PCA as performed inindividual datasets.
Supervised annotation and unsupervised clustering of LNNHCs. We performedgraph-based clustering of PCA-reduced integrated data and supervised annotation,
as described in ‘Data processing and cell clustering of individual cases’ above.
Clusters characterized by extremely low unique feature counts (low-quality cells)
were removed.
Next, we extracted the three major NHC components (BECs, LECs and
NESCs) in silico and performed scaling, PCA-based dimensional reduction
and unsupervised graph-based subclustering of each component. We removed
subclusters that were considered possible doublets as characterized by highexpressions of marker genes for different NHC components and incongruously
high unique feature counts. In BEC subclustering, we also performed supervisedannotation for the identification of arterial, capillary and venous BECs using
canonical markers for each BEC component29–32.
DEG analysis. DEG analysis was performed using the FindMarkers or
FindAllMarkers functions with a minimum of 20% of the gene-expressing
cells and a minimum log fold-change of 0.25 in gene expression between
each cluster and other clusters. We primarily used the Wilcoxon rank-sum test for
DEG detection. To confirm detected DEGs, we also used the model-based
analysis of single-cell transcriptomics (MAST) method77. DEGs were definedas genes confirmed to show an adjusted P value (based on the Bonferroni
correction) of <0.05 by using both methods. Results of the Wilcoxon rank-sum
test were used to construct DEG lists and volcano plots. Volcano plots werecreated using the R package EnhancedVolcano (v.1.8.0). DEG analysis to compare
corresponding clusters between mLN and pLN samples and between MFLN
and FL samples was performed in a similar manner using the cut-off parametersdescribed above.
For DEG analyses between MFLN and FL NHC subclusters, we adopted amultistep approach. Several previous studies had indicated differences in gene andprotein expression between mLNs and pLNs78–81. Therefore, we initially profiled
DEGs between mLNs and pLNs among MFLNs at subcluster levels (Supplementary
Table 13 and Supplementary Note). Referring to this profile, we identified DEGs
upregulated in FL by removing those detected between mLNs and pLNs. We also
performed DEG analysis between MFLN and FL NHC subclusters using onlypLN samples (MFLN 7–MFLN 9 and FL 2–FL 10) to support the reliability of thedetected DEGs.
GO enrichment analysis of DEGs in particular clusters was performed using
Metascape (http://metascape.org)82.
Trajectory analysis. We performed trajectory analysis using the Monocle 3
package (v.0.2.3)36 in RStudio on integrated BEC, NESC and LEC data constructedusing Seurat. Data pre-processing was performed using the preprocess_cds
function, with the number of dimensions set at 100. Dimensionality reduction
and clustering were performed using the reduce_dimension and cluster_cells
functions, respectively. We then fit a principal graph within each cluster using thelearn_graph function and visualized the order of cells in pseudotime by plot_cells
or plot_cells_3d functions, as appropriate with the pseudotime colouring option.
Single-cell analysis of FL haematopoietic cells. We performed single-cell analysis
of cryopreserved CD45+ cells from nine FL samples (FL 2–FL 10). After thawing,cell suspensions were filtered through a 70-μm mesh and incubated with 7-AAD
viability staining solution for 10 min in the dark. The 7-AAD– live cells were
sorted using a FACSAria II or III after removing doublets, then were converted tobarcoded scRNA-seq libraries, as performed for CD45– cells. Library preparation,
sequencing and data processing were performed as for CD45– cells. Data quality
control, processing and graph-based clustering were performed in each individual
case using the Seurat package, with dimension and resolution parameters of 50
and 0.5, respectively. Thereafter, we identified malignant B-cell populations bydetecting restrictions of light chain kappa/lambda genes, as suggested by previous
studies83,84. In brief, we projected the B-cell marker CD79A and the light chain
genes IGKC (for light chain kappa) and IGLC2 (for light chain lambda) to cellclusters on the UMAP plot of each sample (Extended Data Fig. 8a). We thencalculated the ratio of cells expressing IGLC2 and IGKC with expression levels of>1 and >2, respectively, in each B-cell cluster. We defined B-cell clusters with aratio of >2.0 or <0.25 as malignant (Extended Data Fig. 8b).

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Malignant B-cell signature analysis in FL B cells. To support the reliability ofmalignant B-cell detection, we performed signature analysis on data from FL
B cells. We developed a gene set that represents a malignant B-cell signature
based on the recent single-cell analysis of FL B cells reported by Andor et al.83.
We carefully selected genes that were described as significantly upregulated
in malignant compared to non-malignant B cells in a uniform manner among
different FL samples83. Selected genes are listed in Supplementary Table 21. A
malignant B-cell signature score was calculated in B cells of all nine FL samples
using the GSVA package (v.1.38.2)85 and depicted using the FeaturePlot and
VlnPlot functions of Seurat.
Intercellular ligand–receptor interaction analysis. We investigated interactions
between NHC subclusters and malignant B cells of nine FL samples (FL 2–FL 10)
using the CellPhoneDB package (v.2.1.1)52 on Python (v.3.6). Gene expression
information relevant to each NHC subcluster in integrated FL NHCs was used
for NHC data, whereas gene expression information relevant to malignant B-cellclusters in each FL sample was separately used for malignant B-cell data, as gene
expression profiles of malignant B cells vary greatly among samples. We thenperformed pairwise comparisons between NHC subclusters and malignant B-cellclusters. In brief, we derived potential ligand–receptor interactions based on theexpression of a receptor gene by one lineage subpopulation and a ligand gene byanother. We filtered genes expressed in >20% of cells in any given subpopulation.
We then permuted the cluster labels of all input cells 1,000 times and calculated themean interaction score (the average receptor expression level in a subpopulation
multiplied by the average ligand expression level in the interacting subpopulation),
which generated a null distribution of the mean interaction score for each ligand–receptor pair in each pairwise comparison across subpopulations. Thereafter,
we located observed mean interaction scores that were the same or higher than
the actual mean score in the null distribution and calculated the proportion of
the observed scores, conferring a P value for the likelihood of specificity of agiven ligand–receptor complex to a given cluster pair. To consider interactions
between FL NHCs and FL malignant B cells, we selected only interactions with a
P value of <0.05 in more than half of FL cases (>4 cases). Furthermore, to assess
subcluster-specific lymphomagenesis mechanisms in FL stroma, we extracted
interactions that included a molecule in which gene expression was significantly
upregulated in at least one FL NHC subcluster compared with that in thecorresponding MFLN subcluster. We integrated interaction scores and P values ofinteractions between pairs consisting of the same NHC subcluster and malignant
B-cell clusters from different FL samples, as previously described84. In brief, we
calculated mean interaction scores for pairs that included the same NHC subcluster
and malignant B-cell clusters from different FL samples, then normalized the meaninteraction scores per interaction. We also combined P values of interactions forpairs that consisted of the same NHC subcluster and malignant B-cell clusters
from different FL samples using Fisher’s method. The P values were correctedusing the Benjamini–Hochberg method. In Fig. 6a, circles are coloured when gene
expression for the indicated stroma-derived factor is upregulated in relevant FL
subclusters compared to that in the MFLN counterparts (log fold-change >0 andadjusted P value <0.05).
IF staining. Human LN and lymphoma samples were immediately embedded in
OCT compound (Sakura Finetek Japan, 45833) and frozen in hexane cooled withdry ice. Samples were sliced to 3-μm thickness with a cryostat at −20 °C. Sections
were dried for 1 h at 20 °C, fixed for 10 min in 4% paraformaldehyde, incubatedfor 10 min with 0.1% Triton X-100 (Sigma-Aldrich, T9284) for permeabilization,and then treated with 10% goat serum (Sigma-Aldrich, G9023) in PBS orserum-free protein blocking buffer (Dako, X0909) (when using non-goat-derived
secondary antibodies) for 30 min. Sections were stained overnight at 4 °C withprimary antibodies listed in Supplementary Table 22. After several washes
with tris-buffered saline with tween 20 (Sigma-Aldrich, P9416), sections were
stained for 1 h with combinations of the following secondary antibodies at 20 °C:
AF488-goat-anti-rat IgG (ThermoFisher Scientific), AF594-goat-anti-rabbit IgG(ThermoFisher Scientific), AF594-donkey-anti-goat IgG (ThermoFisher Scientific)
and AF647-goat-anti-mouse IgG (ThermoFisher Scientific). A TrueVIEW
Autofluorescence Quenching kit (Vector, SP-8500) was used to decrease possible
tissue autofluorescence per the manufacturer’s instructions. Sections were thenmounted in mounting medium with 4,6-diamidino-2-phenylindole (DAPI; Vector,
H-1200). Stained samples were imaged using a Leica DMi8 S Platform with theThunder imaging system (3D Live Cell & 3D Cell Culture & 3D assay). Analysed
LNs were verified as malignancy-free by pan-cytokeratin staining. Quantitative
analysis of acquired images was performed using ImageJ software (National
Institute of Health, v.2.1.0). As LNs and FL carry localized structures, we randomly
acquired at least five different regions of interest within each sample and used the
median values for statistical analysis.
Flow cytometry analysis of FL haematopoietic cells. To analyse the expressionof CD27 in malignant FL B cells and to perform the binding/adhesion assays
described below, we used additionally collected cryopreserved FL samples(FL 11–FL 18). Clinical characteristics of patients in the additional FL cohort
are described in Supplementary Table 23. After thawing, cells were filtered

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through a 70-μm mesh and incubated with PE-anti-CD27 (BioLegend; 1:500),
FITC-anti-CD3 (BioLegend; 1:500), APC-anti-CD19 (Miltenyi Biotec; 1:500) and
PE-Cy7-anti-CD10 (BioLegend; 1:500) antibodies for 20 min on ice. Cells werethen incubated with 7-AAD viability staining solution for 10 min in the dark andanalysed using a FACSAria II or III and FlowJo software.
Recombinant protein binding assay. Recombinant Fc chimera CD70
(SinoBiological, 10780-H01H) or human IgG (R&D systems, 1-001-A) was
incubated with a single-cell suspension of FL haematopoietic cells for 10 min at
4 °C in RPMI with 10% FCS. To block CD70–CD27 binding, cells were incubatedin the presence of anti-CD27 blocking antibody (R&D systems, MAB382) or
isotype mouse IgG1 (R&D systems, MAB002) for 30 min at 4 °C before binding.
After binding, the cells were washed, fixed using 4% paraformaldehyde for10 min at 20 °C, incubated with PE-anti-human IgG Fc (R&D systems; 1:500),
FITC-anti-CD3 (1:500), APC-anti-CD19 (1:500) and PE-Cy7-anti-CD10 (1:500)
for 20 min at 4 °C, and analysed using flow cytometry (FACSAria II or III) and
FlowJo software.
Ex vivo cell adhesion assay. Frozen FL sections were sliced at 6-μm thickness
immediately before the assay. For malignant B-cell isolation, we used FL samples
in which >90% B cells were confirmed to be malignant by flow cytometry. B cells
were isolated from the FL haematopoietic cell suspension using an EasySep Release
Human CD19 Positive Selection kit (StemCell Technologies, ST-17754). Cells werethen treated with anti-CD27 blocking antibody or isotype mouse IgG1 for 30 min
at 4 °C. Thereafter, 2 × 106 cells were applied on the sections and incubated with
60 r.p.m. rotation for 5 min, followed by incubation without rotation for 15 min.
The incubation with and without rotation was repeated two more times. Afterincubation, the sections were gently washed with PBS, sealed with a cover glass
and imaged using a Keyence BZ-X710 microscope (Keyence). Adherent cells weremanually counted using ImageJ.
Prognostic analysis of stroma-derived markers in FL. To analyse the prognostic
potential of gene expression patterns of NHCs in patients with FL, we used a bulk
microarray dataset of 180 FL biopsy samples from independent, newly diagnosed
cases66. To narrow candidates to stroma-specific genes, we initially selected DEGs
upregulated in FL BEC and NESC subclusters relative to MFLN counterparts. These
were narrowed down to those showing a log fold-change of >0.5 and < 0.1% of cellswith an expression level higher than 0 in FL haematopoietic cells (Extended Data
Fig. 9a). We did not use genes upregulated in FL LEC subclusters, as the proportionof FL LECs was considerably decreased relative to MFLN LECs and the specificity ofthese genes to FL stroma was considered unlikely in analyses of bulk tissues. Next,
we tested the expression of all candidate genes using the Kaplan–Meier method
and two-sided log-rank test. Cut-off expression values of each gene for the Kaplan–
Meier survival curves was determined using maximally selected rank statistics86.
As many putative stroma-specific genes were upregulated in FL, it was possiblethat P value collection (for example, the Bonferroni method) greatly reduced thenumber of candidate genes, considering that the sample size in the dataset was notparticularly large. Therefore, we extracted genes with reliable prognostic impacts
using another approach (Extended Data Fig. 9a,c). We initially divided patients into
three groups according to survival outcomes: a favourable group, which comprised
patients alive 10 years after diagnosis; an unfavourable group, which comprised
patients who died within 5 years of diagnosis; and an intermediate/indefinitegroup, which comprised the remaining patients (Extended Data Fig. 9c). We thencompared the proportion of patients with higher expression of each candidate gene
between favourable and unfavourable groups. Genes were considered prognostic
when the proportion was significantly higher in the unfavourable group comparedwith that in the favourable group (Extended Data Fig. 9d). These prognostic genes
were further subjected to multivariate analysis (Extended Data Fig. 9a).
To evaluate the prognostic efficiency of the FL TRC signature, we extracted
the DEGs that were upregulated in FL TRCs in comparison to MFLN TRCs
(Supplementary Table 17). We considered the DEGs with an expression level
higher than 0 in <0.1% FL haematopoietic cells, <10% FL BECs and <10% FL
LECs and were detectable in the microarray dataset66. A total of 11 extracted genes
constituting the FL TRC signature are listed in Supplementary Table 19.
Whole-exome sequencing. Whole-exome sequencing was performed on genomic
DNA extracted from nine FL samples (FL 2–FL 10). Libraries were prepared using
SureSelect Human All Exon v.7 kits (Agilent Technologies, 5191-4004) according
to the manufacturer’s instructions and sequenced using an Illumina HiSeq X
Ten system with a 150-bp paired-end protocol. We used the Genomon2 pipeline(v.2.6.2) for alignment of sequence and mutation calling. Somatic mutations witha Fisher’s exact P value of <0.01 and an empirical Bayesian call P value of <0.0001
were adopted. Thereafter, mutations of synonymous single nucleotide variants,
variants only in unidirectional reads, variants in intergenic, intronic, untranslated
regions and noncoding RNA regions, and variants in repetitive genomic regions
were excluded. Furthermore, known genetic alterations affecting at least 10%of FL11 were screened for additional mutations. Finally, mutations derived frommapping errors were excluded using Integrative Genomics Viewer. Detected
somatic mutations are listed in Supplementary Table 2.
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Statistics and reproducibility. Statistical analysis was performed using R on
RStudio or GraphPad Prism 9 (GraphPad, v.9.2.0). A two-sided P value of <0.05
was considered statistically significant.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.

Data availability

The scRNA-seq data that support the findings of this study have been depositedat the European Genome-Phenome Archive (https://ega-archive.org) databaseand can be retrieved using the accession number EGAD00001008311. For
survival analysis, a DNA microarray dataset from Leich et al.66 was downloaded
from the Gene Expression Omnibus (GEO) (accession number: GSE16131). For
mapping of scRNA-seq data, GRCh38 (https://www.ncbi.nlm.nih.gov/assembly/
GCF_000001405.39) was used. All other data are available from the corresponding
authors on reasonable request. Source data are provided with this paper.

Code availability

The codes for key computational analyses are available on GitHub at https://github.
com/yoshiakiabe1018/Stroma01. All of the packages used are available online.

References

72. Zhao, Q. et al. Single-cell transcriptome analyses reveal endothelial cell
heterogeneity in tumors and changes following antiangiogenic treatment.
Cancer Res. 78, 2370–2382 (2018).
73. Baryawno, N. et al. A cellular taxonomy of the bone marrow stroma in
homeostasis and leukemia. Cell 177, 1915–1932.e16 (2019).
74. Yang, S. et al. Decontamination of ambient RNA in single-cell RNA-seq with
DecontX. Genome Biol. 21, 57 (2020).
75. Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination fromdroplet-based single-cell RNA sequencing data. GigaScience https://doi.
org/10.1093/gigascience/giaa151 (2020).
76. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating
single-cell transcriptomic data across different conditions, technologies, andspecies. Nat. Biotechnol. 36, 411–420 (2018).
77. Finak, G. et al. MAST: a flexible statistical framework for assessing
transcriptional changes and characterizing heterogeneity in single-cell RNA
sequencing data. Genome Biol. 16, 278 (2015).
78. Streeter, P. R., Berg, E. L., Rouse, B. T., Bargatze, R. F. & Butcher, E. C. A
tissue-specific endothelial cell molecule involved in lymphocyte homing.
Nature 331, 41–46 (1988).
79. Berlin, C. et al. α4β7 integrin mediates lymphocyte binding to the mucosal
vascular addressin MAdCAM-1. Cell 74, 185–195 (1993).
80. von Andrian, U. H. & Mempel, T. R. Homing and cellular traffic in lymph
nodes. Nat. Rev. Immunol. 3, 867–878 (2003).
81. Lee, M. et al. Transcriptional programs of lymphoid tissue capillary and highendothelium reveal control mechanisms for lymphocyte homing. Nat.
Immunol. 15, 982–995 (2014).

Nature Cell Biology | www.nature.com/naturecellbiology

82. Zhou, Y. et al. Metascape provides a biologist-oriented resource for the
analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).
83. Andor, N. et al. Single-cell RNA-seq of follicular lymphoma reveals malignant
B-cell types and coexpression of T-cell immune checkpoints. Blood 133,
1119–1129 (2019).
84. Roider, T. et al. Dissecting intratumour heterogeneity of nodal B-cell
lymphomas at the transcriptional, genetic and drug-response levels. Nat. Cell
Biol. 22, 896–906 (2020).
85. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis
for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).
86. Hothorn, T. & Zeileis, A. Generalized maximally selected statistics. Biometrics
64, 1263–1269 (2008).

Acknowledgements

We thank our staff at associated institutions and departments for cooperation incollections of the human samples; E. Matsuzawa and Y. Sakashita for technicalassistance; and A. Suzuki and Y. Suzuki (the University of Tokyo) for their support
in the computational analysis of scRNA-seq data. Finally, we acknowledge E. Lamarfor outstanding editorial assistance. This work was supported by Grants-in-Aid for
Scientific Research (KAKENHI: JP20J20851 to Y.A., JP21H02945 to M.S.-Y., andJP19H03683 to S.C.) from the Ministry of Education, Culture, Sports, and Science ofJapan; AMED under grant numbers JP21ck0106544 and JP21ck0106644 (to M.S.-Y.)and JP21cm0106505 (to S.C.); and Okinaka Memorial Institute for Medical Research,
Foundation for Promotion of Cancer Research, and Takeda Science Foundation (toM.S.-Y.). The funders had no role in the study design, data collection and analysis,
decision to publish or preparation of the manuscript.

Author contributions

Y.A. collected the human samples, performed all experiments and computational
analyses, and generated all figures and tables. M.S.-Y. and M.F. developed experimental
and analytical scRNA-seq systems and supported scRNA-seq experiments and analyses.
H.M., T.B.N. and K.O. assisted in the immunostaining experiments. Y.S., K.H., M.K., T.S.,
H.N., Y.O., T.E., A.S., H.B., C.Y., R.T., T.T., M.N., K.U., T.O. and K.M. aided in humansample collection. M.S.-Y. conceived the study. Y.A., M.S.-Y. and S.C. designed the
project and wrote the manuscript.

Competing interests

The authors declare no competing interests.

Additional information

Extended data is available for this paper at https://doi.org/10.1038/s41556-022-00866-3.
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41556-022-00866-3.
Correspondence and requests for materials should be addressed to
Mamiko Sakata-Yanagimoto or Shigeru Chiba.
Peer review information Nature Cell Biology thanks the anonymous reviewers for theircontribution to the peer review of this work. Peer reviewer reports are available.
Reprints and permissions information is available at www.nature.com/reprints.

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Extended Data Fig. 1 | Single-cell analysis of LN and lymphoma NHCs. a, Macroscopy of representative human metastasis-free lymph node (MFLN) (top)and follicular lymphoma (FL) (bottom) samples. Scale bars, 1 cm. b, Gating strategy for the isolation and analysis of non-haematopoietic cells (NHCs) inhuman LN and lymphoma in flow cytometry (see ‘Single-cell isolation of LNNHCs’ in the Methods section). BEC, blood endothelial cell; LEC, lymphatic
endothelial cell; NESC, non-endothelial stromal cell; scRNA-seq, single-cell RNA sequencing. c, Overview of scRNA-seq analysis conducted in the present
study. PTCL, peripheral T-cell lymphoma; tDLBCL, diffuse large B-cell lymphoma transformed from FL. d, UMAP plots of NHCs, colour coded by patients(top left), sites of sample collection (top right), and patient age (bottom), according to patient cohorts. e, Proportion of each major NHC component
between MFLN (left) and FL (right) cohorts. f, PDPN expression in stroma-enriched cells from MFLN samples. High magnification image indicatesheterogeneous PDPN expression levels among LECs. g, Strategy used to detect BECs (top), LECs (middle), and NESCs (bottom) using flow cytometry(FCM, red-coloured) or scRNA-seq (red circles) analysis in a representative case (MFLN 8).

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Extended Data Fig. 2 | See next page for caption.

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Extended Data Fig. 2 | Dissection of human LN BECs at single-cell resolution. a, Expression of marker genes for each BEC subcluster. Red arrowheads
show cells expressing indicated marker genes. b, Volcano plots of up- or down-regulated genes between cBECs and C-aHEVs (left) or between aHEVs
and hHEVs (right). Significance was determined as an adjusted P value of <0.05 (two-sided Wilcoxon Rank-Sum test with Bonferroni correction) (bluecoloured dots) and log2 fold-change of ≥1 (red-coloured dots). Larger dots indicate log2 fold-change of ≥2. Key genes are labelled. c, Expression ofmarker genes for each BEC subcluster in a single-cell BEC object, generated by Monocle 3. d–i, IF staining of MECA-79 (green) and GJA5 (white) shows
large arterial BECs (ABECs) (d, white arrowheads); CD31 (white) and SSUH2 (red) identify arteries surrounding LN capsule (caBECs) (e, red arrowhead);
MECA-79 (green), INSR (white), and CD31 (red) identify arterioles (aBECs) (f); MECA-79 (green), PLVAP (white), and PGF (red). High magnification
images (i and ii) are presented in 3D identifying tip cells (tBECs) (g, red arrowhead); MECA-79 (green), PLVAP (white), and HES1 (red) show activated
HEVs (aHEVs) (red arrowheads) and transitional BECs between capillary BECs and aHEVs (C-aHEVs) (white arrowheads) (h); MECA-79 (green), SELE
(white), and CXCL10 (red) to identify aHEVs and CXCL10-HEVs. Arrowheads show cells positive for indicated proteins (i). IFR, interfollicular region. Scalebars, 50 μm (grey), 200 μm (white). Representative images from one of three independent experiments are shown. i, LN schematic depicting topologicallocalization of 10 BEC subclusters. j, Expression of marker genes for key mouse LN BEC subclusters (proposed by Brulois et al25) in our human data.
k, Comparison of BEC subclusters identified here with those characterized in mice25. Bar heights of the mouse study are adjusted to the cell numbers(belonging to each subcluster) identified in this study. Key markers for mouse BEC subclusters25 are listed on right.

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Extended Data Fig. 3 | See next page for caption.

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Extended Data Fig. 3 | Dissection of human LN LECs at single-cell resolution. a, Violin plots showing expression of top marker genes for each LEC
subcluster. b, Number of DEGs per LEC subcluster. c, Heatmap showing expression of top-ranking marker genes for each LEC subcluster. Key genes
are indicated on the left. d, UMAP plot of fLECs and pfsLECs discriminated by unsupervised sub-clustering of a single ‘fLEC and pfsLEC’ subcluster. e,
Composition of fLECs and pfsLECs in the ‘fLEC and pfsLEC’ subcluster. f, Volcano plot of up- or down-regulated genes in fLECs and pfsLECs. Significancewas determined as an adjusted P value of <0.05 (two-sided Wilcoxon Rank-Sum test with Bonferroni correction) (blue-coloured dots) and log2 foldchange of ≥1 (red-coloured dots). Key genes are labelled. g, Expression of marker genes for each LEC subcluster in a single-cell LEC object, generated by
Monocle 3. h, Single-cell LECs, ordered according to pseudo-time developmental stages. Dark winding lines indicate putative developmental trajectories.
Cell regions are assigned to LEC subclusters based on marker gene expression. i–l, IF staining of PAI1 (green) and PROX1 (red) to identify bridge LECs
(bLECs). Bold dashed lines in magnified images (i and ii) indicate subcapsular sinuses (SCSs). Solid lines indicate perifollicular sinuses. Scale bars,
500 μm (left panel), 200 μm (magnification panels) (i); PTX3 (green) and PROX1 (red) to identify medullary sinus LECs (msLECs). Dashed lines indicate
boundaries between the LN cortex and medullary regions. High magnification image at right corresponds to boxed area at left. Scale bars, 200 μm (j);MARCO (red) for identification of perifollicular sinus LECs (pfsLECs). High magnification image at right shows staining of CD31 (white) and MARCO(red). Dashed lines indicate boundaries between the LN cortex and medullary regions (left) or follicles (right). Scale bars, 200 μm (k); MFAP4 (white)
and PROX1 (red) to identify collecting vessel LECs (arrowheads). High magnification images show afferent (i and ii) or efferent (iii) collecting vessels. Bolddashed lines in left panel indicate boundaries between the LN cortex and medullary regions. Scale bars, 200 μm (l). Representative images from one ofthree independent experiments are shown.

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Extended Data Fig. 4 | See next page for caption.

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Extended Data Fig. 4 | Dissection of human LN NESCs at single-cell resolution. a, Expression of marker genes for each NESC subcluster. Red arrowheads
show cells expressing indicated marker genes. b,c, Marker gene expressions in a NESC object shown in Fig. 4g (b) or Fig. 4h (c). d, DCN expression in
MFLN advSCs, SFRP4-SCs, SFRP2-SCs, TNF-SCs, and C7-SCs. e–o, IF staining of DCN showing DCN-positive fibroblasts (e); FBN1 (white) and DCN (red)to identify SCs in the capsule adventitia (advSC) (arrowheads) (f); MECA-79 (green), NR4A1 (a marker of LN fibroblastic reticular cells; white)21, andSFRP2 (red) to identify SFRP2-SCs (g); PTX3 (green), CD31 (white), and DCN (red) to identify TNF-SCs (green arrowheads) (h); C7 (white) and DCN(red) to identify C7-SCs (i); MECA-79 (green), α-smooth muscle actin (αSMA, white), and AGT (red) to identify AGT-SCs (j); MECA-79 (green), αSMA
(white), and CD31 (red) to identify SMCs. White arrowheads indicate SMCs around arteries (k); MECA-79 (green) and PLN (white) identifying SMCs
around HEVs (filled arrowheads) and arteries (empty arrowheads) (left); MECA-79 (green), αSMA (white), and ATF3 (red) identifying ATF3hi SMCs
around HEVs (middle); and MYH11 (white) and ATF3 (red) showing ATF3hi and ATF3lo SMCs around arteries (l); HSP70 (green), αSMA (white), and ATF3(red) on SMCs (m); MECA-79 (green), CD31 (white), and HIGD1B (red) identifying PCs around arteries (empty arrowheads) and HEVs (filled arrowheads)(n); BAFF (green) and CD21 (white) identifying MRCs (green arrowheads) and FDCs (white arrowheads), respectively (o). IFR, interfollicular region; LN,
lymph node; SCS, subcapsular sinus. Scale bars, 50 μm (grey),200 μm (white). Representative images from one of three independent experiments areshown. p, LN schematic depicting NESC subclusters excluding perivascular SCs (left) and an overlay image of all BEC, LEC, and NESC subclusters (right).
q, Expression of marker genes for key mouse LN NESC subclusters21 in our human data. r, Comparison of NESC subclusters identified here with thosecharacterized in mice21.

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Extended Data Fig. 5 | Overview of LNNHC atlas. a, Proportion of each NHC subcluster based on patients in the MFLN and FL cohorts. b, UMAP plot ofmajor NHC components from MFLN samples, highlighting NHCs from a patient with a benign tumour (MFLN 8) (red dots). c, Proportions of top DEGs
detected using all MFLN data and validated by DEGs in MFLN 8 according to NHC subclusters. Top DEGs were defined as the top 10% of DEGs of each
NHC subcluster, calculated using all MFLN data (listed in Supplementary Table 3,8,10). Bars of subclusters with >50 cells in MFLN 8 were highlighted byochre colouring. Dashed line indicates 80% validation.

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Extended Data Fig. 6 | See next page for caption.

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Extended Data Fig. 6 | Comparative analyses between mesenteric and peripheral LNs. a, Violin plots comparing expressions of key genes between mLN(red) and pLN (blue) samples according to NHC subclusters. *P < 0.05, **P < 0.01, ***P < 0.001 (two-sided Wilcoxon Rank-Sum test with Bonferroni
correction). NS, not significant. The exact P values are provided in Supplementary Table 13. b, Key gene ontologies of DEGs upregulated in mLN (red) orpLN (blue) compared with the other LN type according to representative NHC subclusters.

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Extended Data Fig. 7 | See next page for caption.

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Extended Data Fig. 7 | Alterations of gene expression profiles in FL stroma. a, UMAP plots of FL BEC (top), LEC (middle), and NESC (bottom)
subclusters. b, Violin plots comparing expressions of key genes between MFLN (orange) and FL (blue) samples, according to NHC subclusters.
***P < 0.001 (two-sided Wilcoxon Rank-Sum test with Bonferroni correction). NS, not significant. The exact P values are provided in Supplementary
Table 15–17. c, Gene ontology changes in FL NHC subclusters. GO enrichment analysis of DEGs upregulated in FL BEC (top left), LEC (top right), or NESC
(bottom left) subclusters relative to MFLN counterpart subclusters. d, IF staining of PROX1 (red) showing LEC distribution in representative MFLN (left)
and FL (right) samples. Scale bars, 200 μm. e, Number of PROX1-positive LECs per mm2, detected by IF staining in biologically independent MFLN (n = 5)
and FL (n = 4) samples. The box plots show the interquartile range (box limits), median (centre line), minimum to max values (whiskers), and samples(circles). **P = 0.0025 (two-sided unpaired t-test). f, IF staining of CD74 (green) and PROX1 (red) showing CD74-positive LECs in representative MFLN
(left) and FL (right) samples. Scale bars, 200 μm. g, Proportions of CD74-positive LECs among PROX1-positive LECs (%) detected by IF staining in
biologically independent MFLN (n = 4) and FL (n = 4) samples. The box plots show the interquartile range (box limits), median (centre line), minimum tomax values (whiskers), and samples (circles). *P = 0.033 (two-sided unpaired t-test). The statistical source data are provided.

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Extended Data Fig. 8 | See next page for caption.

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Extended Data Fig. 8 | Interactome analysis across FL stroma and malignant B cells. a, Strategies used to identify malignant B-cell components in FL
samples in silico. Shown are representative cases with light chain kappa (FL 4; top) or lambda (FL 8; bottom) restrictions confirmed by flow cytometry
analysis (data not shown). After identifying B-cell components by detecting CD79A expression, we assessed expression of IGKC (for light chain kappa)
and IGLC2 (for light chain lambda). Clusters with cells expressing IGKC and those expressing IGLC2 were considered non-malignant B cells, while clusters
with cells expressing only one of these genes were considered malignant B cells. b, Scatter plot showing clear discrimination of malignant (filled circles)from non-malignant (empty circles) B-cell clusters in each FL sample, based on the ratio of cells expressing IGLC2 (expression level >1; y-axis) to thoseexpressing IGKC (expression level >2; x-axis,). Red-shaded areas indicate regions in which the ratio was >2.0 or <0.25. c, Representative UMAP plots
showing B cells from FL 4 according to B-cell types (beige; non-malignant, red; malignant) (left panel) or malignant B-cell signature score (right panel).
d, Violin plots showing malignant B-cell signature score in extracted non-malignant and malignant B cells, according to different FL samples (FL 2–10).
***P = 1.1 × 10−204 (FL 2), ***P = 0 (FL 3), ***P = 3.3 × 10−176 (FL 4), ***P = 0 (FL 5), ***P = 4.2 × 10−122 (FL 6), ***P = 0 (FL 7), ***P = 4.2 × 10−161 (FL 8),***P = 3.7 × 10−256 (FL 9), ***P = 2.5 × 10−81 (FL 10) (two-sided Wilcoxon Rank-Sum test with Bonferroni correction). e, Violin plots showing the expressionof CD27 in non-malignant and malignant B cells. ***P = 0 (two-sided Wilcoxon Rank-Sum test with Bonferroni correction). f, Comparison of CD27 mean
fluorescence intensity (MFI) between FL CD19+CD10− (non-malignant B-cell fraction) and CD19+CD10+ (malignant B-cell fraction) cells. Circles represent
biologically independent samples (n = 8; FL 11–18). *P = 0.039 (two-sided Wilcoxon matched-pairs signed rank test). g, Flow cytometry analysis of CD27
expression on CD19+CD10− and CD19+CD10+ cells of a representative FL sample (FL 14). The statistical source data are provided.

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Extended Data Fig. 9 | See next page for caption.

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Extended Data Fig. 9 | Exploring stroma-derived prognostic markers in FL. a, Scheme of stepwise survival analysis using public data from FL patients66
to identify stroma-derived prognostic markers. b, Genes with unfavourable prognostic impact, as revealed by survival analysis using Kaplan–Meiermethods with the two-sided log-rank test (Step 2). Representative NHC subcluster denotes subclusters in which indicated gene expression is most greatly
upregulated. FC, fold-change; HR, hazard ratio. c, Strategies used to confirm prognostic impact of candidate genes identified in Step 2 (Step 3). OS, overall
survival. d, Results of analysis performed in Step 3. Shown is the proportion of patients whose samples highly expressed indicated genes in favourable orunfavourable prognostic groups. *P = 0.034 (LY6H), *P = 0.017 (LOX), *P = 0.024 (PTGIS), *P = 0.014 (PIEZO2), *P = 0.027 (CHI3L1), **P = 0.0077 (TDO2),**P = 0.0092 (REM1) (two-sided Fisher’s exact test). NS, not significant. e, Kaplan–Meier curves showing overall survival of newly diagnosed FL patients(n = 180) based on expression of PTGIS, PIEZO2, and CHI3L1. Statistical analysis was performed using the two-sided log-rank test. f, Estimation of overall
survival based on expression of LY6H, LOX, TDO2, and REM1 in the FL patients of the intermediate prognosis group (n = 64, two-sided log-rank test). g,
Estimation of overall survival based on expression of PECAM1 and CDH5 (n = 180, two-sided log-rank test). The statistical source data are provided.

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Extended Data Fig. 10 | See next page for caption.

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Extended Data Fig. 10 | Applicability of the LNNHC atlas to PTCL and tDLBCL stroma. a, UMAP plots of PTCL BEC (left), LEC (middle), and NESC
(right) subclusters. b, Compositional differences between PTCL and MFLN NHCs based on major NHC components, BEC subclusters, LEC subclusters,
and NESC subclusters (from left to right). **P = 0.0076, ***P = 2.7 × 10−4 (two-sided chi-squared test). NS, not significant. c, UMAP plots of tDLBCL BEC(left), LEC (middle), and NESC (right) subclusters. d, Compositional differences between tDLBCL and MFLN NHCs based on major NHC components, BEC
subclusters, LEC subclusters, and NESC subclusters (from left to right). *P = 0.030, ***P = 3.4 × 10−6 (Major NHC components), ***P = 6.3 × 10−16 (LEC),***P = 1.1 × 10−23 (NESC) (two-sided chi-squared test). e, Violin plots comparing expressions of key genes between MFLN (orange) and tDLBCL (green)samples according to selected NHC subclusters. *P = 0.015, **P = 0.0039 (LY6H), **P = 0.0090 (LOX), **P = 0.0075 (VCAM1), ***P = 8.0 × 10−111(two-sided Wilcoxon Rank-Sum test with Bonferroni correction). NS, not significant. f, Expression of TNFSF13B (left) and CR2 (right) in tDLBCL follicularstromal cells identifying MRCs and FDCs, respectively. g,h, Pseudo-time developmental stages in tDLBCL advSCs, SFRP2-SCs, TNF-SCs, C7-SCs,
MRCs, and FDCs (g) or in tDLBCL SMC subclusters, PCs, TRCs, AGT-SCs, MRCs, and FDCs (h). Dark winding lines in the cell objects indicate putative
developmental trajectories. The statistical source data are provided.

Nature Cell Biology | www.nature.com/naturecellbiology

Last updated by author(s): Jan 20, 2022

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Corresponding author(s): Mamiko Sakata-Yanagimoto

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FACSAria II and III (BD Biosciences) were used for acquisition of flow cytometry data.
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Libraries were sequenced on an Illumina HiSeq X Ten system, mapped to the human genome (build GRCh38), and demultiplexed using
CellRanger pipelines (10x Genomics, version 3.1.0).

Data analysis

Flowcytometric data were analyzed using FlowJo software (Tree Star Inc. v10.7.1).
Single-cell data was analyzed using R package Seurat (R. Satija Lab. v3.2.2) on RStudio (v3.5.0 or v4.0.2).
Monocle 3 package (Trapnell Lab. v0.2.3) was used for trajectory analysis on single-cell data.
CellPhoneDB package (Teichlab. v2.1.1) was used on Python (v3.6) for intercellular ligand–receptor interaction analysis.
GO enrichment analysis was performed using metascape (http://metascape.org).
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DecontX (in celda package. Campbell Lab. v1.6.1) and SoupX (v1.5.2) were used for the detection of ambient RNA contamination.
For analysis of whole-exome sequencing, the Genomon2 pipeline (v2.6.2) was used for sequence alignment and mutation calling.
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Please find detailed descriptions for each analysis in the Methods section.
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April 2020

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Policy information about availability of data
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scRNA-seq data that support the findings of this study have been deposited at the European Genome-Phenome Archive (https://ega-archive.org) database and canbe retrieved using the accession number EGAD00001008311. For survival analysis, a DNA microarray dataset from Leich et al66 was downloaded from the Gene
Expression Omnibus (GEO) (accession number: GSE16131). For mapping of scRNA-seq data, GRCh38 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.39)
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Sample size

No statistical method was used to determine sample size a priori. The number of human lymph node and lymphoma samples was highly
restricted due to the limited availability of these samples in clinical settings.

Data exclusions

Pre-processed single-cell data from each sample were further processed and analysed individually using R package Seurat on RStudio. Afterremoving ribosomal genes, genes expressed in fewer than 3 cells, and cells expressing fewer than 200 genes, we filtered out cells with lessthan 200 unique feature counts (low quality cells). Cells with unique feature counts greater than three times the median value (possible
doublets) and/or cells with more than twice the median number of mitochondrial genes (possible apoptotic or lysed cells) were also removed.
After the data integration and clustering analysis, we removed data of NHC subclusters considered possible doublets as characterized by highexpressions of marker genes for different NHC components and incongruously high unique feature counts.

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Antibodies

ChIP-seq

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Animals and other organisms
Human research participants
Clinical data

Antibodies
Antibodies used

April 2020

Dual use research of concern

Flow cytometric analysis: PE-anti-CD45 (BioLegend. Cat# 304058), AF488-anti-pan-cytokeratin (ThermoFisher Scientific. Cat#
53-9003-82), FITC-anti-CD31 (Biolegend, 303103), APC-anti-podoplanin (Biolegend. Cat# 337021), PE-Cy7-anti-CD31 (Biolegend. Cat#
303117), PE-Cy7-anti-CD34 (Biolegend, 343515). PE-anti-CD27 (Biolegend, 302842), FITC-anti-CD3 (Biolegend, 300406), APC-antiCD19 (Miltenyi Biotec, 130-113-165), PE-Cy7-anti-CD10 (Biolegend. Cat# 312214). The dilution for each antibody is described in the
Methods section of the manuscript.

2

Functional blocking: anti-CD27 blocking antibody (R&D systems, MAB382), isotype mouse IgG1 (R&D systems, MAB002).
Antibodies used for immunofluorescence staining were listed in Supplementary Table 9.

Validation

All the antibodies used in this study have been tested by the manufacturer and have been cited by other authors and references areavailable on the manufacturer's websites. We provide catalog numbers for all the antibodies in the Methods section of themanuscript or in Supplementary Table 9 as readers can retrieve the information of the antibodies. We have further evaluated thespecificity of the antibodies in our samples by analyzing the distribution of the antibody signals and the absence of the antibody
signals in the regions where the target protein was not supposed to be expressed.

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Policy information about studies involving human research participants
Population characteristics

Supplementary Table 1 summarises the characteristics of patients in metastasis-free lymph node and follicular lymphoma
cohorts. Metastasis-free lymph node cohort consists of neoplasm-bearing patients (n = 9) who had undergone surgical LN
dissection. The median age of the patients in this cohort is 66 years old. Follicular lymphoma cohort consists of 10 patientswith the median age of 59. Among the follicular lymphoma patients, six patients were newly diagnosed cases. Most of thefollicular lymphoma patients (n = 9) were with pathological grade of 1-2.
Supplementary Table 7 summarises the characteristics of patients in peripheral T-cell lymphoma cohort and diffuse large Bcell lymphoma transformed from follicular lymphoma cohort. Peripheral T-cell lymphoma cohort consists of five newly
diagnosed patients with various subtypes of lymphoma with the median age of 78. Diffuse large B-cell lymphomatransformed from follicular lymphoma cohort consists of three patients.
Additional follicular lymphoma samples for functional experiments were collected from eight patients. Characteristics offollicular lymphoma patients in the additional cohort is summarised in Supplementary Table 10.

Recruitment

Samples were prospectively collected from patients who agreed to participate in the study. There were no other criteria forpatient selection. There is no self-selection bias or other biases in recruitment.

Ethics oversightThis study was approved by the Ethics Committee of the University of Tsukuba Hospital and the review boards of associatedinstitutions that provided human samples (Kameda Medical Center, NTT Medical Center Tokyo, and Mito Medical Center)and conducted according to all relevant ethical regulations regarding human patients. Written informed consent was
obtained from all participating patients. The participants were not compensated for their participation.

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Recombinant protein binding assay: PE-anti-human IgG Fc (R&D systems, FAB110P). The dilution is described in the Methods sectionof the manuscript.

Note that full information on the approval of the study protocol must also be provided in the manuscript.

Flow Cytometry
Plots
Confirm that:
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All plots are contour plots with outliers or pseudocolor plots.
A numerical value for number of cells or percentage (with statistics) is provided.

Methodology
Sample preparation

April 2020

Single-cell isolation of LN non-haematopoietic cells: After collection, LN or lymphoma samples were immediately minced anddigested for 1 h with RPMI 1640 medium (Sigma-Aldrich, R8758) with 5% fetal bovine serum containing 0.2 mg/ml
collagenase P (Sigma-Aldrich, 11213857001), 0.8 mg/ml dispase (Gibco, 17105041), and 0.1 mg/ml DNase 1 (Worthington,
LS002139), with continuous agitation. Cells were then filtered through a 70 μm mesh and red blood cells were lysed in 1%
ammonium-chloride-potassium buffer. Thereafter, haematopoietic cells and contaminated red blood cells were depletedusing human CD45 (130-045-801) and CD235a (130-050-501) microbeads according to the manufacturer’s instructions(Miltenyi Biotec). For MFLN samples, the remaining single-cell suspension was incubated with phycoerythrin (PE)-anti-CD45(Biolegend) in combination with Alexa Fluor 488-pan-cytokeratin (ThermoFisher Scientific), allophycocyanin (APC)-antipodoplanin (Biolegend), and PE-cyanin 7 (PE-Cy7)-anti-CD31 (Biolegend). For lymphoma samples, PE-anti-CD45 was mixed
with fluorescein isothiocyanate-anti-CD31 (Biolegend), APC-anti-podoplanin, and PE-Cy7-anti-CD34 (Biolegend). The samples
were incubated for 20 min, then 7-AAD Viability Staining Solution (ThermoFisher Scientific, 00-6993-50) was added andincubated for 10 min in the dark on ice. CD45-negative live cells were sorted using FACSAria II or III (BD Bioscience) after
removing doublets by gating with a FSC-H versus FCS-W plot and a SSC-H versus SSC-W plot.
Single-cell isolation of FL haematopoietic cells: After thawing, cell suspensions were filtered through a 70 μm mesh andincubated with 7-AAD Viability Staining Solution for 10 min in the dark.
Flow cytometric analysis of FL haematopoietic cells: After thawing, cells were filtered through a 70 μm mesh, and incubatedwith PE-anti-CD27 (Biolegend), FITC-anti-CD3 (Biolegend), APC-anti-CD19 (Miltenyi Biotec), and PE-Cy7-anti-CD10 (Biolegend)

3

Recombinant protein binding assay: Recombinant Fc chimera CD70 (SinoBiological, 10780-H01H) or human IgG (R&D
systems, 1-001-A) was incubated with a single-cell suspension of FL haematopoietic cells for 10 min at 4 ºC in RPMI with 10%
FCS. To block CD70–CD27 binding, malignant B cells were incubated in the presence of anti-CD27 blocking antibody (R&D
systems, MAB382) or isotype mouse IgG1 (R&D systems, MAB002) for 30 min at 4 ºC before binding. After binding, the cells
were washed, fixed by 4% PFA for 10 min at 20 ºC and incubated with PE-anti-human IgG Fc (R&D systems), FITC-anti-CD3,
APC-anti-CD19, and PE-Cy7-anti-CD10 for 20 min at 4 ºC.

Instrument

FACSAria II and III (BD Biosciences)

SoftwareData collection: FACSAria II and III (BD Biosciences)
Data analysis: FlowJo (Tree Star Inc. v10.7.1)

Cell population abundance

The purity of sorted fractions was not determined for each sorting because FACS was used justed to enrich cell fractions ofinterest, followed by scRNA-seq which finally identifies the cell-type of sorted cells.

Gating strategy

All captured cells were first gated for singlet cells with a FSC-H versus FCS-W plot and a SSC-H versus SSC-W plot. Thereafter,
dead cells were removed as 7-AAD-positive cells.
Non-haematopoietic cells: From singlets, CD45-negative (SSC-H vs PE-CD45).
Blood endothelial cells: From non-haematopoietic cells, CD31-positive and PDPN-negative (PE-Cy7-CD31 vs APC-PDPN).
Lymphatic endothelial cells: From non-haematopoietic cells, CD31-positive and PDPN-positive (PE-Cy7-CD31 vs APC-PDPN).
Non-endothelial stromal cells: From non-haematopoietic cells, CD31-negative (PE-Cy7-CD31 vs APC-PDPN).
FL malignant B cells: From singlets, CD19-positive (SSC-H vs APC-CD19), followed by CD10-positive (SSC-H vs PE-Cy7-CD10).
FL non-malignant B cells: From singlets, CD19-positive (SSC-H vs APC-CD19), followed by CD10-negative (SSC-H vs PE-Cy7CD10).
CD70-Fc-bound FL malignant B cells: From FL malignant B cells, human IgG Fc-positive (SSC-H vs PE-anti-human IgG Fc).

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antibodies for 20 min on ice. Cells were then incubated with 7-AAD Viability Staining Solution for 10 min in the dark andanalysed using FACSAria II or III.

Tick this box to confirm that a figure exemplifying the gating strategy is provided in the Supplementary Information.

April 2020

4

ORIGINAL PAPER
Nagoya J. Med. Sci. 84. 80–90, 2022
doi:10.18999/nagjms.84.1.80

Alternating bortezomib-dexamethasone and lenalidomidedexamethasone in patients with newly diagnosed multiple
myeloma aged over 75 years
Akihiro Yokoyama1,2, Akiko Kada3, Toshiya Kagoo1, Michihiro Hidaka4,
Hiroatsu Iida5, Yasuhiko Miyata5, Akiko M. Saito3, Morio Sawamura6,
Takuya Komeno7, Kazutaka Sunami8, Naoki Takezako9
and Hirokazu Nagai5
Division of Hematology, Department of Internal Medicine, National Hospital Organization Tokyo Medical
Center, Tokyo, Japan
2
Division of Transfusion Medicine, National Hospital Organization Tokyo Medical Center, Tokyo, Japan3
Department of Clinical Trials and Research, Clinical Research Center, National Hospital Organization
Nagoya Medical Center, Nagoya, Japan
4
Department of Hematology, National Hospital Organization Kumamoto Medical Center, Kumamoto, Japan
5
Department of Hematology, National Hospital Organization Nagoya Medical Center, Nagoya, Japan
6
Department of Hematology, National Hospital Organization Shibukawa Medical Center, Shibukawa, Japan
7
Department of Hematology, National Hospital Organization Mito Medical Center, Ibaraki, Japan
8
Department of Hematology, National Hospital Organization Okayama Medical Center, Okayama, Japan
9
Department of Hematology, National Hospital Organization Disaster Medical Center of Japan, Tachikawa,
Japan
1

ABSTRACT
More than 40% of Japanese patients with multiple myeloma (MM) are over 75 years of age at diagnosis.
Regardless of the treatment benefits, complications and relapses obstruct long-term survival. We conducteda phase II, open-label, single-arm, multicenter clinical trial to assess the efficacy and safety of alternatingbortezomib-dexamethasone (Bd) and lenalidomide-dexamethasone (Ld) (Bd/Ld) treatment in MM patients
aged over 75 years (MARBLE trial). Patients received Bd therapy from days 1 to 35 and Ld therapy fromdays 36 to 63. For Bd therapy, patients were administered bortezomib 1.3 mg/m2 and oral dexamethasone
20 mg on days 1, 8, 15, and 22. For Ld therapy, they were administered lenalidomide 15 mg from days
36 to 56 and dexamethasone 10 mg on days 36, 43, 50, and 57. They underwent six treatment cycles intotal, each consisting of a 63-day regimen. In total, 10 patients were enrolled, with a median age of 81
years. Efficacy was not evaluated because the patients were fewer than planned. The overall response rate
was 80.0% and complete response rate 40.0%. Seventy percent of patients completed the study treatment.
Progression-free survival and overall survival at 2 years were 40.0% and 80.0%, respectively. Adverse
events of grade 3 or higher, including anemia, decreased lymphocyte count, neutropenia, and hypokalemia,
were observed in eight patients. Alternating chemotherapy with Bd/Ld might be feasible, but its efficacy
should be verified further.
Keywords: myeloma, alternating therapy, bortezomib, lenalidomide, dexamethasone

Received: November 25, 2020; accepted: May 21, 2021
Corresponding Author: Akihiro Yokoyama, MD, PhD
Division of Hematology, Department of Internal Medicine and Division of Transfusion Medicine, National
Hospital Organization Tokyo Medical Center, 2-5-1 Higashigaoka, Meguro-ku, Tokyo 152-8902, Japan
Tel: +81- 3-3411-0111 , E-mail: yokoyama.akihiro.sa@mail.hosp.go.jp
80

NHOH-MARBLE study

Abbreviations:AEs: adverse events
ASCT: autologous stem cell transplantation
BMP: bortezomib/melphalan/prednisone
Bd: bortezomib-dexamethasone
Bd/Ld: bortezomib-dexamethasone and lenalidomide-dexamethasone
CI: confidence interval
CRR: complete response rate
FIRST trial: Frontline Investigation of Revlimid and Dexamethasone Versus Standard Thalidomide trial
Ld: lenalidomide plus low-dose dexamethasone
MM: multiple myeloma
NDMM: newly diagnosed multiple myeloma
ORR: overall response rate
OS: overall survival
PFS: progression-free survival
TTR: time to response
UMIN: University Hospital Medical Information Network
VGPR: very good partial response
VISTA trial: Velcade as Initial Standard Therapy in Multiple Myeloma: Assessment with Melphalan
and Prednisone trial

This is an Open Access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
License. To view the details of this license, please visit (http://creativecommons.org/licenses/by-nc-nd/4.0/).

INTRODUCTION
Multiple myeloma (MM) is caused by an accumulation of cancerous plasma cells, theterminally differentiated form of B-lymphocytes, in the bone marrow, and it is characterized by
the production of an abnormal protein called monoclonal immunoglobulin (M-protein) by thetumor cells. This refractory disease shows several clinical symptoms, including hypercalcemia,
renal insufficiency, anemia, and bone osteolytic changes, otherwise known as CRAB criteria,
even in the novel agent era.1
Nevertheless, radical improvements in treatment outcomes have been accomplished by treatment
with high-dose melphalan plus autologous stem cell transplantation (ASCT) as well as withnovel chemotherapeutic agents, including bortezomib, lenalidomide, and thalidomide. However,these treatments have failed to improve long-term survival in elderly patients, who are generally
ineligible for ASCT. Therefore, the greatest therapeutic benefits of these treatments are limited
to patients under 70 years of age, mainly due to adverse events (AEs).2
Based on data from randomized phase III trials, the following two treatment options arerecommended for elderly patients: bortezomib/melphalan/prednisone (BMP), based on the Velcade
as Initial Standard Therapy in Multiple Myeloma: Assessment with Melphalan and Prednisone(VISTA) trial3 and lenalidomide plus low-dose dexamethasone (Ld), based on the Frontline
Investigation of Revlimid and Dexamethasone Versus Standard Thalidomide (FIRST) trial. 4 In
Japan, both BMP and Ld are approved as front-line options.
Further, combination therapy with bortezomib-dexamethasone (Bd) has been investigated,
mainly by the UPFRONT trial, a community-based phase IIIB study in the US, performed tocompare three front-line regimens based on bortezomib (Bd, BMP, and Bd plus thalidomide)
in ASCT-ineligible patients with MM; the study results suggested that a Bd treatment regimen
balances efficacy and safety in elderly MM patients.5
To further improve the treatment outcome in this population, a triplet combination of bortNagoya J. Med. Sci. 84. 80–90, 2022

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doi:10.18999/nagjms.84.1.80

Akihiro Yokoyama et al

ezomib plus Ld administered in a modified dose and schedule was examined; it was found to
be a well-tolerated and effective treatment option for newly diagnosed MM (NDMM) patients
aged over 65 years.6
Presently, despite the known benefits of these novel agents, most patients pass away after
repeated relapses. There is little evidence supporting alternating chemotherapy as a treatment
strategy for MM. The international phase II trial by Mateos MV et al comparing BMP and Ld
administered in a sequential (121 patients) and alternating (120 patients) manner did not show
differences in efficacy and safety between the two groups.7 In this study, Ld was administeredafter the start of bortezomib treatment because Ld was not approved for newly diagnosed or
untreated MM in Japan when this study was designed. In our study, we investigated an alternating
chemotherapy regimen of Bd and Ld (alkylator-free regimen) with respect to balancing safety,
tolerability, and efficacy in elderly patients with ASCT-ineligible NDMM. We assumed that theside effects will be suppressed by alternating two drugs with different mechanisms of action as
an initial treatment strategy for MM, leading to high efficacy and improved survival. Furthermore,
we detected chromosomal abnormalities in patients using fluorescence in situ hybridization (FISH)
and assessed whether the alternating treatment strategy suppresses clonal evolution.

METHODS
Trial design
In this phase II, open-label, single-arm, multicenter trial, we assessed the efficacy and safetyof alternating Bd and Ld as an induction therapy for NDMM patients aged 75 years or more
who were ineligible for ASCT. Patients who satisfied the eligibility criteria were enrolled in the
study. The details of this study are described elsewhere.8
This study was performed at 22 facilities in Japan and was registered in the Clinical Trial
Registry of the University Hospital Medical Information Network (UMIN), Japan (Registration
Number: UMIN000013773).
Interventions
Patients were enrolled in the trial within 4 weeks of diagnosis and began treatment according
to the study protocol. Patients received Bd therapy from days 1 to 35 (for 35 days) and Ld
therapy from days 36 to 63 (for 28 days). Patients underwent a total of six treatment cycles,
each consisting of a 63-day regimen, as mentioned above. The starting doses of bortezomib andlenalidomide were adjusted based on patient age, general condition, and renal function.
For Bd therapy, patients were administered bortezomib 1.3 mg/m2 and oral dexamethasone
20 mg on days 1, 8, 15, and 22. The site of bortezomib administration was rotated between
sessions to avoid consecutive injections at the same site (eg, left thigh, right thigh, abdomen).
Following Bd therapy, patients were administered lenalidomide 15 mg from days 36 to 56 anddexamethasone 10 mg on days 36, 43, 50, and 57 as Ld therapy. A physician decided whether
each patient should receive treatment in an inpatient or outpatient setting.
It was recommended to administer antibacterial agents, antifungal agents, or sulfamethoxazoletrimethoprim to prevent infectious diseases and to use anticoagulants or antiplatelet agents toprevent deep vein thrombosis in patients who were concerned about the increase in the risk ofvenous thromboembolism.
We performed the chromosome test (G-binding) and FISH test at baseline 2–4 weeks before
the treatment started. The myeloma FISH panel included the following: Vysis LSI IGH/CCND1
XT Dual Color Dual Fusion Probes probe to detect t(11;14)(q13;q32), Vysis LSI IGH/FGFR3
Nagoya J. Med. Sci. 84. 80–90, 2022

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doi:10.18999/nagjms.84.1.80

NHOH-MARBLE study

Dual Color Dual Fusion Probes to detect t(4;14)(p16;q32), Vysis LSI IGH/MAF Dual Color
Dual Fusion Probes probe to detect t(14;16)(q32;q23), Vysis LSI IGH/MYC/CEP 8 Tri-Color
Dual Fusion FISH Probes to detect t(8;14)(q24;q32), Vysis LSI D13s319/13q34 FISH Probes todetect deletion of 13q14, Vysis LSI TP53 SpectrumOrange/ CEP 17 SpectrumGreen Probes todetect deletion of p53 (17p13.1), CKS1B/CEN1p Dual Color FISH Probe to detect chromosome
1q21 gain, AHCYL1/CEN1p Dual Color FISH Probe to detect deletion of 1p13, and CDKN2C/
CEN1p Dual Color FISH Probe to detect deletion of 1p32.
Further, we conducted a comprehensive geriatric assessment, including the Cumulative Illness
Rating Scale for Geriatrics, at baseline.9
Sample size
One trial reported that Ld therapy achieved an overall response rate (ORR) of 70.4% in untreated MM patients aged 75 years or older.10 In contrast, the EVOLUTION study revealed ORRs
of 85% and 88% for bortezomib-lenalidomide-dexamethasone and bortezomib-dexamethasonecyclophosphamide-lenalidomide therapies in a population of untreated MM patients, respectively.11
We expected our approach of alternating chemotherapy with Bd and Ld to achieve an ORR
of 88%. Thereby, the required sample size was calculated as n = 32, assuming an expected
response rate of 88%, a threshold response rate of 70.4%, α = 0.05 (one-sided), and β = 0.2(80% power), based on a binomial distribution. However, we set the sample size at 35 according
to a dropout rate of 10%.
Statistical methods
The primary endpoint was the ORR during the period of alternating chemotherapy with Bd and
Ld. Secondary endpoints were AEs, the proportion of treatment continuation, complete response
rate (CRR), very good partial response (VGPR), progression-free survival (PFS), overall survival(OS), and time to response (TTR).
ORR, CRR and VGPR were estimated with 90% confidence intervals (CIs). Survival curves
of PFS, OS and TTR were calculated using the Kaplan–Meier method; CI was calculated using
Greenwood’s formula. Further, the occurrence of worst-grade AEs, grade-3 or higher AEs, andserious AEs were calculated.

RESULTS
Patient demographics
Ten patients were enrolled between October 2014 and March 2016 from three centers of the
National Hospital Organization in Japan. The study ended before reaching the target sample size
because of slow accrual. All patients (five men and five women) were included in the intentionto-treat analysis. Patient characteristics are shown in Table 1.

Table 1

Patient characteristicsn (%)*
Number of patients
GenderMale
FemaleNagoya J. Med. Sci. 84. 80–90, 2022

10
5 (50.0%)
5 (50.0%)83

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Akihiro Yokoyama et al

Age (years)
Durie & Salmon System stage
II
III
International Staging System stageI
II
III
ECOG Performance Status score
1
2
3
Serum M protein type
IgG
IgA
BJP
Bone lesion
Osteoporosis
Osteolytic lesions
Extensive bone destruction and major fractures
Translocation karyotype/Chromosome abnormalityt(11;14)(q13;q32), other
Other
Chromosome 13 abnormalities -13/13qFISH abnormalities (n = 9)t(4;14)(p16;q32)t(8;14)(q24;q32)t(11;14)(q13;q32)
del(17p)del(1p32.3)
1q21 gaindel(13)
Other (n = 3)
Serum β2-microglobulin (mg/L)
Albumin (g/dL)
Hb (g/dL)
Creatinine clearance (mL/min)
Ca (mg/dL)
LDH (U/L)
Ratio of bone marrow plasma cells
Comorbidity#
Vascular
Respiratory
Eyes, ears, nose, throat, and larynx
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81 (76–85)
2 (20.0%)8 (80.0%)
1 (10.0%)
5 (50.0%)
4 (40.0%)
3 (30.0%)
3 (30.0%)
4 (40.0%)
4 (40.0%)
5 (50.0%)
1 (10.0%)
3 (30.0%)
2 (20.0%)
5 (50.0%)
1 (10.0%)
1 (10.0%)
1 (10.0%)
3 (33.3%)
1 (11.1%)
2 (22.2%)
1 (11.1%)
1 (11.1%)
5 (55.6%)
5 (55.6%)
1 (33.3%)
5.3 (3.1–9.5)
3.2 (2.5–4.0)
4.4 (3.9–4.9)
45.8 (31.8–66.7)
9.5 (7.2–11.3)
167 (121–253)
41.6 (9.0–80.4)
6 (60.0%)
2 (20.0%)
1 (10.0%)
doi:10.18999/nagjms.84.1.80

NHOH-MARBLE study

Liver, pancreas, and biliary
Renal
Neurologic
Endocrine and breast
Psychiatric illness

1 (10.0%)
5 (50.0%)
1 (10.0%)
2 (20.0%)
1 (10.0%)*Data are presented as median (range) or number (%).
BJP: Bence Jones proteinuria
Ca: calcium
ECOG: Eastern Cooperative Oncology GroupFISH: fluorescence in situ hybridization
Hb: hemoglobin
IgA: immunoglobulin A
IgG: immunoglobulin G
LDH: lactate dehydrogenase#
Scores ≥ 2, assessed with the Cumulative Illness Rating Scale-Geriatric during comprehensive
geriatric assessment.

The median age was 81 years (range, 76–85 years). Eight patients (80.0%) had stage III MM
according to the Durie and Salmon system. All patients had Eastern Cooperative Oncology Group
Performance Status score ≥ 1; three (30.0%) had score 1, three (30.0%) had score 2, and four
(40.0%) had score 3. In terms of the serum M protein type, five (50.0%), four (40.0%), andone (10.0%) patients had IgA, IgG, and Bence Jones protein, respectively. In one patient, thetranslocation karyotype was t(11;14)(q13;q32). FISH abnormalities were observed as follows:
five patients with 1q21 gain, five with del(13), three with t(4;14)(p16;q32), two with t(11;14)(q13;q32), one with t(8;14)(q24;q32), one with del(17p), and one with del(1p32.3). Six patientswere categorized into the high-risk subgroup with chromosomal abnormalities: t(4;14), t(14;16),del(17p), or 1q21 gain, as determined by the International Myeloma Working Group and Mayo
Clinic.12,13 All patients had bone lesions, and the median serum β2-microglobulin level of 5.3
mg/L (range 3.1–9.5 mg/L) was high. Among out of the 10 patients, 7 (70.0%) completed thetrial planned six treatment of 6 cycles.
The remaining three patients discontinued up to six cycles of Bd therapy, during four cycles of
Bd therapy, and after three cycles of Bd therapy. The reasons for early treatment discontinuation
were extended treatment interval (n = 2) and patient request (n = 1).
Outcomes
The maximum effect of patient response is shown in Table 2.
Table 2

Bd/Ld

sCR
3

CR
1

Maximum effect of patient response

VGPR
2

PR
2

SD
2

Total (n)
10

ORR
80.0%

Bd: bortezomib-dexamethasone
Ld: lenalidomide-dexamethasonesCR: stringent complete response
CR: complete response
VGPR: very good partial response
PR: partial response
SD: stable disease
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Akihiro Yokoyama et al

The ORR was 80.0% (90% CI: 49.3–96.3%), CRR was 40%, and VGPR rate was 20% for
Bd/Ld alternating treatment, with seven patients (70.0%) having completed the planned six cycles
of study treatment. In two patients, the effect worsened when the treatment was switched from
Ld to Bd.
At the cut-off date (January 15, 2018), the median follow-up time was 27 months. The 2-year
OS and PFS were 80.0% and 40.0% (95% CI: 40.9–94.6%, 12.3–67.0%), respectively (Fig. 1).

Fig. 1

Kaplan-Meier curve for overall survival, progression-free survival, and time to response

Four out of six patients with relapse or disease progression received the first salvage therapy
with immunomodulatory drugs (IMiDs). Moreover, two patients additionally received second
salvage therapy with IMiDs or bortezomib. Regarding the response time, the probability at 10
months was 20.0% (95% CI: 3.1–47.5%). Three patients from the high-risk subgroup died during
the follow-up period due to myeloma.
Of the total 10 patients, 8 had AEs of grade 3 or higher, the most common of which were
hematologic toxicities, such as anemia, lymphocyte count decrease, neutropenia, platelet countdecrease, and white blood cell decrease, as shown in Table 3. Grade 4 AEs of neutropenia,
lymphocyte count decrease, platelet count decrease, and white blood cell decrease were observedin one patient during cycle 4 of Bd therapy.
Table 3 Adverse events of grade 3 or more

Cycle

n

1

2

3

4

5

6

Bd

Ld

Bd

Ld

Bd

Ld

Bd

Ld

Bd

Ld

Bd

Ld

10

10

10

10

10

9

9

8

8

8

7

7

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NHOH-MARBLE study

Anemia

3

1

2

1

1

0

1

0

0

0

0

0

Diarrhea

0

0

1

0

0

0

0

0

0

0

0

0

Nausea

0

1

0

0

0

0

0

0

0

0

0

0

Infections,infestations, andothers

0

0

0

0

0

0

1

0

0

0

0

0

Lymphocyte countdecrease

1

2

0

2

0

0

1

0

0

0

0

1

Neutropenia

2

0

1

1

1

1

1

0

0

0

0

0

Platelet countdecrease

1

1

0

0

0

0

1

0

0

0

0

0

White blood cellcount decrease

1

1

1

1

1

1

1

0

0

0

0

0

Anorexia

0

0

0

0

0

0

1

0

0

0

0

0

Hypokalemia

0

0

1

2

1

1

0

0

0

0

0

0

Hyponatremia

1

0

0

0

0

0

0

0

0

0

0

0

Back pain

1

0

0

0

0

0

0

0

0

0

0

0

Bone pain

1

0

0

0

0

0

0

0

0

0

0

0

Two patients experienced unexpected serious AEs; one had grade 2 arthralgia and the othergrade 3 musculoskeletal and connective tissue disorders. The former was diagnosed as osteoarthritis of the hip; subsequently, bortezomib on day 22 was omitted, and the patient recovered after
undergoing joint arthroplasty. The latter was diagnosed as a fracture of the right femoral neckand was resolved with an arthroplasty. We discontinued the study treatment for the latter patient.
In terms of its association with the number of treatment cycles, the number of AEs of grade
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Akihiro Yokoyama et al

3 or higher decreased after cycle 4 of Ld treatment, and only one case of lymphocyte countdecrease occurred at cycle 6 of Ld treatment.

DISCUSSION AND CONCLUSION
In the present study, we evaluated the feasibility and efficacy of alternating Bd and Ld treatments in elderly patients with NDMM.
We hypothesized that the study treatment would be effective because patients would receive
first-line therapy with drugs of different mechanisms of action during each cycle; this would
suppress the myeloma cells while reducing severe toxicity. Our results did not support the
hypothesis in terms of efficacy, as the ORR, the primary endpoint, was 80.0% in this study, andits lower limit of 90% CI was below the threshold value of 70.4%. However, the 2-year PFS and
OS of 40.0% and 80.0%, respectively, were comparable to those reported in elderly patients in
the FIRST trial14 or the UPFRONT study,15 despite this MARBLE trial having 55.6% of patientswith high-risk cytogenetics. Further, with respect to toxicity, the incidence of hematologic andnon-hematologic AEs or serious AEs was similar to that reported previously.7,14 Because Ld wasapproved in our country after the start of this study, it was not possible to enroll the planned
number of patients in this study, and the study was discontinued early.
All patients received at least three cycles of Bd/Ld treatment, and 70.0% of them completed
six cycles (54 weeks), suggesting that this study treatment was well-tolerated even in elderly
patients with MM.7 Further, considering that our study was aimed at patients aged over 75 years
and that 70.0% of these patients completed the study treatment, our results for AEs are better
than those reported in previous studies with elderly patients.7,14
Moreover, the treatment of patients aged 75 years or more needs to be optimized. Although
the number of subjects was too small to draw any conclusions from this study, the alternating
chemotherapy strategy based on a combination of proteasome inhibitors and IMiDs still has thepotential to become one of the treatment options for elderly MM patients. This strategy can beapplied in combination with anti-CD38 monoclonal antibodies.
The Bd/Ld alternating therapy is widely indicated in patients with standard-risk (other thanhigh-risk subgroup), who face difficulty in using a combination of proteasome inhibitors and
IMiDs owing to AEs and who can visit the hospital to receive Bd therapy once a week.
To summarize, the study suggests that alternating Bd/Ld treatment might be tolerable in elderly
patients with NDMM; however, its efficacy was not determined because the number of patientswas smaller than planned.

AUTHOR CONTRIBUTIONSAY and AK equally contributed to this work.

ACKNOWLEDGEMENTS
This study was supported by an operating expense grant for research from the National
Hospital Organization.

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NHOH-MARBLE study

DECLARATIONSStatement of ethics
Written informed consent was obtained from every participant in the study.
This trial was approved by the Central Ethics Review Committee for Clinical Research of the
National Hospital Organization on July 20, 2014 (H26-0320002).
Conflicts of interest statement
AK reports personal fees from Bayer Yakuhin, Ltd., as a member of the independent data
monitoring committee of clinical trials outside the submitted work. KS received research funding
from Ono Pharmaceutical, MSD, Celgene, AbbVie, Takeda Pharmaceutical, Sanofi, Bristol-Myers
Squibb, Daiichi Sankyo, Janssen, Novartis, Alexion Pharma, and GlaxoSmithKline and received
honoraria from Ono Pharmaceutical, Celgene, Takeda Pharmaceutical, and Bristol-Myers Squibb.
The other authors have no conflicts of interest to declare.
Funding sources
This study was supported by an operating expense grant for research from the National
Hospital Organization.

REFERENCES
1
2
3
4

5
6

7
8

9

10

11

12

13

Munshi NC, Longo DL, Anderson KC. Plasma cell disorders. In: Harrison’s Principles of Internal Medicine.
20th ed. New York:McGraw-Hill Medical;2018:793.
Brenner H, Gondos A, Pulte D. Recent major improvement in long-term survival of younger patients withmultiple myeloma. Blood. 2008;111:2521–2526. doi:10.1182/blood-2007-08-104984.
San Miguel JF, Schlag R, Khuageva NK, et al. Bortezomib plus melphalan and prednisone for initial
treatment of multiple myeloma. N Engl J Med. 2008;359:906–917. doi:10.1056/NEJMoa0801479.
Facon T, Dimopoulos MA, Dispenzieri A, et al. Final analysis of survival outcomes in the phase
3 FIRST trial of up-front treatment for multiple myeloma. Blood. 2018;131:301–310. doi:10.1182/
blood-2017-07-795047.
Niesvizky R, Flinn IW, Rifkin R, et al. Community-based phase IIIB trial of three UPFRONT bortezomibbased myeloma regimens. J Clin Oncol. 2015;33:3921–3929. doi:10.1200/JCO.2014.58.7618.
O’Donnell EK, Laubach JP, Yee AJ, et al. A phase 2 study of modified lenalidomide, bortezomib anddexamethasone in transplant-ineligible multiple myeloma. Br J Haematol. 2018;182:222–230. doi:10.1111/
bjh.15261.
Mateos MV, Martínez-López J, Hernández MT, et al. Sequential vs alternating administration of VMP and Rd
in elderly patients with newly diagnosed MM. Blood. 2016;127:420–425. doi:10.1182/blood-2015-08-666537.
Yokoyama A, Kada A, Saito AM, et al. Phase II Study of Treatment for Newly Diagnosed Multiple Myeloma
Patients Over 75 Years Old with Alternating Bortezomib/dexamethasone and Lenalidomide/dexamethasone:
the MARBLE Trial. Acta Med Okayama. 2019;73(6):547–552. doi:10.18926/AMO/57722.
Tucci A, Ferrari S, Bottelli C, et al. A comprehensive geriatric assessment is more effective than clinicaljudgment to identify elderly diffuse large cell lymphoma patients who benefit from aggressive therapy.
Cancer. 2009;115(19):4547–4553. doi:10.1002/cncr.24490.
Vesole DH, Jacobus S, Rajkumar SV, et al. Lenalidomide plus low-dose dexamethasone (Ld): Superior
one- and two-year survival regardless of age compared to lenalidomide plus high-dose dexamethasone (LD).
Blood. 2010;116:abstract 308. doi:10.1182/blood.V116.21.308.308.
Kumar S, Flinn I, Richardson PG, et al. Randomized, multicenter, phase 2 study (EVOLUTION) of
combinations of bortezomib, dexamethasone, cyclophosphamide, and lenalidomide in previously untreatedmultiple myeloma. Blood. 2012;119:4375–4382. doi:10.1182/blood-2011-11-395749.
Palumbo A, Avet-Loiseau H, Oliva S, et al. Revised International Staging System for Multiple Myeloma:
A Report From the International Myeloma Working Group. J Clin Oncol. 2015;33:2863–2869. doi:10.1200/
JCO.2015.61.2267.
Dingli D, Ailawadhi S, Bergsagel PL, et al. Therapy for relapsed multiple myeloma: Guidelines from the

Nagoya J. Med. Sci. 84. 80–90, 2022

89

doi:10.18999/nagjms.84.1.80

Akihiro Yokoyama et al

14

15

Mayo stratification for myeloma and risk-adapted therapy. Mayo Clin Proc. 2017;92:578–598. doi:10.1016/j.
mayocp.2017.01.003.
Hulin C, Facon T, Shustik C, et al. Effect of age on efficacy and safety outcomes in patients (Pts) with
newly diagnosed multiple myeloma (NDMM) receiving lenalidomide and low-dose dexamethasone (Rd):The first trial. Blood. 2014;124:81. doi:10.1182/blood.V124.21.81.81.
Niesvizky R, Flinn IW, Rifkin R, et al. Efficacy and safety of three bortezomib-based combinations inelderly, newly diagnosed multiple myeloma patients: Results from all randomized patients in the communitybased, phase 3b UPFRONT study. Blood. 2011;118:478. doi:10.1182/blood.V118.21.478.478.

References End

Nagoya J. Med. Sci. 84. 80–90, 2022

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doi:10.18999/nagjms.84.1.80

research-article2021

JHCXXX10.1369/00221554211048551Increased Cross-sectional Area of HEVs in AITLKogami et al.

ArticleJournal of Histochemistry & Cytochemistry 2021, Vol. 69(10) 645­–657© The Author(s) 2021
Article reuse guidelines:
sagepub.com/journals-permissions
https://doi.org/10.1369/00221554211048551
DOI: 10.1369/00221554211048551
journals.sagepub.com/home/jhc

The Conspicuousness of High Endothelial Venules inAngioimmunoblastic T-cell Lymphoma Is Due to Increased
Cross-sectional Area, Not Increased Distribution Density
Akiya Kogami, Mana Fukushima, Hitomi Hoshino, Takuya Komeno,
Tadakazu Okoshi, Masataka Murahashi, Tomoya O. Akama, Junya Mitoma,
Haruo Ohtani, and Motohiro Kobayashi

Department of Tumor Pathology, Faculty of Medical Sciences, University of Fukui, Eiheiji, Japan (AK, MF, HH, MM, MK); Omachi Municipal General
Hospital, Omachi, Japan (MF, MK); Department of Hematology, National Hospital Organization Mito Medical Center, Ibaraki, Japan (TK); Departmentof Pathology, Japanese Red Cross Fukui Hospital, Fukui, Japan (TO); Department of Pharmacology, Kansai Medical University, Hirakata, Japan (TOA);
Department of Medical Life Sciences, School of Medical Life Sciences, Kyushu University of Health and Welfare, Nobeoka, Japan (JM); and Departmentof Pathology, Mito Saiseikai General Hospital, Mito, Japan (HO)Summary
Angioimmunoblastic T-cell lymphoma (AITL) is a T-cell lymphoma of follicular helper T-cell origin. Histologically, neoplastic
T-cells proliferate to form clusters adjacent to or between arborizing high endothelial venules (HEVs). HEVs in normal
lymph nodes express sulfated glycans called peripheral lymph node addressin (PNAd); however, it remains unclear whether
PNAd is also expressed on HEVs in AITL. Furthermore, although it is widely accepted that HEVs are conspicuous in
AITL due to their proliferation, quantitative histological support for this concept is lacking. To investigate these issues,
we employed monoclonal antibodies recognizing PNAd, namely, MECA-79, HECA-452, and 297-11A, and performed
quantitative immunohistochemical analysis of HEVs in 36 AITL-affected and 67 normal lymph nodes. Staining with all
three antibodies confirmed that AITL HEVs express PNAd. Moreover, AITL HEVs were bound calcium-dependently
by L-selectin-IgM fusion proteins, indicating that they function in the recruitment of L-selectin-expressing lymphocytes.
Unexpectedly, HEV distribution density was not increased but rather decreased in AITL compared with normal lymph
nodes, but HEV cross-sectional area in AITL was significantly greater than that seen in normal lymph nodes. Overall, theseresults indicate that the prominence of AITL HEVs is likely due to increased cross-sectional area rather than increased
distribution density. (J Histochem Cytochem 69:645–657, 2021)
Keywords
CD34, N-acetyllactosamine, sialyl Lewis x (sLex)

Introduction
Angioimmunoblastic T-cell lymphoma (AITL) is one of
the most common subtypes of peripheral T-cell lymphoma (PTCL), accounting for roughly 20% of PTCL.1
Currently, neoplastic T-cells are thought to originate
from follicular helper T-cells, which, under normal conditions, play pivotal roles in the formation and maintenance of germinal centers and B-cell differentiation.2
In AITL, neoplastic T-cells proliferate against abackground consisting of reactive host immune cells,
including B-cells (often with a blast form), plasma cells,
Received for publication June 1, 2021; accepted September 7, 2021.
Corresponding Author:
Motohiro Kobayashi, Department of Tumor Pathology, Faculty of
Medical Sciences, University of Fukui, 23-3 Matsuoka-Shimoaizuki, Eiheiji,
Fukui 910-1193, Japan.
E-mail: motokoba@u-fukui.ac.jp

Journal of Histochemistry & Cytochemistry1048551

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Kogami et al.

Figure 1. Glycan structure of 6,6′-disulfo sLex attached to both extended core 1 and core 2-branched O-glycans, one of the most fully
glycosylated and sulfated forms of putative L-selectin ligands. Epitopes are shown for MECA-79, HECA-452, and 297-11A monoclonal
antibodies.1–3,5–8

eosinophils, and macrophages, as well as an irregularly
expanded meshwork of follicular dendritic cells (FDCs)
and arborizing high endothelial venules (HEVs).3,4HEVs are specialized venules composed of tall andplump endothelial cells located mainly in T-cell zones,
such as paracortical and interfollicular areas; however,some HEVs are also present in B-cell zones, particularly in the periphery of lymphoid follicles.5 Under
physiological conditions, HEVs function to recruit lymphocytes from the circulation to the lymphoid parenchyma. HEVs express a series of glycoproteins,
including CD34 decorated with sulfated sialyl Lewis x(sLex) glycans, collectively called peripheral lymph
node addressin (PNAd).6–8 Expression of PNAd in
HEV-like vessels is reportedly induced in various
chronic inflammatory diseases,9 and we also reported
such PNAd expression in chronic Helicobacter pylori
gastritis,10 ulcerative colitis,11 autoimmune pancreatitis,12 chronic prostatitis associated with benign prostatic hyperplasia,13 eosinophilic chronic rhinosinusitis(ECRS),14 and, most recently, oral lichen planus.15 The
relatively weak, transient adhesive interaction between
PNAd on HEVs (as well as HEV-like vessels) andL-selectin on circulating lymphocytes results in lymphocyte rolling along the luminal face of HEVs, followed by chemokine-dependent lymphocyte activation,

integrin-mediated lymphocyte firm attachment toendothelium, and platelet endothelial cell adhesion
molecule 1 (PECAM-1)-mediated lymphocyte transmigration across blood vessels.8,16
PNAd is often detected using the monoclonal antibody MECA-79,17 whose epitope has been shown tobe 6-sulfo [i.e. N-acetylglucosamine (GlcNAc)-6-Osulfated] N-acetyllactosamine (LacNAc) attached
to extended core 1 O-glycans, Galβ14(SO46)
GlcNAcβ13Galβ13GalNAcα1Ser/Thr (in which
Gal is galactose and GalNAc is N-acetylgalactosamine)(Fig. 1).18 This epitope overlaps with 6-sulfo sLex,
Siaα23Galβ14[Fucα13(SO46)]GlcNAcβ1R(in which Sia is sialic acid and Fuc is fucose), theL-selectin recognition determinant. This determinant
can be also detected by the monoclonal antibody
HECA-452, whose epitope has been shown tobe sLex tetrasaccharide, Siaα23Galβ14(Fucα13)
GlcNAcβ1R, regardless of GlcNAc- and/or Gal-6-Osulfation (Fig. 1).19–21 GlcNAc-6-O-sulfation of sLex moieties is required for optimal L-selectin binding and iscatalyzed cooperatively by GlcNAc-6-O-sulfotransferase
1 (GlcNAc6ST-1) and GlcNAc-6-O-sulfotransferase 2(GlcNAc6ST-2).22,23 On the contrary, HEVs in peripheral
lymph nodes also express sialyl 6′-sulfo (i.e., Gal-6-Osulfated) LacNAc-capped O-glycans, Siaα23(SO46)

Increased Cross-sectional Area of HEVs in AITL
Galβ14GlcNAcβ1R, which can be detected withthe antibody 297-11A (Fig. 1), which we recently developed.24 Gal-6-O-sulfation of sLex moieties also comprises an L-selectin-binding determinant in vitro25 andis catalyzed by keratan sulfate Gal-6-O-sulfotransferase(KSGal6ST).26 However, it remains to be determined
whether these sulfated sLex-related glycans are also
expressed in HEVs formed in AITL. Furthermore,
although it is widely accepted that HEV proliferation isa histological hallmark of AITL, to our knowledge, there
are no reports of quantitative histological analysis of
HEVs to confirm this idea.
This study consists of two parts: functional analysisof the carbohydrate moiety of PNAd expressed in
HEVs, and quantitative and morphometric analysis of
HEVs. First, we conducted immunohistochemical
staining with a battery of anti-PNAd monoclonal antibodies and confirmed that sulfated sLex, which isexpressed in normal lymph node HEVs, is also
expressed in HEVs formed in AITL. Then, we used
selectin-IgM fusion proteins to determine whether sulfated sLex expressed in these HEVs potentially functions as an L-selectin ligand. In the second part of ouranalysis, we asked whether HEV distribution density(specifically, the number per unit area) in AITL differedfrom that seen in normal lymph nodes and how HEVcross-sectional area in AITL compared with that seen
in normal lymph nodes. Our analysis indicated overallthat HEVs formed in AITL show increased cross-sectional area, rather than increased distribution density.

Materials and Methods
Human Tissue Samples
Formalin-fixed, paraffin-embedded (FFPE) tissue blocksof biopsied lymph nodes with a diagnosis of AITL wereobtained from the pathology archives of University of
Fukui Hospital (Eiheiji, Japan), National Hospital
Organization Mito Medical Center (Ibaraki, Japan),Omachi Municipal General Hospital (Omachi, Japan),and the Japanese Red Cross Fukui Hospital (Fukui,Japan). Using hematoxylin and eosin (H&E) staining
with adjunctive immunohistochemical staining for CD3(rabbit polyclonal, cat. no. A0452; Dako, Glostrup,
Denmark), CD4 (mouse monoclonal IgG1, clone 1F6,
cat. no. NCL-CD4-1F6; Leica Biosystems, Newcastle
Upon Tyne, UK), CD10 (mouse monoclonal IgG1, clone
56C6, cat. no. 413261; Nichirei Biosciences, Tokyo,
Japan), CD20 (mouse monoclonal IgG2a, clone L26,
cat. no. M0755; Dako), CD21 (mouse monoclonal IgG1,
clone 1F8, cat. no. M0784; Dako), BCL6 (mouse monoclonal IgG1, clone P1F6, cat. no. 413761; Nichirei
Biosciences), CXCL13 (goat polyclonal, cat. no. AF801;

647
R&D Systems, Minneapolis, MN), and CXCR5 (rabbit
polyclonal, cat. no. GTX100351; GeneTex, Irvine, CA),histological diagnosis of AITL was re-evaluated by certified pathologists (AK, MF, HO and MK) according to thelatest edition of the World Health Organization (WHO)classification (revised 4th edition).4 From that analysis,
we selected specimens with histological findings in best
agreement with AITL (n=36 from 19 patients; mean age,
73.7 ± 12.2 years; male:female = 12:7). FFPE tissue
blocks of surgically resected axillary lymph nodes negative for metastasis (n=67 from 19 patients; mean age,59.2 ± 12.8 years; male:female = 1:18) were collected
as controls. Analysis of human lymph node tissues wasapproved by the Ethics Committee of the Faculty of
Medical Sciences, University of Fukui (reference number 20160100, approved on December 7, 2016; reference number 20200193, approved on April 14, 2021).

Monoclonal Antibodies
The following monoclonal antibodies were used as
primary antibodies: QBEND10 (mouse IgG1, cat. no.
IM0786; Immunotech, Marseille, France) recognizing
human CD34, a marker of vascular endothelial cells27;
MECA-79 (rat IgM, cat. no. 553863; BD Biosciences,Franklin Lakes, NJ)17; HECA-452 (rat IgM, cat. no.
550407; BD Biosciences)19; and 297-11A (mouse
IgM).24 Epitopes for MECA-79, HECA-452, and 29711A are shown in Fig. 1.

Expression of MECA-79 and 297-11A Epitopes
in Lec2 Cells
To express the minimum epitope of MECA-79, Lec2
cells (obtained from American Type Culture Collection,
Manassas, VA), a mutant CHO line that lacks sialic
acid modification due to inactivation of the Golgi
CMP-sialic acid transporter,28 were co-transfectedwith pcDNA1-GlcNAc6ST-229,30 and pcDNA3-β1,3-Nacetylglucosaminyltransferase 3 (β3GlcNAcT-3)18(the latter harboring a neomycin resistance gene) at a
9:1 ratio using Lipofectamine Plus (Thermo Fisher
Scientific; Waltham, MA) according to the manufacturer’s instructions. We then selected stable transformants in 400 µg/ml of Geneticin (Sigma-Aldrich; St.
Louis, MO). Cells positive for MECA-79 were cloned
by limited dilution, resulting in Lec2/β3GlcNAcT-3/
GlcNAc6ST-2 cells. Cells were then transiently transfected with either pcDNA3-KSGal6ST26 or empty
pcDNA3 (mock). After 36 hr, cells were dissociatedinto monodispersed cells using phosphate-bufferedsaline (PBS) containing 0.5 mM ethylenediaminetetraacetic acid (EDTA) and then doubly immunostained

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with MECA-79 and 297-11A. Expression of both glycoepitopes was assayed simultaneously by flow cytometry using an FACSCanto II (BD Biosciences) with
FlowJo software (Tree Star; Ashland, OR).

exclusively to non-sulfated sLex,20,21 as a means todetect 6-sulfo sLex only.

Immunohistochemistry

Immunostained slides were scanned with NanoZoomer
RS (Hamamatsu Photonics; Hamamatsu, Japan) toobtain whole slide images. The area of each lymph
node (mm2) and the number of vessels positive for
CD34, MECA-79, HECA-452, or 297-11A in the entire
area of each lymph node were determined using NDP.
view2 Plus software (Hamamatsu Photonics). We
then divided the number of vessels positive for CD34,
MECA-79, HECA-452, or 297-11A in each lymph nodeby the area of corresponding lymph node to calculate
the distribution density of vessels of interest. In addition, the number of HEVs positive for MECA-79,
HECA-452, or 297-11A was divided by the number of
CD34-positive vessels to determine their proportion in
total vessels. To determine the mean cross-sectional
area of one HEV, as well as the total cross-sectional
area of HEVs present in a unit area (1 mm2), regions
in H&E-stained sections with densely distributed
HEVs were selected from five cases each of AITLaffected or normal lymph nodes, and for each HEV
present in a region, we determined the inner area surrounded by the basement membrane.

Immunohistochemical staining for MECA-79 andHECA-452 was conducted using an indirect method
as described,14 and staining for CD34 and 297-11A
was carried out using the Histofine system (Nichirei
Biosciences), as per the manufacturer’s instructions.
For 297-11A staining, to remove sialic acid, which
inhibits 297-11A binding, sections were preincubatedat 37C for 90 min in a humidified chamber with 0.2 U/ml
neuraminidase from Arthrobacter ureafaciens (Nacalai
Tesque; Kyoto, Japan) dissolved in 50 mM sodium
acetate (pH 5.2).31 Double immunofluorescence staining for MECA-79 and 297-11A was performed essentially as described.32

L- and E-Selectin-IgM Chimera In Situ Binding
Assays
To obtain L- or E-selectin-IgM chimeric proteins,
COS-1 cells were transiently transfected with
pcDNA1.1-L-selectin•IgM or pcDNA1.1-E-selectin•IgM,
respectively.10 Transfected cells were cultured 3 days
and conditioned media were recovered. L- andE-selectin-IgM binding to CHO cells expressing nonsulfated sLex attached to core 2-branched O-glycans(named CHO/CD34/F7/C2) and to those expressing
its GlcNAc-6-O-sulfated form (designated CHO/
CD34/F7/C2/LSST)31 was assayed by flow cytometry,as described.21,33 L- and E-selectin-IgM chimera insitu binding assays on FFPE tissue sections were carried out as described,10 with modification. In brief,
after quenching endogenous peroxidase activity andblocking nonspecific protein binding, sections wereincubated with conditioned media containing L- or
E-selectin-IgM chimeras for 30 min and washed with
Dulbecco’s modified Eagle medium (DMEM; Thermo
Fisher Scientific). Sections were then incubated 30
min with horseradish peroxidase (HRP)-conjugatedgoat anti-human IgM antibody (Millipore; Billerica,MA) and washed with DMEM. The color reaction wasdeveloped using Betazoid DAB Chromogen Kit(Biocare Medical; Pacheco, CA). Sections were briefly
counterstained with hematoxylin. Negative controls
were washed using DMEM supplemented with 5 mM
EDTA to chelate calcium ions. In the E-selectin-IgMchimera in situ binding assay, sections were preincubated with CSLEX1 monoclonal antibody (mouse
IgM, cat. no. 551344; BD Biosciences), which binds

Quantification of HEVs

Statistical Analysis
Data are expressed as mean ± SD. Differences
between groups were statistically analyzed by twotailed unpaired t-test using GraphPad Prism 7 software(GraphPad Software; La Jolla, CA). P values less than
0.05 were considered significant.

Results
HEVs Formed in AITL Express Sulfated sLex
That Constitutes PNAd
AITL cases examined here showed histopathologicalfeatures typical of AITL,4 in which lymphoma cells proliferate throughout the lymph node, and normal lymph
node architecture is effaced (Fig. 2A). Against a background of reactive host immunoblasts, plasma cells,
and eosinophils, small to medium-sized lymphocyteswith clear-to-pale cytoplasm (so-called clear cells)
proliferate to form clusters adjacent to or between
arborizing HEVs (Fig. 2B). Normal lymph node HEVs
reportedly constitutively express a series of glycoproteins, including CD34 decorated with sulfated sLex,
which constitutes PNAd.6,7 To determine whether

Increased Cross-sectional Area of HEVs in AITL

649

Figure 2. Immunohistochemical profiles of HEVs formed in AITL. (A, B) Histology of AITL. Normal lymph node architecture is effaced
due to proliferation of lymphoma cells throughout the lymph node (A). Lymphoma cells with “clear cell” features proliferate to form
clusters adjacent to or between arborizing HEVs (B). H&E staining. (C–F) HEVs formed in AITL stain positively for CD34 (C), MECA79 (D), HECA-452 (E), and 297-11A (F). Signals were visualized with 3,3’-diaminobenzidine (DAB) (brown), and tissues were counterstained with hematoxylin. Bar = 400 µm for panel A and 40 µm for the rest. Abbreviations: HEVs, high endothelial venules; AITL,
angioimmunoblastic T-cell lymphoma; H&E, hematoxylin and eosin.

Figure 3. Dual immunofluorescence of HEVs formed in AITL for 297-11A (red) and MECA-79 (green). Yellow signals in Merged indicate colocalization of both carbohydrate antigens. Bar = 40 µm. Abbreviations: HEVs, high endothelial venules; AITL, angioimmunoblastic
T-cell lymphoma.

HEVs formed in AITL are decorated with sulfatedsLex, we conducted immunohistochemical staining for
CD34, MECA-79, HECA-452, and 297-11A. In addition
to CD34 (Fig. 2C), HEVs formed in AITL were positive
for MECA-79 (Fig. 2D), and a proportion were also
positive for HECA-452 (Fig. 2E) or 297-11A (Fig. 2F)(see “The Proportion of HEVs in Total Blood Vessels
Does Not Increase in AITL” below for detailed quantitative data). Given the glycoepitopes recognized bythese antibodies (see Fig. 1), this result indicatesthat as in normal lymph node, HEVs formed in AITL
express sulfated sLex.

Gal-6-O-sulfation of LacNAc in the MECA-79
Epitope Does Not Inhibit Reactivity to MECA-79
We then conducted double immunofluorescence staining of AITL tissues with MECA-79 and 297-11A anti­
bodies and found that, overall, 297-11A and MECA-79
signals on HEVs colocalized (Fig. 3), a pattern described

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Kogami et al.

Figure 4. Gal-6-O-sulfation of LacNAc in the MECA-79 epitope does not inhibit reactivity to MECA-79. (A) Stable expression of the MECA79 minimum epitope in Lec2/β3GlcNAcT3/GlcNAc6ST-2 cells (filled histogram in right panel). Filled histogram in the left panel and open
histogram in the right panel represent negative controls in which the primary antibody was replaced with isotype-matched immunoglobulin.
X- and Y-axes indicate fluorescence intensity and number of events, respectively. Note that cells expressing the MECA-79 epitope are distributed normally, indicating that they are of a single clone. (B) Lec2/β3GlcNAcT-3/GlcNAc6ST-2 cells were transiently transfected with empty
vector (mock; upper panels) or KSGal6ST cDNA (lower panels), and then doubly immunostained with MECA-79 and 297-11A (rightpanels). Left panels represent negative controls replacing primary antibodies with isotype-matched immunoglobulins. X- and Y-axes indicate fluorescence intensity originating from MECA-79 and 297-11A, respectively. Abbreviations: LacNAc, N-acetyllactosamine; cDNA,complementary DNA.

Increased Cross-sectional Area of HEVs in AITL
in normal lymph node HEVs.24 The minimum MECA-79
epitope is reportedly 6-sulfo LacNAc attached toextended core 1 O-glycans, and it is known that α2,3sialylated and/or α1,3-fucosylated forms of this structure are also recognized by MECA-79 antibody (seeFig. 1).18 To determine whether 6′-O-sulfation (Gal-6-Osulfation) of LacNAc in the MECA-79 epitope alters
its reactivity to MECA-79, we first established Lec2/
β3GlcNAcT-3/GlcNAc6ST-2 cells, which express theminimum MECA-79 epitope (Fig. 4A). We then transfected these cells with either KSGal6ST cDNA to
express the 297-11A epitope in addition to the MECA-79
epitope or the empty vector (mock) and then evaluatedthe expression levels of both epitopes simultaneously
by flow cytometry. As shown in Fig. 4B (right upper
panel), 29.8% (29.7% + 0.1%) of mock-transfected cells
were MECA-79-positive, whereas in cells transfectedwith KSGal6ST cDNA, 33.3% (28.1% + 5.2%) of cells
were MECA-79-positive (Fig. 4B, right lower panel).
Importantly, expression levels of the MECA-79 epitopein the 297-11A-positive cell population were not attenuated relative to those seen in the 297-11A-negative cell
population (Fig. 4B, right lower panel). This result indicates that Gal-6-O-sulfation of LacNAc—that is, formation of the 297-11A epitope—in the MECA-79 epitope
does not inhibit reactivity to MECA-79.

Sulfated sLex Expressed in AITL HEVs Potentially
Functions as an L-Selectin Ligand
Although both L- and E-selectin reportedly bind to
6-sulfo sLex,33,34 binding affinity to 6-sulfo sLex of IgM
fusion proteins of these two selectins has not been
fully evaluated. Thus, we first assayed their binding
affinity to 6-sulfo sLex by flow cytometry using CHO
cells expressing 6-sulfo sLex, as well as control CHO
cells expressing non-sulfated sLex. As shown in Fig.
5A (blue histograms), L-selectin-IgM chimeras did
not bind to CHO cells expressing non-sulfated sLex
(upper panel) but did bind to CHO cells expressing
6-sulfo sLex, although the signal intensity was weak(lower panel). On the contrary, E-selectin-IgM chimeras showed comparable binding to CHO cells
expressing either non-sulfated sLex or 6-sulfo sLex
(Fig. 5A, red histograms). It should be noted that
E-selectin-IgM chimeras bound to 6-sulfo sLex more
readily than did L-selectin-IgM chimeras (Fig. 5A,
lower panel). This finding indicates that, at least in
vitro, E-selectin-IgM chimeras possess higher capacity to bind to 6-sulfo sLex than do L-selectin-IgM chimeras and thus could serve as a sensitive functional
probe for 6-sulfo sLex.
We then conducted L- and E-selectin-IgM chimerain situ binding assays to investigate whether sulfatedsLex expressed in AITL HEVs functions as an L-selectin

651
ligand. Both L-selectin-IgM (Fig. 5B) and E-selectinIgM (Fig. 5C) bound calcium-dependently not only to
HEVs in normal lymph nodes but also to those formedin AITL, although the signal intensity of the L-selectinIgM chimera in AITL HEVs (Fig. 5B, arrows) was muchless than that seen in normal lymph node HEVs. It isnoteworthy that E-selectin-IgM chimera binding tothese HEVs was preserved even after incubation oftissue sections with anti-non-sulfated sLex antibody
CSLEX1 (Fig. 5C, with CSLEX1). These findings collectively indicate that HEVs formed in AITL possess
some selectin-binding ability, albeit limited.

HEV Distribution Density in AITL Decreases
Rather Than Increases
Given reports that vascularity in AITL often increases
as does HEV arborization,4 we investigated thesechanges by performing quantitative immunohistochemical analysis of HEVs using 36 AITL-affected
lymph nodes and 67 normal axillary lymph nodes ascontrols. To calculate vessel distribution density, specifically, the number of vessels per unit area, we first
determined the area of each lymph node. Not surprisingly, the area of AITL-affected lymph nodes (52.2 ±59.7 mm2) was greater than that of normal lymph nodes(19.3 ± 19.8 mm2) with high statistical significance(p<0.001) (Fig. 6A). Consequently, the distribution density of total vessels in AITL (62.5 ± 20.6/mm2) was lessthan that of normal lymph nodes (110.8 ± 35.4/mm2)
with high statistical significance (p<0.001) (Fig. 6B). As
for HEVs, the distribution density of MECA-79-positive
HEVs in AITL (31.0 ± 16.1/mm2) was less than that ofnormal lymph nodes (47.8 ± 14.9/mm2) with high statistical significance (p<0.001) (Fig. 6C). Similarly, the distribution density of both HECA-452-positive and297-11A-positive HEVs in AITL (14.2 ± 8.9/mm2 and
7.8 ± 12.4/mm2, respectively) was less than that seen in
normal lymph nodes (30.2 ± 13.1/mm2 and 17.3 ± 19.9/mm2,
respectively) with statistical significance (p<0.001 and
p=0.011, respectively) (Fig. 6D and E, respectively).
These findings indicate that rather than increasing, vascularity in AITL decreases relative to normal lymph
nodes, as does HEV distribution density.

The Proportion of HEVs in Total Blood Vessels
Does Not Increase in AITL
We next assessed the proportion of HEVs in total
vessels. As shown in Fig. 7A, the proportion of MECA79-positive HEVs among CD34-positive total vessels in
AITL (50.2% ± 22.2%) and in normal lymph nodes(46.2% ± 17.1%) was comparable (p=0.320). Similarly,
the proportion of 297-11A-positive HEVs among total
vessels in AITL (11.9% ± 17.3%) and in normal lymph

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Kogami et al.

Figure 5. L- and E-selectin-IgM chimera binding assays. (A) Flow cytometric analysis of L-selectin-IgM (blue histograms) and E-selectinIgM (red histograms) chimera binding to CHO cells expressing non-sulfated sLex (upper panel) or 6-sulfo sLex (lower panel). Blackhistograms represent negative controls performed using conditioned medium from untransfected COS-1 cells. X- and Y-axes indicate
fluorescence intensity and number of events, respectively. (B, C) L-selectin-IgM (B) and E-selectin-IgM (C) in situ binding assays on FFPE
tissue sections of normal (upper panels) and AITL-affected (lower panels) lymph nodes. Both L- and E-selectin-IgM chimeras bind to
HEVs in the presence of calcium ions (without EDTA), but binding is completely abrogated in the absence of calcium ions (with EDTA).
Note very weak L-selectin-IgM binding to HEVs formed in AITL (arrows). E-selectin-IgM chimera binding to HEVs is maintained evenafter incubating sections with CSLEX1 antibody, which binds exclusively to non-sulfated sLex (with CSLEX1). Signals were visualized withDAB (brown), and tissues were counterstained with hematoxylin. Bar = 40 µm. Abbreviations: FFPE, formalin-fixed, paraffin-embedded;
AITL, angioimmunoblastic T-cell lymphoma; HEVs, high endothelial venules; EDTA, ethylenediaminetetraacetic acid.

Increased Cross-sectional Area of HEVs in AITL

653

Figure 6. Distribution density of HEVs. (A) The size (area) of normal (n=67; open boxes) and AITL-affected (n=36; filled boxes) lymph
nodes. (B–E) Distribution density of vessels immunoreactive for CD34 (B), MECA-79 (C), HECA-452 (D), or 297-11A (E) in normaland AITL-affected lymph nodes. Data are presented as means with SD. Abbreviations: HEVs, high endothelial venules; AITL, angioimmunoblastic T-cell lymphoma. *p < 0.05; ***p < 0.001.

Figure 7. The proportion of HEVs immunoreactive for MECA-79 (A), HECA-452 (B), or 297-11A (C) among CD34-positive total
vessels in normal (n=67; open boxes) and AITL-affected (n=36; filled boxes) lymph nodes. Data are presented as means with SD.
Abbreviations: HEVs, high endothelial venules; AITL, angioimmunoblastic T-cell lymphoma; NS, not significant. **p<0.01.

nodes (15.6% ± 14.7%) did not differ significantly(p=0.249) (Fig. 7C). These findings indicate that among
total vessels, the proportion of HEVs is comparable in
AITL and normal lymph nodes. Interestingly, however,the proportion of HECA-452-positive HEVs among total
vessels in AITL (22.1% ± 10.9%) was smaller than thatseen in normal lymph nodes (28.7% ± 12.2%) with statistical significance (p=0.008) (Fig. 7B).

HEV Cross-sectional Area Increases in AITL
We next performed a careful review of H&E-stained
tissue specimens and found that HEVs formed in AITL

(Fig. 8B) were composed of higher endothelial cellswith more plump cytoplasm and a larger cross-sectional area than those seen in normal lymph nodes(Fig. 8A). Also, it is noteworthy that in some AITL HEVs,
neoplastic “clear cell” T-cells infiltrate the space
between endothelial cells and basement membrane
and appear to push the basement membrane outward,
resulting in increased cross-sectional area of HEVs
(Fig. 8C). Thus, we first measured the cross-sectional
area of every single HEV in AITL-affected and normal
lymph nodes. As shown in Fig. 8D, the mean crosssectional area of an HEV in AITL (0.0014 ± 0.0012 mm2)
was greater than that seen in normal lymph nodes

654

Kogami et al.

Figure 8. Increased cross-sectional area of HEVs in AITL-affected lymph nodes. (A–C) Photomicrographs of HEVs in normal (A) and
AITL-affected (B and C) lymph nodes. Panel C is an enlarged view of the region indicated by asterisk in panel B. Note that in AITL,
neoplastic T-cells (arrows) and reactive immunoblasts infiltrate the space between endothelial cells (arrowheads) and the basement
membrane (dotted lines) likely pushing the basement membrane outward and resulting in increased cross-sectional area of HEVs. H&E
staining. Bar = 40 µm for panels A and B and 20 µm for panel C. (D) The mean cross-sectional area of one HEV in normal (n=5; openbox) and AITL-affected (n=5; filled box) lymph nodes. (E) The total cross-sectional area of HEVs present in a unit area (1 mm2) of normal(n=5) and AITL-affected (n=5) lymph nodes. (F) The number of HEVs per unit area in the region assessed in (E) in normal (n=5) and
AITL-affected (n=5) lymph nodes. Data are presented as means with SD. Abbreviations: HEVs, high endothelial venules; AITL, angioimmunoblastic T-cell lymphoma; NS, not significant. *p<0.05, ***p<0.001.

(0.0008 ± 0.0010 mm2) with high statistical significance (p<0.001). We then calculated the total crosssectional area of HEVs present in a unit area
(1 mm2) of lymph nodes. As shown in Fig. 8E, that areain AITL (0.062 ± 0.015 mm2) was greater than thatseen in normal lymph nodes (0.035 ± 0.015 mm2) withstatistical significance (p=0.019), whereas the number
of HEVs present in the same region did not differ
between normal and AITL-affected lymph nodes (41.6± 8.1/mm2 and 44.8 ± 8.4/mm2, respectively; p=0.558)(Fig. 8F).

Discussion
In this study, we demonstrate that HEVs formed in AITL
express sulfated sLex that constitutes PNAd and potentially functions as an L-selectin ligand. Moreover, wedemonstrate that the distribution density of HEVs does
not increase but rather decrease in AITL comparedwith normal lymph nodes, whereas the proportion of
HEVs in total blood vessels is essentially comparable
between AITL and normal lymph nodes. On the contrary, we found that the cross-sectional area of HEVs in
AITL was significantly greater than that seen in normal
lymph nodes. These results overall indicate that theapparent increase in PNAd-expressing functional
HEVs in AITL is likely due to increased cross-sectionalarea, not to increased distribution density.
HEV proliferation is a hallmark of AITL, as is
described in the latest edition of the WHO classification.4 Strikingly, however, here we found that the distribution density of not only total vessels but also HEVs
in AITL was significantly less than that seen in normal
lymph nodes. These findings suggest that AITL lymph
nodes become enlarged due to proliferation of neoplastic T-cells and recruitment and/or proliferation ofreactive host immune cells, possibly in response tohumoral factors secreted by neoplastic T-cells,3whereas stromal components including HEVs do notincrease, or even if they do, that increase is not to thesame extent as the tumor parenchyma. Nevertheless,some pathologists report that HEVs apparently
increase in number in AITL,1 a perception that we attribute to increased HEV cross-sectional area. This phenomenon could be due to increased expression oflymphotoxin αβ (LT-αβ) secreted by neoplastic T-cells
or reactive immunoblastic B-cells.35 As LT-β receptor
signaling is important for the formation and maintenance of HEV morphology, including endothelial cell
cuboidal shape and vessel arborization,36 increased

Increased Cross-sectional Area of HEVs in AITL
signaling in this pathway may contribute to the unusualmorphology of HEVs seen in AITL. Alternatively, in
AITL, neoplastic “clear cell” T-cells often proliferate toform clusters surrounding HEVs, and in some HEVs
infiltrate the space between endothelial cells andbasement membrane to expand the area surroundedby the basement membrane.
Overall, we feel our findings could be helpful in diagnosing AITL. Occasionally, tissue specimens that havea strong probability of being AITL lack prominent HEVs.
In such cases, it is important to understand that it is not
the number of HEVs that increases in AITL but rather
their cross-sectional area. A consideration of this finding might prevent pathologists from erroneously rulingout a diagnosis of AITL in these circumstances.
Muramatsu37 has reported that the number and proportion of HEVs, which were identified morphologically based on H&E and silver staining, weresignificantly greater in AITL than in normal lymph
nodes. This result is inconsistent with our results
reported here, potentially due to different methods
used to identify HEVs. In the Muramatsu study, vessels with a diameter of less than 15 µm, even if they
were composed of high endothelial cells, were classified as capillaries rather than HEVs. Thus, a substantial portion of small-sized PNAd-expressing vesselsthat we categorize as HEVs here might not have beenclassified as HEVs in the other study. As these vessels
are frequently found in normal lymph nodes, it is plausible that the number of HEVs in normal lymph nodes
was underestimated, resulting in an apparent relative
increase in the number of HEVs in AITL.
We also demonstrate that HEVs formed in AITL
express sulfated sLex recognized by MECA-79, andsome are also positive for HECA-452 and 297-11A. Thisis an immunohistochemical profile similar to that seen
in normal peripheral lymph node HEVs.24 We previously demonstrated that sulfated sLex is also expressed
in HEVs formed in lymphoid stroma of salivary Warthin’stumor,38 testicular seminoma,39 and a diffuse sclerosing variant of papillary thyroid carcinoma.40 These
HEVs presumably function in the histogenesis of distinct lymphoid stroma formed in these tumors. However,
HEV function in AITL histogenesis remains uncharacterized. As AITL is composed not only of neoplastic
T-cells but also of a variety of reactive host immune
cells, it is tempting to speculate that HEVs formed in
AITL may function to recruit these reactive host immune
cells, particularly those expressing high levels of
L-selectin such as naïve T- and B-cells.5 Further studies are, however, required to test this hypothesis.
We also show for the first time that a Gal-6-Osulfated form of the MECA-79 epitope is recognizedby this antibody. Double immunofluorescence staining
revealed that 297-11A signals on HEVs formed in AITL

655
colocalized with MECA-79 signals, as is seen in HEVs
in normal peripheral lymph nodes.24 Furthermore,
flow cytometric analysis revealed that Gal-6-Osulfation of LacNAc, that is, formation of the 297-11A
epitope, in the MECA-79 epitope does not attenuate
reactivity to MECA-79. In addition, we previously demonstrated by glycan array analysis that GlcNAc-6-Osulfation of Gal-6-O-sulfated LacNAc does not alter
reactivity to 297-11A.24 These findings overall indicatethat KSGal6ST catalyzes Gal-6-O-sulfation of 6-sulfo
LacNAc attached to extended core 1 O-glycans toform 6,6′-disulfo (both GlcNAc- and Gal-6-O-sulfated)
LacNAc attached to extended core 1 O-glycans, which
can be detected by both MECA-79 and 297-11A.
In in situ binding assay performed here, normal
lymph node HEVs were readily bound by L-selectinIgM chimeras. By contrast, HEVs formed in AITL were
decorated very weakly with this chimera, indicating
substantially diminished L-selectin-binding capacity of
HEVs formed in AITL. On the contrary, E-selectin-IgMchimeras bound readily to HEVs in normal lymph
nodes and relatively well to those formed in AITL. As
E-selectin-IgM chimera binding was not abolished by
pretreatment of tissue sections with CSLEX1 antibody,
it is reasonable to conclude that E-selectin-IgM chimera binding to HEVs is mediated by 6-sulfo sLex.
Thus, we propose that HEVs formed in AITL likely possess potential L-selectin-binding properties, albeit to alimited extent.
In this study, although the proportion of MECA-79positive and 297-11A-positive HEVs in total vessels did
not differ significantly between AITL-affected and normal lymph nodes, the proportion of HECA-452-positive
HEVs in total vessels was significantly smaller in AITL
compared with normal lymph nodes. Given the glycoepitopes recognized by MECA-79, 297-11A, and HECA452 antibodies (see Fig. 1), this result suggests that
GlcNAc-α1,3-fucosylation in sLex moieties is downregulated in AITL compared with normal lymph nodes.
Hiraoka et al.41 previously demonstrated that KSGal6ST
competes with α1,3-fucosyltransferase 7 (FucT-7) for
the same acceptor substrate, namely, LacNAc, anddownregulates synthesis of 6-sulfo sLex by inhibiting
α1,3-fucosylation. As GlcNAc-α1,3-fucosylation of
6-sulfo sLex moieties is a prerequisite for selectinbinding,42 L-selectin-binding capacity of HEVs may bedecreased in AITL. This hypothesis is supported by thefinding that both L- and E-selectin-IgM chimera binding
to HEVs decreased in AITL, although the significanceof this finding remains to be determined.
Acknowledgments
The authors thank Hisataka Kato, Takuya Suga, Shinichi
Fujii, and Moriyasu Hattori for preparation of tissue sections;
Maiko Yamanaka for immunohistochemical staining; Junko

656
Yamamoto for assisting with flow cytometry; Dr. Akifumi
Muramoto for helpful discussion on statistical analysis; andDr. Elise Lamar for critical reading of the manuscript.

Competing Interests
The author(s) declared no potential conflicts of interest withrespect to the research, authorship, and/or publication ofthis article.

Author Contributions
All authors have contributed to this article as follows: AK
designed and performed the research, analyzed the data,
and wrote the manuscript; MF, HH, TK, and TO performed
the research; MM wrote the manuscript; TOA and JM performed the research; HO analyzed the data and wrote
the manuscript; and MK conceived of and designed theresearch, analyzed the data, and wrote the manuscript. All
authors have read and approved the final manuscript.

Funding
The author(s) disclosed receipt of the following financial
support for the research, authorship, and/or publication ofthis article: This work was supported in part by a Grant-inAid for Scientific Research (B) 21H02702 from the Japan
Society for the Promotion of Science (to M.K.).

Literature Cited
1. Chiba S, Sakata-Yanagimoto M. Advances in understanding of angioimmunoblastic T-cell lymphoma.
Leukemia. 2020;34(10):2592–606.
2. Ueno H, Banchereau J, Vinuesa CG. Pathophysiology
of T follicular helper cells in humans and mice. Nat
Immunol. 2015;16(2):142–52.
3. Ohtani H, Komeno T, Agatsuma Y, Kobayashi M,
Noguchi M, Nakamura N. Follicular dendritic cell
meshwork in angioimmunoblastic T-cell lymphoma ischaracterized by accumulation of CXCL13+ cells. J Clin
Exp Hematop. 2015;55(2):61–9.
4. Dogan A, Gaulard P, Jaffe ES, Müller-Hemelink HK, deLeval L. Angioimmunoblastic T-cell lymphoma and othernodal lymphomas of T follicular helper cell origin. In:
Swerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA,
Stein H, Thiele J, editors. WHO classification of tumoursof haematopoietic and lymphoid tissues. Revised 4th
ed. Lyon: International Agency for Research on Cancer;
2017. p. 407–12.
5. Miyasaka M, Tanaka T. Lymphocyte trafficking acrosshigh endothelial venules: dogmas and enigmas. Nat
Rev Immunol. 2004;4(5):360–70.
6. Berg EL, Robinson MK, Warnock RA, Butcher EC. The
human peripheral lymph node vascular addressin is aligand for LECAM-1, the peripheral lymph node homing
receptor. J Cell Biol. 1991;114(2):343–9.
7. Michie SA, Streeter PR, Bolt PA, Butcher EC, Picker LJ.
The human peripheral lymph node vascular addressin.
An inducible endothelial antigen involved in lymphocyte
homing. Am J Pathol. 1993;143(6):1688–98.

Kogami et al.
8. Butcher EC, Picker LJ. Lymphocyte homing and homeostasis. Science. 1996;272(5258):60–6.
9. Aloisi F, Pujol-Borrell R. Lymphoid neogenesis inchronic inflammatory diseases. Nat Rev Immunol. 2006;
6(3):205–17.
10. Kobayashi M, Mitoma J, Nakamura N, Katsuyama T,
Nakayama J, Fukuda M. Induction of peripheral lymph
node addressin in human gastric mucosa infected by
Helicobacter pylori. Proc Natl Acad Sci U S A. 2004;
101(51):17807–12.
11. Suzawa K, Kobayashi M, Sakai Y, Hoshino H, Watanabe
M, Harada O, Ohtani H, Fukuda M, Nakayama J.
Preferential induction of peripheral lymph nodeaddressin on high endothelial venule-like vessels in theactive phase of ulcerative colitis. Am J Gastroenterol.
2007;102(7):1499–509.
12. Maruyama M, Kobayashi M, Sakai Y, Hiraoka N, Ohya
A, Kageyama S, Tanaka E, Nakayama J, Morohoshi
T. Periductal induction of high endothelial venule-likevessels in type 1 autoimmune pancreatitis. Pancreas.
2013;42(1):53–9.
13. Inamura S, Shinagawa T, Hoshino H, Sakai Y, Imamura
Y, Yokoyama O, Kobayashi M. Appearance of highendothelial venule-like vessels in benign prostatic
hyperplasia is associated with lower urinary tract symptoms. Prostate. 2017;77(7):794–802.
14. Tsutsumiuchi T, Hoshino H, Fujieda S, Kobayashi M.
Induction of peripheral lymph node addressin in humannasal mucosa with eosinophilic chronic rhinosinusitis.
Pathology. 2019;51(3):268–73.
15. Yoshida H, Imamura Y, Yoshimura H, Kobayashi M.
Induction of high endothelial venule-like vessels in oral
and cutaneous lichen planus: a comparative study. J
Histochem Cytochem. 2020;68(5):343–50.
16. Muller WA, Weigl SA, Deng X, Phillips DM. PECAM-1 isrequired for transendothelial migration of leukocytes. J
Exp Med. 1993;178(2):449–60.
17. Streeter PR, Rouse BT, Butcher EC. Immunohistologic
and functional characterization of a vascular addressin
involved in lymphocyte homing into peripheral lymph
nodes. J Cell Biol. 1988;107(5):1853–62.
18. Yeh JC, Hiraoka N, Petryniak B, Nakayama J, Ellies
LG, Rabuka D, Hindsgaul O, Marth JD, Lowe JB,
Fukuda M. Novel sulfated lymphocyte homing receptors and their control by a core 1 extension β1,3-Nacetylglucosaminyltransferase. Cell. 2001;105(7): 957–69.
19. Duijvestijn AM, Horst E, Pals ST, Rouse BN, Steere
AC, Picker LJ, Meijer CJ, Butcher EC. High endothelial
differentiation in human lymphoid and inflammatory tissues defined by monoclonal antibody HECA-452. Am J
Pathol. 1988;130(1):147–55.
20. Mitsuoka C, Sawada-Kasugai M, Ando-Furui K, Izawa
M, Nakanishi H, Nakamura S, Ishida H, Kiso M, Kannagi
R. Identification of a major carbohydrate capping groupof the L-selectin ligand on high endothelial venules inhuman lymph nodes as 6-sulfo sialyl Lewis X. J Biol
Chem. 1998;273(18):11225–33.
21. Mitoma J, Miyazaki T, Sutton-Smith M, Suzuki M, Saito
H, Yeh JC, Kawano T, Hindsgaul O, Seeberger PH,

Increased Cross-sectional Area of HEVs in AITL
Panico M, Haslam SM, Morris HR, Cummings RD, Dell
A, Fukuda M. The N-glycolyl form of mouse sialyl Lewis
X is recognized by selectins but not by HECA-452 andFH6 antibodies that were raised against human cells.
Glycoconj J. 2009;26(5):511–23.
22. Kawashima H, Petryniak B, Hiraoka N, Mitoma J,
Huckaby V, Nakayama J, Uchimura K, Kadomatsu K,
Muramatsu T, Lowe JB, Fukuda M. N-acetylglucosamine6-O-sulfotransferases 1 and 2 cooperatively control lymphocyte homing through L-selectin ligand biosynthesis
in high endothelial venules. Nat Immunol. 2005;6(11):1096–104.
23. Uchimura K, Gauguet JM, Singer MS, Tsay D, Kannagi
R, Muramatsu T, von Andrian UH, Rosen SD. A majorclass of L-selectin ligands is eliminated in mice deficientin two sulfotransferases expressed in high endothelial
venules. Nat Immunol. 2005;6(11):1105–13.
24. Tsutsumiuchi M, Hoshino H, Kogami A, Tsutsumiuchi
T, Yokoyama O, Akama TO, Kobayashi M. Preferential
expression of sialyl 6’-sulfo N-acetyllactosaminecapped O-glycans on high endothelial venules in humanperipheral lymph nodes. Lab Invest. 2019;99(10):1428–41.
25. Patnode ML, Yu SY, Cheng CW, Ho MY, Tegesjö L,
Sakuma K, Uchimura K, Khoo KH, Kannagi R, Rosen
SD. KSGal6ST generates galactose-6-O-sulfate in highendothelial venules but does not contribute to L-selectindependent lymphocyte homing. Glycobiology. 2013;23(3):381–94.
26. Akama TO, Nakayama J, Nishida K, Hiraoka N, Suzuki
M, McAuliffe J, Hindsgaul O, Fukuda M, Fukuda MN.
Human corneal GlcNAc 6-O-sulfotransferase and mouseintestinal GlcNAc 6-O-sulfotransferase both produce
keratan sulfate. J Biol Chem. 2001;276(19):16271–8.
27. Fina L, Molgaard HV, Robertson D, Bradley NJ,
Monaghan P, Delia D, Sutherland DR, Baker MA,
Greaves MF. Expression of the CD34 gene in vascularendothelial cells. Blood. 1990;75(12):2417–26.
28. North SJ, Huang HH, Sundaram S, Jang-Lee J, Etienne
AT, Trollope A, Chalabi S, Dell A, Stanley P, Haslam
SM. Glycomics profiling of Chinese hamster ovary cell
glycosylation mutants reveals N-glycans of a novel sizeand complexity. J Biol Chem. 2010;285(8):5759–75.
29. Hiraoka N, Petryniak B, Nakayama J, Tsuboi S, Suzuki
M, Yeh JC, Izawa D, Tanaka T, Miyasaka M, Lowe JB,
Fukuda M. A novel, high endothelial venule-­
specific
sulfotransferase expresses 6-sulfo sialyl Lewisx, an
L-selectin ligand displayed by CD34. Immunity. 1999;
11(1):79–89.
30. Bistrup A, Bhakta S, Lee JK, Belov YY, Gunn MD, Zuo
FR, Huang CC, Kannagi R, Rosen SD, Hemmerich
S. Sulfotransferases of two specificities function in
the reconstitution of high endothelial cell ligands for
L-selectin. J Cell Biol. 1999;145(4):899–910.
31. Kobayashi M, Mitoma J, Hoshino H, Yu SY, Shimojo
Y, Suzawa K, Khoo KH, Fukuda M, Nakayama J.
Prominent expression of sialyl Lewis X-capped core657
2-branched O-glycans on high endothelial venule-likevessels in gastric MALT lymphoma. J Pathol. 2011;
224(1):67–77.
32. Fujiwara M, Kobayashi M, Hoshino H, Uchimura K,
Nakada T, Masumoto J, Sakai Y, Fukuda M, Nakayama
J. Expression of long-form N-acetylglucosamine-6-Osulfotransferase 1 in human high endothelial venules. J
Histochem Cytochem. 2012;60(5):397–407.
33. Taga M, Hoshino H, Low S, Imamura Y, Ito H, Yokoyama
O, Kobayashi M. A potential role for 6-sulfo sialyl Lewis
X in metastasis of bladder urothelial carcinoma. Urol
Oncol. 2015;33(11):496.e1–9.
34. Ohmori K, Kanda K, Mitsuoka C, Kanamori A, KurataMiura K, Sasaki K, Nishi T, Tamatani T, Kannagi R.
P- and E-selectins recognize sialyl 6-sulfo Lewis X, therecently identified L-selectin ligand. Biochem Biophys
Res Commun. 2000;278(1):90–6.
35. Foss HD, Anagnostopoulos I, Herbst H, Grebe M,
Ziemann K, Hummel M, Stein H. Patterns of cytokine gene expression in peripheral T-cell lymphomaof angioimmunoblastic lymphadenopathy type. Blood.
1995; 85(10):2862–9.
36. Onder L, Danuser R, Scandella E, Firner S, Chai Q,
Hehlgans T, Stein JV, Ludewig B. Endothelial cell-specific lymphotoxin-β receptor signaling is critical for lymph
node and high endothelial venule formation. J Exp Med.
2013;210(3):465–73.
37. Muramatsu T. Vascular system of lymph nodes: vascular density of AILD and malignant lymphomas withspecial reference to HEV. Fukushima J Med Sci. 1987;
33(1):17–28.
38. Ohya A, Kobayashi M, Sakai Y, Kawashima H,
Kageyama S, Nakayama J. Lymphocyte recruitment via
high endothelial venules in lymphoid stroma of Warthin’s
tumour. Pathology. 2013;45(2):150–4.
39. Sakai Y, Hoshino H, Kitazawa R, Kobayashi M. Highendothelial venule-like vessels and lymphocyte recruitment in testicular seminoma. Andrology. 2014;2(2):282–9.
40. Low S, Sakai Y, Hoshino H, Hirokawa M, Kawashima H,
Higuchi K, Imamura Y, Kobayashi M. High endothelial
venule-like vessels and lymphocyte recruitment in diffuse sclerosing variant of papillary thyroid carcinoma.
Pathology. 2016;48(7):666–74.
41. Hiraoka N, Petryniak B, Kawashima H, Mitoma J, Akama
TO, Fukuda MN, Lowe JB, Fukuda M. Significant decreasein α1,3-linked fucose in association with increase in 6-sulfated N-acetylglucosamine in peripheral lymph nodeaddressin of FucT-VII-deficient mice exhibiting diminishedlymphocyte homing. Glycobiology. 2007;17(3):277–93.
42. Malý P, Thall A, Petryniak B, Rogers CE, Smith PL,
Marks RM, Kelly RJ, Gersten KM, Cheng G, Saunders
TL, Camper SA, Camphausen RT, Sullivan FX, Isogai
Y, Hindsgaul O, von Andrian UH, Lowe JB. The α(1,3)
fucosyltransferase Fuc-TVII controls leukocyte trafficking through an essential role in L-, E-, and P-selectin
ligand biosynthesis. Cell. 1996;86(4):643–53.

International Journal of Clinical Oncology
https://doi.org/10.1007/s10147-021-02030-7

ORIGINAL ARTICLE

A phase 2, open‑label, multicenter study of ixazomib
plus lenalidomide and dexamethasone in adult Japanese patientswith relapsed and/or refractory multiple myeloma
Shinsuke Iida1 · Tohru Izumi2,3 · Takuya Komeno4 · Yasuhito Terui5,6 · Takaaki Chou7,8 · Takashi Ikeda9 ·Deborah Berg10 · Shinichi Fukunaga11 · Kenkichi Sugiura12 · Makoto Sasaki13
Received: 2 June 2021 / Accepted: 12 September 2021
© The Author(s) 2021

Abstract
Background TOURMALINE-MM1 was a global study that demonstrated a significant improvement in progression-freesurvival with ixazomib plus lenalidomide and dexamethasone compared with placebo plus lenalidomide and dexamethasone, in patients with relapsed and/or refractory multiple myeloma. The current study was conducted to evaluate further theefficacy and safety of ixazomib plus lenalidomide and dexamethasone in Japanese patients.
Methods This phase 2, open-label, single-arm, multicenter study enrolled patients aged ≥ 20 years with relapsed and/or
refractory multiple myeloma at 16 sites in Japan. Patients refractory to lenalidomide or proteasome inhibitor-based therapy at
any line were excluded. The primary endpoint was the rate of very good partial response or better in the response-evaluable
analysis set. Secondary endpoints were progression-free survival, overall response rate, duration of response, time to progression, overall survival and safety.
Results In total, 34 patients were enrolled. The rate of very good partial response or better was 50.0% (95% confidence
interval 31.9–68.1) and the overall response rate was 84.4% (95% confidence interval 67.2–94.7). Median progression-freesurvival was 22.0 months (95% confidence interval 17.3–not evaluable) and median overall survival was not estimable. The
safety profile of ixazomib plus lenalidomide and dexamethasone in this study was similar to that in the TOURMALINEMM1 study.
Conclusions The efficacy and safety of ixazomib plus lenalidomide and dexamethasone in Japanese patients with relapsed
and/or refractory multiple myeloma are comparable with reported TOURMALINE-MM1 study results.
Clinicaltrials.gov identifier NCT02917941; date of registration September 28, 2016.
Keywords Ixazomib · RRMM · Efficacy · Safety · Japanese
7

Department of Internal Medicine, Niigata Cancer Center
Hospital, Niigata, Japan

8

Niigata Kenshin Plaza, General Incorporated Foundation,
Health Medicine Prevention Association, Niigata, Japan

9

Department of Hematology, Tochigi Cancer Center,
Utsunomiya, Tochigi, Japan

Division of Hematology and Stem Cell Transplantation,
Shizuoka Cancer Center, Nagaizumi, Shizuoka, Japan10

Department of Hematology, Sendai Medical Center, Sendai,
Miyagi, Japan

Clinical Research and Development, Takeda Pharmaceutical
Company Limited, Osaka, Japan

11

Department of Hematology, National Hospital Organization
Mito Medical Center, Ibaraki, Japan

Oncology Therapeutic Area Unit for Japan and Asia, Takeda
Pharmaceutical Company Limited, Osaka, Japan

12

Statistical & Quantitative Sciences, Data Sciences Institute,
Research & Development, Takeda Pharmaceutical Company
Limited, Osaka, Japan

13

Division of Hematology, Department of Internal Medicine,
Juntendo University School of Medicine, Tokyo, Japan* Shinsuke Iida
iida@med.nagoya-cu.ac.jp
1

2

3

4

5

6

Department of Hematology and Oncology, Nagoya City
University Graduate School of Medical Sciences, Kawasumi
1, Mizuho‑cho, Mizuoho‑ku, Nagoya‑shi, Aichi, Japan

Department of Hematology Oncology, Cancer InstituteHospital of Japanese Foundation for Cancer Research, Koto,
Tokyo, Japan
Department of Hematology, Saitama Medical University
Hospital, Moroyama‑machi, Saitama, Japan

13

Vol.:(0123456789)

International Journal of Clinical Oncology

Introduction
Multiple myeloma (MM) is a malignant disease in which
monoclonal plasma cells proliferate, mainly in the bonemarrow. MM causes increases in monoclonal immunoglobulin (M-protein) production by myeloma cells, hematopoietic deterioration, bone destruction, hypercalcemia
and renal failure. MM constitutes approximately 1% of allreported neoplasms and approximately 13% of hematologic cancers worldwide [1]. In Japan, the National Cancer
Center estimated there would be 7,800 new cases of MM,
with approximately 4500 deaths in 2019 [2].
The treatment landscape has shifted from autologous
stem cell transplantation (ASCT) being the mainstay oftherapy, to its combination with novel agent-based induction regimens and post-ASCT consolidation and maintenance treatments [3]. While historic treatment approaches
focused on the use of cytotoxic drugs such as alkylating
agents, anthracyclines and corticosteroids, the introductionof the first-in-class proteasome inhibitor (PI), bortezomib
and immunomodulatory drugs (IMiDs) such as thalidomide and lenalidomide have improved treatment outcomes[4–7]. PIs and IMiDs currently remain the backbone oftherapy throughout the MM treatment pathway [8, 9].
Monoclonal antibody drugs also play an important role[10].
Although some data have suggested an increasing cure
fraction rate in front-line patients, MM is still generally
regarded as incurable [11]. Most patients receive multiplelines of therapy, including combination regimens, over thecourse of their disease [12].
In an effort to further target the increased proteasome
activity known to occur in MM and other cancers, ixazomib, a small molecule 20S PI, was developed. In contrast to bortezomib, ixazomib has demonstrated a fasterdissociation rate from the proteasome; improved pharmacokinetics and pharmacodynamics, which may result inenhanced tumor penetration; and antitumor activity in a
broader range of tumor xenografts [13]. The clinical benefit of ixazomib has been studied previously and ixazomib
is being developed globally as a treatment option forrelapse/refractory (RR)MM, newly diagnosed (ND)MM,
maintenance monotherapy for MM and relapsed or refractory systemic light-chain amyloidosis [14–17]. In Japan,
ixazomib is approved for the treatment of RRMM andmore recently was approved as maintenance monotherapy.
The pivotal TOURMALINE-MM1 (MM1) study wasa phase 3 global, randomized, double-blind, placebocontrolled study that evaluated the efficacy and safety ofixazomib combined with lenalidomide and dexamethasone (LenDex) versus placebo combined with LenDex, inpatients with RRMM who had received at least one prior

13

therapy [17]. The efficacy results of the primary analysis
for the overall population demonstrated a statistically significant and clinically meaningful prolongation in the primary endpoint of progression-free survival (PFS) with theixazomib plus LenDex regimen compared with the placebo
plus LenDex regimen (median PFS of 20.6 months versus
14.7 months, respectively [hazard ratio 0.742; p = 0.012]),as assessed by an independent review committee [17].
In addition, the ixazomib plus LenDex regimen provided
clinical benefit, as demonstrated by significant improvements in complete response (CR) rate, overall response rate(ORR) and rate of very good partial response (VGPR) or
better (VGPR + CR), and longer disease control, as demonstrated by a significant improvement in time to progression(TTP) and a longer duration of response (DOR). Ixazomib
in combination with LenDex has thus been shown to be an
efficacious regimen [17].
However, the PFS data for the Japanese subpopulation in
the MM1 study was limited [17]; hence, consistency withthe overall population could not be concluded, and efficacy
in Japanese patients remained to be confirmed.
The current study evaluated the efficacy and safety of ixazomib when administered with LenDex in Japanese patientswith RRMM. In relapsed or refractory settings, significantly
longer PFS or TTP have been demonstrated in patients with
VGPR or better, compared with those with partial response(PR) [18]. Therefore, the endpoint of VGPR + CR rate isoften clinically correlated with PFS. Hence, the primaryobjective of this study was to determine the rate of VGPR
or better in the response-evaluable analysis set, which wasagreed upon in a consultation meeting between the study
sponsor and the Pharmaceuticals and Medical Devices
Agency of Japan.

Materials and methods
Study design
This was a phase 2, open-label, single-arm, multicenter
study, with patients enrolled at 16 study sites in Japan.
Response was assessed according to the International Myeloma Working Group criteria every 4 weeks until progressive disease (PD) [19]. All patients were followed up for
survival after progression, and patients were contacted every
12 weeks until death or termination of the study. Patients
attended an end of treatment visit approximately 30 days
after receiving their last dose of any study drug (ixazomib,
lenalidomide or dexamethasone) and continued to be followed up for other assessments. Patients discontinuing study
treatment prior to PD continued to be assessed for PD during the PFS follow-up portion of the study. Primary analysis was conducted approximately 12 months after the lastInternational Journal of Clinical Oncology

enrollment, and the final analysis was conducted after the
final database lock, approximately 24 months after the lastenrollment.
This study was conducted in compliance with Good Clinical Practice (GCP), Good Post-marketing Study Practice(GPSP) and all applicable local regulations and guidelines.
All study-related documents were reviewed and approved by
the local or central institutional review boards of all study
sites. This study was also conducted in accordance with the
Declaration of Helsinki, and the International Council for
Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Harmonised Tripartite Guideline
for GCP and GPSP, and all applicable regulations. Objectives and potential risks and benefits were explained topatients using the informed consent form approved by theinstitutional review board, with each patient having signed
and dated the form before screening.

Patient selection
Impor tant inclusion criteria were (1) Japanesepatients ≥ 20 years with diagnosed MM and measurable disease with serum M-protein ≥ 1 g/dl, urine M-protein ≥ 200 mg/24 h or abnormal serum free light chain(FLC) ratio with involved FLC level ≥ 10 mg/dl and who
were relapsed and/or refractory after receiving one to threeprior therapies; (2) patients who had an Eastern Cooperative Oncology Group (ECOG) performance status of 0, 1 or
2. Patients who had received autologous transplants werealso eligible for inclusion. Additional inclusion criteria aredescribed in Online Resource 1.
An important exclusion criterion was patients who
were refractory to lenalidomide or PI-based therapy at any
line. Additional exclusion criteria are described in Online
Resource 1.

Treatment
Patients received 4 mg of ixazomib on days 1, 8 and 15 plus
lenalidomide (25 mg) on days 1 through 21, and dexamethasone (40 mg) on days 1, 8, 15 and 22 of a 28-day cycle.
Patients with a low creatinine clearance of < 60 ml/min
received a reduced lenalidomide dose of 10 mg once daily.
Patients continued to receive treatment until progressive disease or unacceptable toxicity, whichever came first.

Statistical analysis
For the VGPR + CR rate and ORR, two-sided 95% confidence intervals (CIs) were calculated in the responseevaluable analysis set and the full analysis set (FAS). The
response-evaluable population was defined as patients who
received at least one dose of ixazomib, had measurable disease at baseline and had at least one post-baseline response
assessment. For PFS, OS and TTP, Kaplan–Meier estimates (and the 25th, 50th [median] and 75th percentiles, if
estimable) were calculated with their two-sided 95% CIs in
the FAS. For DOR, the Kaplan–Meier estimates was calculated as well for responders. Based on the results of theMM1 study, a sample size of 27 was required to provide
a point estimate of 48.1% for the expected VGPR + CR
rate, which was higher than the threshold rate of 39.0%
with 80% probability. Assuming a dropout rate of 10%, thetarget number of patients for this study was set to 30. Allanalyses were conducted with SAS version 9.2.

Results
Patient background
Patient demographics and baseline characteristics aredescribed in Table 1. Of the 34 patients in the FAS, themedian age was 67 years. Median time from initial diagnosis was 44.4 months, with 71% of MM diagnosed beingof the IgG type.
At study entry, 82% of patients were International Staging System stage I, and 74% had an ECOG performancestatus of 0. Additionally, 29% of patients had a creatinine
clearance of 30 to < 60 ml/min, 62% of patients only had
one prior line of therapy, 91% of patients had relapsed
disease and 9% of patients had refractory disease.
Overall, 68% of patients had undergone ASCT with a
median time of 38.8 months since the time of last transplantation to the first dose at study entry. 91% of patients
had previous exposure to PIs and 35% to IMiDs. 32% of
patients had high-risk cytogenetic abnormalities, whereby
cut-offs were 5% positive cells for del(17p), 3% for t(4;14)
and 3% for t(14;16), of cells testing positive for theseabnormalities.

Endpoints
The primary endpoint was the rate of VGPR + CR in the
response-evaluable analysis set. The secondary endpoints
were PFS, ORR, DOR, TTP, safety and overall survival(OS). The definitions of secondary endpoints are describedin Online Resource 2.

Exposure
The study drug exposure is described in Table 2. The median
number of treatment cycles for all study drugs was 20, with a
median relative dose intensity of 86.0% for ixazomib, 81.6%
for lenalidomide and 91.7% for dexamethasone.

13

International Journal of Clinical Oncology
Table 1  Patient demographics and baseline characteristics in the fullanalysis set
Ixazomib + LenDex
N = 34Age (years)
Median67
Min, max
40, 78Sex (n, %)Male
19 (56)
Female15 (44)Baseline body surface area (­ m2)
Median1.62
Min, max
1.20, 2.03
Time since initial diagnosis to first dose at study entry (months)
Median44.4
Min, max
10, 176
Type of myeloma at initial diagnosis (n, %)IgG
24 (71)IgA
4 (12)
Bence-Jones
5 (15)
Light chain only
1 (3)ISS stage for myeloma at study entry (n, %)I
28 (82)II
5 (15)III
1 (3)Creatinine clearance (ml/min) (n, %)30 to < 60
10 (29)60 to < 90
15 (44)90 to max
9 (26)
Baseline ECOG performance status (n, %)0
25 (74)1
9 (26)
Prior therapy (n, %)Lines of prior therapy
1
21 (62)2
12 (35)3
1 (3)Patient population categories
31 (91)
Relapsed ­patientsa
3 (9)Refractory ­patientsb
Patients with ASCT
23 (68)
Prior IMiD therapy
Exposed12 (35)5 (15)­Thalidomidec
8 (24)­Lenalidomidec
Naive
22 (65)
Prior PI therapy
Exposed31 (91)d30 (88)­Bortezomib
2 (6)­Carfilzomibd
Naive
3 (9)

13

Table 1  (continued)
Ixazomib + LenDex
N = 34
Cytogenetics
Cytogenetics results
High ­riske
Standard
Not available
Chromosomal abnormalities
Del 13 or 13q
Del 17 or 17pt(4;14)t(11;14)t(14;16)

11 (32)21 (62)2 (6)
15 (44)4 (12)
7 (21)2 (6)1 (3)ASCT autologous stem cell transplantation, ECOG Eastern Cooperative Oncology Group, IMiD immunomodulatory drug, PI proteasome
inhibitor, ISS International Staging System, LenDex lenalidomide anddexamethasoneaRelapsed was defined as patients who relapsed from at least one previous treatment but were not refractory to any previous treatmentb

Refractory was defined as patients who were refractory to at least
one previous treatment but did not relapse from any previous treatmentcde

One patient had been exposed to both thalidomide and lenalidomide
One patient had been exposed to both bortezomib and carfilzomib

High-risk cytogenetic abnormalities that were defined as containingt(4;14), t(14;16) or del(17p); cutoff values for defining the presenceof t(4;14), t(14;16) and del(17p) were 3%, 3% and 5% positive cells,
respectively

Efficacy
The response to treatment is described in Table 3. The primary endpoint of confirmed VGPR + CR rate was 50.0%(95% CI 31.9–68.1), which was above the threshold rateof 39.0% based on the results of the MM1 study. The ORR
was 84.4% (95% CI 67.2–94.7) and the CR rate was 28.1%(95% CI 13.7–46.7), with the stringent CR rate being
25.0% (95% CI 11.5–43.4). In the high-risk and standardrisk cytogenetics subgroups, the ORRs were 72.7% and90.5%, respectively. In the response-evaluable analysis set,
stable disease was demonstrated in 15.6% of patients andno patients had PD as their best response.
The median DOR was not estimable for patients with
VGPR or better. At the last assessment, no PD was documented in 78% of patients with CR (n = 9), 56% of patientswith VGPR or better (n = 16) and 48% of patients withPR or better (n = 27). The median time to response was
2.9 months (95% CI 1.8–5.1) for VGPR or better and
1.0 months (95% CI 1.0–1.8) for PR or better.
PFS is described in Fig. 1. With a median followup of 28.1 months, median PFS was 22.0 months (95%

International Journal of Clinical Oncology
Table 2  Study drug exposure in
the safety analysis set

Ixazomib + LenDex
N = 34
Number of treatment c­ yclesa
Mean (standard deviation)MedianMin, max
Relative dose intensity (%)
Mean (standard deviation)MedianMin, max

Ixazomib

Lenalidomide

Dexamethasone

Combination

17.8 (9.3)
20.0
1, 32

17.8 (9.3)
20.0
1, 32

17.8 (9.3)
20.0
1, 32

17.8 (9.3)
20.0
1, 32

85.4 (12.8)
86.0
59, 100

78.2 (18.7)
81.6
40, 100

76.5 (25.0)
91.7
23, 100–––

LenDex lenalidomide and dexamethasoneaA treatment cycle was defined as a cycle in which the patient received any amount of ixazomib, lenalidomide or dexamethasone

Table 3  Summary of response to treatment in the response-evaluable
analysis set
Ixazomib + LenDex
N = ­32a

CR
sCR
PR
VGPR
Overall response (≥ PR)
VGPR or better (CR + VGPR)
SD
PD

n

%

95% CI

9
8
18
7
27
16
5
0

(28.1)(25.0)(56.3)(21.9)(84.4)(50.0)(15.6)(0.0)(13.7, 46.7)(11.5, 43.4)(37.7, 73.6)(9.3, 40.0)(67.2, 94.7)(31.9, 68.1)(5.3, 32.8)(0.0, 10.9)

CI confidence interval, CR complete response, LenDex lenalidomide
and dexamethasone, PD progressive disease, PR partial response,sCR stringent complete response, SD stable disease, VGPR very goodpartial responseaResponse-evaluable population: among 34 subjects enrolled in the
study, 32 subjects who received at least one dose of study drug, hadmeasurable disease at baseline, and had at least one post-baseline
response assessment. Two enrolled patients were not response-evaluable: one subject did not have any evaluable disease at baseline and
one was not evaluable for response assessment

CI 17.3–not evaluable); and 18 patients (53%) had PD,
including one death.
At the time of data cutoff, the OS data were not mature,and the median OS was not estimable.

SafetyThe overall summary of treatment-emergent adverse
events (TEAEs) is described in Table 4 and the most common TEAEs that were reported in ≥ 10% of patients aredescribed in Table 5. All patients experienced at least one
TEAE and drug-related TEAE during the study (n = 34

[100%]). Additionally, 85% of patients had at least 1 TEAE
of Grade ≥ 3 and 79% of patients had a drug-related TEAE
of Grade ≥ 3. The most common TEAEs of Grade ≥ 3
were neutropenia (21%), decreased platelet count (21%),decreased neutrophil count (18%), diarrhea (15%) andmaculopapular rash (12%) (Table 6). Two deaths wereobserved, with one death due to adverse events (AEs)(subarachnoid hemorrhage and subdural hemorrhage) thatwere associated with a fall and without study drug causality, and another death due to primary disease or its complications after discontinuation of study treatment for PD.
Serious adverse events (SAEs) were experienced by 35%
of patients (n = 12), with 29% experiencing drug-related
SAEs. The most common SAEs were pneumonia (9%),
diarrhea (6%) and fall (6%), while the most common drugrelated SAEs were pneumonia (9%) and diarrhea (6%).
Overall, 32% of patients had at least one TEAE leading to
the discontinuation of one or more of the three study drugs;
the most common AE was neutropenia, which was reported
in two patients. Additionally, 76% of patients had at least
one TEAE resulting in dose reduction of any study drug,with the most common AE being maculopapular rash, which
was reported in four patients. Two patients were diagnosed
with a second malignancy while on study treatment: onepatient was reported as having acute myeloid leukemia andthe other had malignant lung neoplasm. Both of these AEs
were serious, considered to be related to study treatment andresulted in discontinuation of study treatment. Patients were
specifically followed up for second malignancies because of
the increased risk with lenalidomide [20, 21].
TEAEs occurring in ≥ 20% of patients were constipation (50%), upper respiratory tract infection (47%), diarrhea(41%), rash (35%), nasopharyngitis (32%), nausea (29%),
platelet count decreased (26%), influenza (24%), neutropenia(24%), chilblains (21%), dysgeusia (21%), fall (21%) andperipheral sensory neuropathy (21%).

13

International Journal of Clinical Oncology
1.0
Probability of Progression Free Survival

Fig. 1  Progression-free survivalin the full analysis set

0.9
0.8
0.7
0.6
0.5

0.4
0.3
0.2

0.1
0.0

No. at risk

Median = 22.05
Number of Event = 18
0

6

34

30

Table 4  Overall summary of TEAEs in the safety analysis set
Ixazomib + LenDex
N = 34n (%)
Any AE
Grade 3 or higher AE
Drug-related AE
Drug-related Grade 3 or higher AE
SAE
Drug-related SAE
AEs resulting in any study drug dose reduction
AEs resulting in any study drug dose­modificationa
AEs resulting in any study drug
­discontinuationb
On-study ­deathsc

34 (100)
29 (85)34 (100)
27 (79)12 (35)10 (29)26 (76)29 (85)
11 (32)1 (3)Dose reduction, dose modification and study drug discontinuation
may have been in relation to any of the three drug regimens. On-study
deaths were defined as deaths that occurred within 30 days of the lastdose of study drug
AE adverse event, LenDex lenalidomide and dexamethasone, SAE
serious adverse event, TEAE treatment-emergent adverse eventa

Dose modification included dose reduction, dose increase, dosedelay and dose discontinuationb

Study drug discontinuation may have been in relation to ≥ 1 of thethree study drugsc

The subject died of subarachnoid hemorrhage and subdural hemorrhage during the study. The event was assessed to be associated witha fall; causality with the study drug was ruled out

Drug-related TEAEs occurring in ≥ 20% of patients werediarrhea (38%), constipation (38%), rash (29%), platelet
count decreased (26%), nausea (24%), peripheral sensory
neuropathy (21%), dysgeusia (21%) and neutropenia (21%).
Drug-related TEAEs of Grade ≥ 3 occurring in ≥ 10% of
patients were platelet count decreased (21%), neutrophil

13

12
18
24
Time (months) from First Study Drug Administraon
24

21

12

30

36

3

0

count decreased (18%), neutropenia (18%), diarrhea (15%)
and maculopapular rash (12%).
A summary of TEAEs of clinical importance is presented
in Table 7. The TEAEs of clinical importance were neutropenia, thrombocytopenia, heart failure, arrhythmia, myocardial infarction, nausea, vomiting, diarrhea, rash, maculopapular rash, urticaria, erythema multiforme, generalized rash,
macular rash, peripheral sensory neuropathy and peripheral
neuropathy.
AEs of clinical importance reported in this study weresimilar to those reported in the global MM1 study. No previously unknown safety concerns were identified in this study.

Discussion
In this study, all demographics and other baseline characteristics except race were similar to those of the MM1 study[17]. The efficacy of ixazomib when administered with LenDex in Japanese RRMM patients was similar to that in theintent-to-treat (ITT) population of the MM1 study. The confirmed VGPR + CR rate was 50.0% and the point estimate
demonstrated in this study exceeded the threshold rate of
39.0%, which was based on the results of the MM1 study.
The confirmed ORR and CR rates in this study (84.4% and28.1%, respectively) were numerically better than those in
the MM1 study (78% and 12%, respectively). The ORR for
the high-risk subgroup was 72.7% and 90.5% for the standard-risk subgroup, although the number of patients withhigh-risk cytogenetics was small in this study. The favorable
result for the primary efficacy endpoint was supported by thefindings from the secondary efficacy endpoints, including
PFS, DOR and TTP.
The safety profile of ixazomib when administered with
LenDex in Japanese RRMM patients was similar to that in
the ITT population of the MM1 study [17]. No new safety

International Journal of Clinical Oncology
Table 5  The most common (≥ 10%) TEAEs in the safety analysis set

Table 6  Grade 3 or higher TEAEs in the safety analysis set

Ixazomib +LenDexN = 34n (%)
Patients with at least 1 AE
Constipation
Upper respiratory tract infection
Diarrhea
Rash
Nasopharyngitis
NauseaPlatelet count decreased
Influenza
Neutropenia
Chilblains
Dysgeusia
Fall
Peripheral sensory neuropathy
Bronchitis
Neutrophil count decreased
Dental caries
MalaiseEdema peripheral
Pharyngitis
Maculopapular rash
Vomiting
Back pain
Dry skin
Hyperglycemia
Oropharyngeal pain
Pain in extremity
Pneumonia
Pyrexia
Stomatitis
Thrombocytopenia
Urticaria

34 (100)
17 (50)16 (47)14 (41)
12 (35)
11 (32)10 (29)9 (26)
8 (24)
8 (24)
7 (21)
7 (21)
7 (21)
7 (21)
6 (18)
6 (18)
5 (15)
5 (15)
5 (15)
5 (15)
5 (15)
5 (15)
4 (12)
4 (12)
4 (12)
4 (12)
4 (12)
4 (12)
4 (12)
4 (12)
4 (12)
4 (12)

AE adverse event, LenDex lenalidomide and dexamethasone, TEAE
treatment-emergent adverse event

concerns were identified in this study, and the overall safety
profile of ixazomib plus LenDex showed that this combination was well tolerated.
Diarrhea was the most commonly reported gastrointestinal AE of clinical importance. While antidiarrheal agentsare not recommended for prophylactic use, they should be
used appropriately as symptomatic treatment. In this study,
antidiarrheal agents were administered if infectious causes
were excluded.
Dermatological disorders were reported in 65% of
patients, while they did not result in any patient discontinuing the study treatment, 24% of patients had their doses ofIxazomib +LenDexN = 34n (%)
Neutropenia
Platelet count decreased
Neutrophil count decreased
Diarrhea
Maculopapular rash
Erythema multiforme
Pneumonia
White blood cell count decreased
Alanine aminotransferase increased
AnemiaAspartate aminotransferase increased
Blood creatine phosphokinase increased
Cellulitis
Fall
Femoral neck fracture
Hyperglycemia
Hypertension
Hypophosphatemia
Hypotension
Leukemia
Lipase increased
Loss of consciousness
Lung neoplasm malignant
Lymphocyte count decreased
Malignant neoplasm of unknown primary ­setaa
Edema peripheral
Osteonecrosis of jaw
Pharyngitis
Pneumonitis
Rash generalized
Macular rash
Sebaceous nevus
Subarachnoid hemorrhage
Subdural hemorrhage
Thrombocytopenia
Tooth infection
Vomiting

7 (21)
7 (21)
6 (18)
5 (15)
4 (12)
3 (9)
3 (9)
3 (9)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)
1 (3)

Toxicity grade defined according to the National Cancer InstituteCommon Terminology Criteria for Adverse Events version 4.03
LenDex lenalidomide and dexamethasone, TEAE treatment-emergent
adverse eventa

Reported during the follow-up period

ixazomib and/or lenalidomide reduced, and 32% had theirdoses of ixazomib and/or lenalidomide withheld. This corresponds with the incidence of rash observed in the Japanese
subgroup enrolled in the MM1 study (70% reported rash)

13

International Journal of Clinical Oncology
Table 7  Summary of TEAEs of clinical i­mportancea in the safety analysis set
N = 34, n (%)

Any Grade
Adverse event

Blood toxicity
Neutropenia
14 (41)
Thrombocyto13 (38)penia
Cardiac disorders
Cardiac failure
1 (3)Gastrointestinal disorders
Nausea10 (29)Vomiting
5 (15)
Diarrhea
14 (41)
Dermatologic
22 (65)disordersRash
12 (35)5 (15)
Maculopapular
rash
Urticaria
4 (12)
Erythema multi- 3 (9)forme
Generalized rash 1 (3)Macular rash
1 (3)Peripheral neuropathy
Peripheral sen7 (21)sory neuropathy
Peripheral neu1 (3)ropathy≥ Grade 3

Treatment/response

Drug related

Adverse event

Drug relatedDose reduction
Dose
discontinuation

Patient withdrawn from­studyb

Next cycle
delayed13 (38)13 (38)13 (38)8 (24)

12 (35)8 (24)

3 (9)
3 (9)

6 (18)
4 (12)

1 (3)
2 (6)

10 (29)5 (15)

0

0

0

0

0

0

0

9 (26)
5 (15)
13 (38)20 (59)01 (3)5 (15)
9 (26)01 (3)5 (15)
8 (24)

0
0
0
0

0
1 (3)
2 (6)
8 (24)0
1 (3)4 (12)
11 (32)

0
0
0
2 (6)

10 (29)5 (15)0
4 (12)0
4 (12)00

0
4 (12)2 (6)
4 (12)

0
0

3 (9)
3 (9)

0
3 (9)

0
3 (9)

0
0

0
2 (6)

0
3 (9)

0
0

1 (3)
1 (3)

1 (3)
1 (3)

1 (3)
1 (3)

0
0

1 (3)
1 (3)

1 (3)
1 (3)

1 (3)
1 (3)

7 (21)

0

0

0

1 (3)

0

1 (3)

1 (3)

0

0

0

0

0

0

TEAE treatment-emergent adverse eventa
TEAEs of clinical importance was not based on a safety signal in the review of the clinical data; rather, they were considered of clinical importance owing to other factors that included, but were not limited to: (1) identification by searches of the clinical database considering the context
of the intended patient population; (2) common adverse reactions for lenalidomide; (3) AEs reported at higher rates both across ixazomib clinical trials and within the MM1 study; and (4) adverse reactions reported with the commercially available PIs bortezomib and carfilzomibbSome patients withdrew from the study due to multiple TEAEs

and higher than that reported in the overall population in the
MM1 study (51% reported rash) [17], suggesting a slightly
higher frequency of skin disorders developing in the Japanese population. However, this AE was manageable either
by dose reduction or supportive care.
This study employed a dose modification guideline forrash that was modified from the MM1 study [17], and consistent with the Japanese package insert (i.e., ixazomib andlenalidomide were withheld in the event of Grade 2 rash not
manageable by supportive care, until it recovered to Grade
1 or better). The successful management of rash may havecontributed to the high relative dose intensity of lenalidomide while also maximizing exposure to ixazomib.
Supportive care and dose modification that are dependent
on the patient’s AEs seem an effective approach for continuous treatment, resulting in better outcomes. This is supported

13

by the results in this study, with patients receiving a medianof 20 treatment cycles and showing good responses.
This study has several clinical limitations. Its single-arm,
open-label design may bias the interpretation of the study
results. Secondly, the sample size and inclusion/exclusion
criteria of the study limit the generalizability of its results.
For example, 82% of patients were international staging system (ISS) stage I, 74% had an ECOG performance status of0, 62% only had one prior line of therapy, 91% had relapsed
disease and 68% had undergone ASCT. Hence, the generalizability of these results to patients with more advanced
stage, poorer performance status or multiple lines of therapy,
those with refractory disease or ASCT-ineligible patients
may be limited.
In conclusion, ixazomib, when administered with LenDex, demonstrated efficacy in achieving a 50% rate ofInternational Journal of Clinical Oncology

confirmed VGPR or better in Japanese patients with RRMM,
which was higher than the 39.0% threshold rate. The results
demonstrated in this study are comparable with those in
the MM1 study [17]. Likewise, the overall safety profile ofixazomib when administered with LenDex showed that thiscombination was well tolerated in this population.
Supplementary Information The online version contains supplementary material available at https://d​ oi.o​ rg/1​ 0.1​ 007/s​ 10147-0​ 21-0​ 2030-7.
Acknowledgements The authors would like to thank all patients andtheir families, and all investigators who have contributed to this study;
Yoshihide Kakimoto, MD, from the Medical Affairs of Takeda Pharmaceutical Co. Ltd. (Tokyo, Japan) for reviewing and providing valuableadvice on this manuscript; and Yoichi Kase, PhD, from the Oncology
Clinical Research Department of Takeda Pharmaceutical Co. Ltd. forproviding valuable inputs on the study design. Medical writing assistance was provided by Darryl Lin and Ivan Olegario of MIMS Pte Ltd,
which was funded by Takeda Pharmaceutical Co. Ltd., and compliedwith Good Publication Practice 3 ethical guidelines (Battisti et al. Ann
Intern Med. 2015;163:461–4).
Authors’ contributions SF, KS, DB and SI were involved in the study
concept and design. All authors were involved in the acquisition, analysis or interpretation of data, and critical revision of the manuscriptfor important intellectual content. SF, KS and SI were involved in thedrafting of the manuscript. KS was involved in statistical analysis. SF
and DB were involved in obtaining funding. SF was involved in administrative, technical or material support. SI and SF were involved in the
study supervision.
Funding This study was funded by Takeda Pharmaceutical Company
Ltd. (Osaka, Japan).
Availability of data and material The authors do not plan to share individual participant’s data supporting the results reported in this article
because informed consent about external data sharing has not beenobtained in any patient in the study due to investigator sites’ policy.
The redacted study protocol, redacted statistical analysis plan, in thisarticle will be made available, within 3 months from initial request, toresearchers who provide a methodologically sound proposal.

Declarations
Conflict of interest Shinsuke Iida reports research funding, honoraria
and fees from Takeda Pharmaceutical Company Limited, during the
conduct of the study; Honoraria and fees from Ono, Celgene, Janssen,
Sanofi, Daiichi Sankyo and Takeda Pharmaceutical Company Limited;
research funding from AbbVie, Bristol-Myers Squibb, Celgene, Daiichi Sankyo, GlaxoSmithKline, Ono and scholarship donations from
Chugai, Kyowa Kirin, Ono, Sanofi and Takeda Pharmaceutical Company Limited, outside the submitted work. Takashi Ikeda reports honoraria and fees from Sanofi, outside the submitted work. Deborah Berg,
Shinichi Fukunaga and Kenkichi Sugiura are employees of Takeda
Pharmaceutical Company Limited, during the conduct of the study.
Deborah Berg and Shinichi Fukunaga have stock ownership in Takeda
Pharmaceutical Company Limited. Takaaki Chou has received honoraria from Takeda Pharmaceutical Company Limited, Ono, and BMS.
Yasuhito Terui reports honoraria and fees from Celgene, Janssen,
Merck Sharp & Dohme, Eisai, Ono, Takeda Pharmaceutical Company
Limited, Chugai and AbbVie. Makoto Sasaki reports honoraria andlecture fees Takeda Pharmaceutical Company Limited. Tohru Izumi
and Takuya Komeno have nothing to disclose.
Ethical approval All study-related documents were reviewed andapproved by the local or central institutional review boards of all study
sites.
Informed consent Objectives and potential risks and benefits wereexplained to patients using the informed consent form approved by theinstitutional review board, with each patient having signed and dated
the form before screening.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article areincluded in the article's Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view acopy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

References
1. Palumbo A, Anderson K (2011) Multiple myeloma. N Engl J Med
364(11):1046–1060
2. Japan National Cancer Center (2019). Projected cancer statistics,
2020. http://​ganjo​ho.​jp/​reg_​stat/​stati​stics/​stat/​short_​pred.​html.
Accessed 02 June 2021
3. Gay F, Engelhardt M, Terpos E et al (2018) From transplant tonovel cellular therapies in multiple myeloma: European Myeloma
Network guidelines and future perspectives. Haematologica
103(2):197–211
4. Dimopoulos M, Spencer A, Attal M et al (2007) Lenalidomide
plus dexamethasone for relapsed or refractory multiple myeloma.
N Engl J Med 357(21):2123–2132
5. Facon T, Mary JY, Hulin C et al (2007) Melphalan and prednisone plus thalidomide versus melphalan and prednisone alone or
reduced-intensity autologous stem cell transplantation in elderly
patients with multiple myeloma (IFM 99–06): a randomised trial.
Lancet 370(9594):1209–1218
6. San Miguel JF, Schlag R, Khuageva NK et al (2008) Bortezomib
plus melphalan and prednisone for initial treatment of multiple
myeloma. N Engl J Med 359(9):906–917
7. Weber DM, Chen C, Niesvizky R et al (2007) Lenalidomide plus
dexamethasone for relapsed multiple myeloma in North America.
N Engl J Med 357(21):2133–2142
8. Iida S, Ishida T, Murakami H et al (2019) JSH practical guidelines
for hematological malignancies, 2018: III. Myeloma-1. Multiple
myeloma (MM). Int J Hematol 109(5):509–538
9. Kumar SK, Callander NS, Hillengass J et al (2019) NCCN guidelines insights: multiple myeloma, version 1.2020. J Natl Compr
Cancer Netw 17(10):1154–1165
10. Yang Y, Li Y, Gu H et al (2020) Emerging agents and regimens
for multiple myeloma. J Hematol Oncol 13(1):150–150
11. Barlogie B, Mitchell A, van Rhee F et al (2014) Curing myeloma at last: defining criteria and providing the evidence. Blood
124(20):3043–3051

13

International Journal of Clinical Oncology
12. Dimopoulos MA, Richardson PG, Moreau P et al (2014) Current treatment landscape for relapsed and/or refractory multiple
myeloma. Nat Rev Clin Oncol 12(1):42–54
13. Kupperman E, Lee EC, Cao Y et al (2010) Evaluation of theproteasome inhibitor MLN9708 in preclinical models of humancancer. Can Res 70(5):1970–1980
14. Dimopoulos MA, Gay F, Schjesvold F et al (2019) Oral ixazomib
maintenance following autologous stem cell transplantation(TOURMALINE-MM3): a double-blind, randomised, placebocontrolled phase 3 trial. Lancet 393(10168):253–264
15. Dimopoulos MA, Špička I, Quach H et al (2020) Ixazomib as
postinduction maintenance for patients with newly diagnosed
multiple myeloma not undergoing autologous stem cell transplantation: the phase III TOURMALINE-MM4 trial. J Clin Oncol
38(34):4030–4041
16. Dispenzieri A, Kastritis E, Wechalekar AD et al (2019) Primary
results from the phase 3 tourmaline-al1 trial of ixazomib-dexamethasone versus physician’s choice of therapy in patients (Pts)with relapsed/refractory primary systemic al amyloidosis (RRAL).
Blood 134(Supplement_1):139–139

13

17. Moreau P, Masszi T, Grzasko N et al (2016) Oral ixazomib, lenalidomide, and dexamethasone for multiple myeloma. N Engl J
Med 374(17):1621–1634
18. Chanan-Khan AA, Giralt S (2010) Importance of achieving acomplete response in multiple myeloma, and the impact of novel
agents. J Clin Oncol 28(15):2612–2624
19. Rajkumar SV, Harousseau J-L, Durie B et al (2011) Consensus
recommendations for the uniform reporting of clinical trials:
report of the International Myeloma Workshop Consensus Panel
1. Blood 117(18):4691–4695
20. U.S. Food and Drug Administration. REVLIMID Prescribing
Information (2019) 4439576
21. Revlimid capsules® (lenalidomide) (2020) Japan Package Insert.
00065995
Publisher's Note Springer Nature remains neutral with regard tojurisdictional claims in published maps and institutional affiliations.

Case report

Takumi Osawa ‍ ‍, Yuta Ito, Tomomi Koizumi
Department of Cardiovascular
Medicine, Mito Medical Center,
Mito, Ibaraki, Japan
Correspondence to
Dr Takumi Osawa;​tosawa49710@​gmail.c​ om
Accepted 30 June 2021

SUMMARY
Coronary artery fistula (CAF) is an uncommon congenital
heart disease. Furthermore, aortopulmonary fistula is arare congenital heart disease of adult onset. We report
the case of a 79-­year-­old man who presented withchest pain. ECG-­gated cardiac CT and coronary artery
angiography revealed an anomalous vessel arisingfrom the right coronary cusp and a CAF from the left
coronary descending artery. These fistulas coalesced anddrained into the same portion of the pulmonary artery.
Haemodynamic studies revealed that the estimatedsystemic-­to-­pulmonary flow ratio was 1.18. The mean
pulmonary pressure was 14 mm Hg. We decided against
surgical intervention due to his advanced age and lackof heart failure symptoms. The patient did not have any
worsening heart failure and chest pain on follow-­up. This
was a rare case of CAF coexistent and coalesced with anaortopulmonary fistula.

BACKGROUND

Coronary artery fistula (CAF) is a very rare disease.1
It is a communication between the coronary arteries
and the cardiac chambers or other vessels. Aortopulmonary fistula is also a rare disease. We report acase where CAF and an aortopulmonary fistula
coalesced and drained into the pulmonary artery(PA). We obtained a detailed anatomical information using multidetector CT angiography and coronary artery angiography.

CASE PRESENTATION© BMJ Publishing Group
Limited 2021. Re-­use
permitted under CC BY-­NC. No
commercial re-­use. See rights
and permissions. Publishedby BMJ.
To cite: Osawa T, Ito Y,
Koizumi T. BMJ Case
Rep 2021;14:e244035.
doi:10.1136/bcr-2021244035

A 79-­year-­old man with a 3-­month history of chest
discomfort unrelated to exercise was admitted toour hospital. The patient reported a medical historyof asthma and chronic obstructive pulmonary
disease, which were well-­controlled by medication.
On initial examination, his blood pressure was
115/82 mm Hg, heart rate was 78 beats/min, respiratory rate was 12 breaths/min, pulse oximetric
oxygen saturation was 98% in room air and his
temperature was 36.2°C. The patient’s height andweight were 170 cm and 66 kg, respectively. Clinicalexamination of the cardiovascular system revealed
no murmurs, and the lungs were clear. His jugularvenous pressure was not elevated, and he did notexhibit heart failure symptoms, such as shortness ofbreath and leg oedema.

INVESTIGATIONS

A 12-­leads ECG revealed sinus rhythm and T-­wave
inversion in leads V1, V2 and V3. Transthoracic
ECG illustrated one flow just above the pulmonary
valve, preserved left ventricular ejection fraction,

Figure 1 The ECG-­gated cardiac CT revealing abnormal
vessels arising from the LAD and RCC. (A) Right-­lateralview illustrates the fistula from RCC. (B) Right-­lateralview illustrates the fistula from the LAD. (C) Superiorview illustrating the exit of fistulas. LAD, left coronary
descending artery; LCX, left coronary circumflex artery;PA, pulmonary artery; RCA, right coronary artery; RCC,
right coronary cusp.
no enlargement of the left and right heart, trivial
mitral regurgitation and trivial tricuspid regurgitation. ECG-­gated cardiac CT revealed an anomalous
vessel arising from the right coronary cusp (RCC)and a CAF from the left coronary descending artery(LAD; figure 1A,B). The abnormal blood vessels
emerging from the RCC and the CAF emerging
from the LAD coalesced, and drained into the PA(figure 1C). Coronary artery angiography revealed
no significant stenosis of the coronary artery;
however, it revealed fistulas originating in both
the RCC and LAD (figure 2A,B; videos 1 and 2).
These fistulas drained into the same portion of themain PA. Haemodynamic studies revealed that theto-­pulmonary flow ratio wasestimated systemic-­
1.18 (table 1). Step-­up of oxygen saturation wasobserved between the main PA and right ventricle
outflow (table 2). The mean PA pressure was within
normal range. Single-­photon emission CT revealed
no myocardial ischaemia.

Figure 2 Angiography of coronary artery and fistulas.
(A) Right-­cranial view illustrating the fistula originatingfrom the right coronary cusp. (B) Right-­caudal viewillustrating the fistula from the left coronary descending
artery. The two fistulas draining into the same portion of
the main PA. CAU. caudal; CRA, cranial; LAD, left coronary
descending artery; LCX, left coronary circumflex artery;PA, pulmonary artery; RAO, right anterior oblique; RCC,
right coronary cusp.

Osawa T, et al. BMJ Case Rep 2021;14:e244035. doi:10.1136/bcr-2021-244035

1

BMJ Case Rep: first published as 10.1136/bcr-2021-244035 on 23 July 2021. Downloaded from http://casereports.bmj.com/ on November 10, 2022 by guest. Protected by copyright.

Rare case of congenital coronary artery fistulacoexistent and coalesced with aortopulmonary fistulaCase report
Haemodynamic studies of the case

Aorta s/d/m

162/106/130

mm Hg

Pulmonary capillary wedgea-­wave/v-­wave/mean

6/1/3

mm Hg

PA s/d/m

21/10/14

mm Hg

Right ventricle systolic/diastolic/EDP

26/6/10mm Hg

Right atrium aa-­wave/v-­wave/mean

9/8/7

mm Hg

Heart rate

71

beats/minCardiac output (Fick)

7.15

L/min

Cardiac index (Fick)

3.97

L/min/㎡

Qp/Qs*

1.18

The estimated systemic-­to-­pulmonary flow ratio is 1.18. The pulmonary artery pressure is
within the normal range.
*Pulmonary blood flow/systemic blood flow ratio.
EDP, end-­diastolic pressure; PA, pulmonary artery; s/d/m, systolic/diastolic/mean.

OUTCOME AND FOLLOW-UP

After discharge, the patient was carefully followed up.

DISCUSSION

Video 1 Angiography of coronary artery and fistulas. Right cranial
view illustrating the fistula originating from the right coronary cusp.

DIFFERENTIAL DIAGNOSISThe differential diagnosis of this case included angina pectoris,
myocarditis, pericarditis, musculoskeletal disease and oesophageal disease.

TREATMENTThe estimated systemic-­to-­pulmonary flow ratio was low, andsingle-­photon emission CT revealed no ischaemic myocardium.
We decided against surgical intervention due to his advanced age
and lack of heart failure symptoms.

We describe a rare case of a CAF. To our knowledge, this is thefirst documented case of congenital aortopulmonary fistula
combined with CAF. These two fistulas were connected to eachother.
CAF is a rare congenital anatomical abnormality of the coronary arteries. CAF prevalence is reportedly 0.9% of all congenital heart diseases.1 CAF may arise from the LAD, left coronary
circumflex artery or the right coronary artery. Congenital
coronary-­PA fistula is a CAF and its characteristics are poorly
understood. The majority of CAFs arise from the left coronary
artery.2–4 The most common drainage site for CAF is PA (85%).4
In this case, the fistula connected the left descending coronary
artery to the PA.
Aortocardiac fistula is a rare condition. Aortopulmonary
fistula is an aortocardiac fistula. There were no reports of theprevalence of the aortopulmonary fistula. The aortocardiac
fistula is common in the aortic-­right atrium (37%) and aortic-­PA(25%).5 The origin of the aortocardiac fistula in this case was
RCC, namely the ascending aorta.
With the development of CT, it has become possible to determine the origins, drainage sites and the number of fistulas indetail.6 As in this case, the results of CT can be used to determine
examination methods and treatment strategies. Angiography ofcoronary artery and fistulas is an invasive study and has some
Table 2Oxygen saturation
O2 saturation (%)

Video 2 Angiography of coronary artery and fistulas. Right caudal
view illustrating the fistula from the left coronary descending artery.
2

Right PA

76.2

Left PA

75.3

Main PA

75.9

Right ventricle outflow

69.6

Right ventricle apex

68.1

Right ventricle inflow

69.4

Superior vena cava

72

Right atrium

70.5

Inferior vena cava

76.2

Aorta

91.2

Illustration of the step-­up of oxygen saturation between the main pulmonary artery andright ventricle outflow.
PA, pulmonary artery.

Osawa T, et al. BMJ Case Rep 2021;14:e244035. doi:10.1136/bcr-2021-244035

BMJ Case Rep: first published as 10.1136/bcr-2021-244035 on 23 July 2021. Downloaded from http://casereports.bmj.com/ on November 10, 2022 by guest. Protected by copyright.

Table 1Case reportof heart failure or pulmonary hypertension. Third, the mean PA
pressure and estimated systemic-­to-­pulmonary flow ratio were
low. Fourth, there were some risks associated with surgery due
to his advanced age.
In conclusion, this was a rare case where CAF and an aortopulmonary fistula coalesced and drained into the PA.
Acknowledgements We are grateful to Dr Yasushi Kobayashi for helpful
discussions and comments on the article.
Contributors TO and YI were part of the team that admitted the patient. TO and
TK did the literature review and article write-­up. All authors gave their final approvalof the version to be published.
Funding The authors have not declared a specific grant for this research from any
funding agency in the public, commercial or not-­for-­profit sectors.
Competing interests None declared.
Patient consent for publication Obtained.
Provenance and peer review Not commissioned; externally peer reviewed.
Open access This is an open access article distributed in accordance with theCreative Commons Attribution Non Commercial (CC BY-­NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non-­commercially,and license their derivative works on different terms, provided the original work
is properly cited and the use is non-­commercial. See: http://​creativecommons.​org/​
licenses/​by-​nc/​4.​0/.
ORCID iD
Takumi Osawa http://​orcid.​org/​0000-​0002-​1602-​7529

Learning points►► To create awareness among physicians regarding thepossibility of coexistence of exceedingly rare anatomicalpathologies, coronary artery fistula and aortopulmonary
fistula, which also coalesced before draining into thepulmonary artery. This case study will also help physicians
understand its presentation.
►► To understand that multidetector CT angiography is helpful
in investigating for congenital coronary artery fistulas andcan be used to plan the approach towards coronary artery
angiography.
►► To assess the risk of the diseases, examinations andanatomical evaluation about adult congenital heart diseases
should be done.

REFERENCES

1 Lim JJ, Jung JI, Lee BY, et al. Prevalence and types of coronary artery fistulas detectedwith coronary CT angiography. AJR Am J Roentgenol 2014;203:W237–43.
2 Verdini D, Vargas D, Kuo A, et al. Coronary-­Pulmonary artery fistulas: a systematic
review. J Thorac Imaging 2016;31:380–90.
3 Kim MS, Jung JI, Chun HJ. Coronary to pulmonary artery fistula: morphologic featuresat multidetector CT. Int J Cardiovasc Imaging 2010;26:273–80.
4 Zhou K, Kong L, Wang Y, et al. Coronary artery fistula in adults: evaluation with dual-­source CT coronary angiography. Br J Radiol 2015;88:20140754.
5 Foster TJ, Amin AH, Busu T, et al. Aorto-­cardiac fistula etiology, presentation, andmanagement: a systematic review. Heart Lung 2020;49:317–23.
6 Yun G, Nam TH, Chun EJ. Coronary artery fistulas: pathophysiology, imaging findings,
and management. Radiographics 2018;38:688–703.
7 Warnes CA, Williams RG, Bashore TM, et al. ACC/AHA 2008 guidelines for the
management of adults with congenital heart disease: Executive summary: a report of
the American College of Cardiology/American heart association Task force on practiceguidelines (writing Committee to develop guidelines for the management of adults
with congenital heart disease). Circulation 2008;118:2395–451.

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3

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risks of complications. Also, the accurate diagnosis rate is low
with invasive angiography.
The most commonly reported symptoms of CAF are chest
pain (39%) and respiratory dyspnoea (25%), with murmur being
the most common finding on physical examination (37%).2 In
this case, however, chest pain was the only symptom. The coronary ‘steal phenomenon’ is considered to be the primary cause of
CAF without coronary artery stenosis. This mechanism is related
to the flow from the high-­pressure coronary artery to a low-­
resistance PA, owing to the pressure gradient. This symptom is
associated with an increased myocardial oxygen demand during
exercise or activity.
Researchers agree that the symptomatic patients should betreated. According to the American College of Cardiology/American Heart Association guidelines, ‘percutaneous or surgicalclosure is a Class I recommendation for large fistulas regardlessof symptoms, and for small or moderate fistulas with evidenceof myocardial ischemia, arrhythmia, ventricular dysfunction,
ventricular enlargement, or endarteritis’.7 In this case, however,
we decided to treat the patient conservatively for multiple
reasons. First, his symptoms were atypical of angina pectoris anddid not worsen on exertion. The frequency of his symptoms wasapproximately once a month. Second, he exhibited no symptoms

ESC HEART FAILURE
ORIGINAL RESEARCH ARTICLE
ESC Heart Failure 2021; 8: 4187–4198
Published online 13 August 2021 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/ehf2.13555

Soluble vascular endothelial growth factor receptor 2
and prognosis in patients with chronic heart failure
Moritake Iguchi1* , Hiromichi Wada2, Tsuyoshi Shinozaki3, Masahiro Suzuki4, Yoichi Ajiro5,
Morihiro Matsuda6, Akihiro Koike7, Tomomi Koizumi8, Masatoshi Shimizu9, Yujiro Ono10, Takashi Takenaka11,
Satoru Sakagami12, Yukiko Morita13, Kazuteru Fujimoto14, Kazuya Yonezawa15, Kazuro Yoshida16,22,
Akiyo Ninomiya16, Toshihiro Nakamura17, Junichi Funada18, Yutaka Kajikawa19, Yoshifumi Oishi20, Toru Kato21,
Kazuhiko Kotani23, Mitsuru Abe1, Masaharu Akao1, Koji Hasegawa2 and for the PREHOSP-CHF Study
Investigators
1
Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto, 612-8555, Japan; 2Division ofTranslational Research, National Hospital Organization Kyoto Medical Center, Kyoto, Japan; 3Department of Cardiology, National Hospital Organization Sendai Medical
Center, Sendai, Japan; 4Department of Clinical Research, National Hospital Organization Saitama Hospital, Wako, Japan; 5Division of Clinical Research, National Hospital
Organization Yokohama Medical Center, Yokohama, Japan; 6Division of Preventive Medicine Institute for Clinical Research, National Hospital Organization Kure Medical
Center and Chugoku Cancer Center, Kure, Japan; 7Department of Cardiology, National Hospital Organization Fukuokahigashi Medical Center, Koga, Japan; 8Department of
Cardiology, National Hospital Organization Mito Medical Center, Ibaraki, Japan; 9Department of Cardiology, National Hospital Organization Kobe Medical Center, Kobe,
Japan; 10Department of Cardiology, National Hospital Organization Higashihiroshima Medical Center, Hiroshima, Japan; 11Department of Cardiology, National Hospital
Organization Hokkaido Medical Center, Sapporo, Japan; 12Institute for Clinical Research, National Hospital Organization Kanazawa Medical Center, Kanazawa, Japan;
13
Department of Cardiology, National Hospital Organization Sagamihara Hospital, Sagamihara, Japan; 14Department of Cardiology, National Hospital Organization
Kumamoto Medical Center, Kumamoto, Japan; 15Division of Clinical Research, National Hospital Organization Hakodate Hospital, Hakodate, Japan; 16Department of
Cardiology, National Hospital Organization Nagasakikawadana Medical Center, Higashisonogi, Japan; 17Department of Cardiology, National Hospital Organization Kyushu
Medical Center, Fukuoka, Japan; 18Department of Cardiology, National Hospital Organization Ehime Medical Center, Toon, Japan; 19Department of Cardiology, National
Hospital Organization Fukuyama Medical Center, Fukuyama, Japan; 20Department of Cardiology, National Hospital Organization Higashitokushima Medical Center, Itano,
Japan; 21Department of Clinical Research, National Hospital Organization Tochigi Medical Center, Utsunomiya, Japan; 22Department of Cardiology, National Hospital
Organization Nagasaki Hospital, Nagasaki, Japan; and 23Division of Community and Family Medicine, Jichi Medical University, Shimotsuke, Japan

Abstract
Aims Endothelial cell vascular endothelial growth factor receptor 2 (VEGFR-2) plays a pivotal role in angiogenesis, which
induces physiological cardiomyocyte hypertrophy via paracrine signalling between endothelial cells and cardiomyocytes. We
investigated whether a decrease in circulating soluble VEGFR-2 (sVEGFR-2) levels is associated with poor prognosis in patientswith chronic heart failure (HF).
Methods and results We performed a multicentre prospective cohort study of 1024 consecutive patients with HF, who wereadmitted to hospitals due to acute decompensated HF and were stabilized after initial management. Serum levels of sVEGFR-2
were measured at discharge. Patients were followed up over 2 years. The outcomes were cardiovascular death, all-cause death,major adverse cardiovascular events (MACE) deﬁned as a composite of cardiovascular death and HF hospitalization, and HF hospitalization. The mean age of the patients was 75.5 (standard deviation, 12.6) years, and 57% were male. Patients with lowersVEGFR-2 levels were older and more likely to be female, and had greater proportions of atrial ﬁbrillation and anaemia, andlower proportions of diabetes, dyslipidaemia, and HF with reduced ejection fraction (<40%). During the follow-up, 113 cardiovascular deaths, 211 all-cause deaths, 350 MACE, and 309 HF hospitalizations occurred. After adjustment for potential clinicalconfounders and established biomarkers [N-terminal B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin I,
and high-sensitivity C-reactive protein], a low sVEGFR-2 level below the 25th percentile was signiﬁcantly associated withcardiovascular death [hazard ratio (HR), 1.79; 95% conﬁdence interval (CI), 1.16–2.74] and all-cause death (HR, 1.43; 95% CI,
1.04–1.94), but not with MACE (HR, 1.11; 95% CI, 0.86–1.43) or HF hospitalization (HR, 1.03; 95% CI, 0.78–1.35). The
stratiﬁed analyses revealed that a low sVEGFR-2 level below the 25th percentile was signiﬁcantly associated withcardiovascular death (HR, 1.76; 95% CI, 1.07–2.85) and all-cause death (HR, 1.49; 95% CI, 1.03–2.15) in the high-NT-proBNP
group (above the median), but not in the low-NT-proBNP group. Notably, the patients with high-NT-proBNP and low-sVEGFR2 (below the 25th percentile) had a 2.96-fold higher risk (95% CI, 1.56–5.85) for cardiovascular death and a 2.40-fold higher risk
(95% CI, 1.52–3.83) for all-cause death compared with those with low-NT-proBNP and high-sVEGFR-2.

© 2021 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium,provided the original work is properly cited, the use is non-commercial and no modiﬁcations or adaptations are made.

4188

M. Iguchi et al.

Conclusions A low sVEGFR-2 value was independently associated with cardiovascular death and all-cause death in patientswith chronic HF. These associations were pronounced in those with high NT-proBNP levels.
Keywords

Heart failure; Biomarker; Angiogenesis; Lymphangiogenesis; Mortality

Received: 22 March 2021; Revised: 9 July 2021; Accepted: 28 July 2021
*Correspondence to: Moritake Iguchi, Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto
612-8555, Japan. Phone: +81 75-641-9161; Fax: +81 75-643-4325. Email: moritake@kuhp.kyoto-u.ac.jp

Introduction
Despite current advances in therapy, heart failure (HF)
remains a leading cause of hospitalization and mortality
worldwide, and its prevalence is progressively increasing in
aging societies.1 HF is a clinical syndrome with several underlying aetiologies. Basic studies have demonstrated thatdysfunction of angiogenesis is a major cause of the pathogenesis and progression of advanced HF, regardless of theaetiology2: cardiac tissue growth is angiogenesis-dependent,
and disruption of coordinated cardiac hypertrophy and angiogenesis causes the transition from adaptive cardiac hypertrophy to decompensated HF.3,4
Vascular endothelial growth factor (VEGF) is a key cytokine
in angiogenesis.5 The VEGF family consists of ﬁve members[VEGF (or VEGF-A), placental growth factor, VEGF-B, VEGF-C,
and VEGF-D] and three tyrosine kinase receptors (VEGFR-1,-2, and -3). Among the pathways involving these members,
the VEGF/VEGFR-2 pathway plays a major role in angiogenesis. A recent study demonstrated that crosstalk between theendothelial cell VEGFR-2 and cardiac myocyte ErbB signalling
pathways coordinates cardiac myocyte hypertrophy with angiogenesis, and thereby contributes to physiological cardiac
growth.6 A soluble form of VEGFR-2 (sVEGFR-2) is generated
by proteolytic hydrolysis of membrane-bound VEGFR-2 or byalternative splicing,7,8 and sVEGFR-2 can be measured in serum and plasma by immunoassay.9 Circulating levels ofsVEGFR-2 were reported to serve as a surrogate biomarker
of VEGF-mediated tumour growth in patients with various
cancers.10–12 However, the clinical signiﬁcance of sVEGFR-2
in patients with chronic HF (CHF) is unknown. We therefore
performed a multicentre prospective cohort study to investigate the prognostic value of serum sVEGFR-2 levels in patients with CHF.

Methods
Study population
The PREHOSP-CHF study (Development of Novel Biomarkers
to Predict REHOSPitalization in Chronic Heart Failure) is a nationwide, multicentre prospective cohort study to determine
the predictive value of possible novel biomarkers related toangiogenesis or lymphangiogenesis for cardiovascular (CV)events in patients with CHF (UMIN Clinical Trials Registry:
UMIN000021657). We enrolled 1065 patients with HF, whowere admitted to hospitals due to acute decompensated HF
and were stabilized after initial management, between
December 2015 and October 2017 in the 21 National Hospital
Organization institutions across Japan. The present study was
conducted by nationally certiﬁed cardiologists. Acute decompensated HF was deﬁned by the modiﬁed Framingham
criteria. The exclusion criteria are described in the Supporting
Information (Supplemental Methods section). After excluding
41 patients who were subsequently found to be ineligible (31patients) or who withdrew consent (10 patients), a total of1024 patients were included in the analyses. The study wasapproved by the central ethics committee of the National
Hospital Organization headquarters and each institution’s
ethical committee. All of the patients provided written informed consent.

Exposures, sample collection, and biomarker
measurement
The primary predictor was the serum level of sVEGFR-2 at thetime of nearest discharge. The serum levels of sVEGFR-2,
VEGF, and high-sensitivity C-reactive protein (hs-CRP) were
measured with speciﬁc, commercially available ELISA kits according to the manufacturers’ instructions: sVEGFR-2 andVEGF were measured using a Quantikine kit (R&D Systems,
Minneapolis, MN), and hs-CRP was measured with a CycLex
kit (Medical & Biological Laboratories [MBL], Nagano,
Japan).13 These assays were performed by an investigatormasked to the sources of the samples. The serum levels of
N-terminal pro-brain natriuretic peptide (NT-proBNP) were
measured using a validated, sandwich electrochemiluminescence immunoassay (Elecsys; Roche Diagnostics,
Indianapolis, IN). The sensitivities of the assays for VEGF,
sVEGFR-2, and hs-CRP were 5.0, 4.6, and 28.6 pg/mL, respectively. The inter-assay/intra-assay coefﬁcients of variation of
ELISA for VEGF, sVEGFR-2, and hs-CRP were <9%/<7%,≤7%/<5%, and <6%/<4%, respectively. The sensitivity of
the assay for NT-proBNP was 5 pg/mL, and the assay coefﬁcients of variation at values of the measuring range(5–35 000 pg/mL) were <10%. The high-sensitivity cardiac
troponin I (hs-cTnI) values were measured using a cardiac
ESC Heart Failure 2021; 8: 4187–4198
DOI: 10.1002/ehf2.13555

4189

sVEGFR-2 and prognosis in CHF

troponin assay (Architect Stat High-sensitive Troponin I;
Abbott Laboratories, Abbott Park, IL). The limit of detectionin this assay is 1.9 pg/mL (range, 0–50 000 pg/mL) and the99th percentile cut-off is 26.2 pg/mL. Additional details aredescribed in the Supporting Information (Supplemental
Methods section).

Study endpoints
The endpoints in the analyses were CV death, all-cause death,major adverse cardiovascular events (MACE) deﬁned as acomposite of CV death and HF-related hospitalization, andHF-related hospitalization. Causes of death were adjudicatedafter consideration of all the available information and wereclassiﬁed according to the following pre-speciﬁed groups:
CV death and non-CV death. CV death included death related
to HF, acute coronary syndrome, stroke, and other vasculardisease, and sudden death. Sudden death was deﬁned as
death related to fatal arrhythmia or unexplained death in apreviously stable patient. Patients were followed up over
2 years. At the end of the follow-up period (Day 720), survival
status and detailed information about MACE were available
in 1009 patients (follow-up rate, 98.5%).

Statistical analysis
We divided the patients into quartiles according to their baseline sVEGFR-2 levels. The categorical variables are presented
as numbers and percentages and were compared using a χ 2
test. The continuous variables are expressed as mean withstandard deviation or median with interquartile range. On
the basis of their distributions, the continuous variables werecompared using ANOVA or the Kruskal–Wallis test. The P
values for trends across the groups were calculated using
the Cochran–Armitage test for categorical variables and the
Jonckheere–Terpstra test for continuous variables. The cumulative incidences of clinical outcomes were estimated by the
Kaplan–Meier method and Cox proportional hazard regression. For competing risk analyses, we used the Fine and Graymodel to estimate the subdistribution hazard ratio (HR). The
relationships between the baseline biomarker levels and theoutcomes were investigated with the use of Cox proportional
hazard regression. In our prior exploratory study of 254 CHF
patients, the incidence of MACE over a 2 year follow-up period was 29.1% in a low sVEGFR-2 group (below the median)and 17.3% in a high sVEGFR-2 group. To realize 99% powerfor MACE, we estimated that a sample size of 785 patientswas required. We increased this sample size by 20% to account for potential loss to follow-up, arriving at a ﬁnal sample
size of 1050 patients with an expected MACE incidence of 242
patients. We therefore included no more than 24 variables forthe statistical analysis, because that was the highest numberof variables supported by the anticipated incidence of MACE.
We used four sets of models to conﬁrm the consistency of theassociation between sVEGFR-2 levels and clinical events:
Model 1, adjusted for age, sex, body mass index, and traditional CV risk factors (i.e. hypertension, diabetes, anddyslipidaemia), as well as established risk factors for HF [i.e.
prior HF hospitalization, left ventricular dysfunction deﬁned
as an ejection fraction (EF) < 40%, and NYHA class 3/4];
Model 2, adjusted for the covariates included in Model 1
and other CV risk factors [i.e. coronary artery disease, old
myocardial infarction, atrial ﬁbrillation (AF), chronic kidneydisease (CKD, deﬁned as an estimated glomerular ﬁltration
rate of less than 60 mL/min/1.73 m2), anaemia (deﬁned as
haemoglobin levels of less than 13 g/dL in male participants
and 12 g/dL in female participants), chronic obstructive
pulmonary disease, and cerebrovascular disease]; Model 3,
adjusted for the covariates included in Model 2 and prescription of renin–angiotensin system inhibitors (RAS-I), beta
blockers, loop diuretics, and mineral corticoid receptor
antagonists (MRA); and Model 4, adjusted for the covariates
included in Model 3 and CV biomarkers [NT-proBNP, hs-cTnI,and hs-CRP (>1 mg/L)].
Subgroup analyses were performed based on the independent determinants for sVEGFR-2 level in the multiple regression analysis, EF category (HFpEF: HF with preserved EF(≥50%); HFmrEF: HF with mid-range EF (40–49%); and HFrEF:
HF with reduced EF [<40%]), and aetiology of HF (ischaemic
or non-ischaemic).
All statistical tests were two-sided, and P < 0.05 wasconsidered signiﬁcant. Analyses were performed using JMP
version 12 (SAS Institute, Cary, NC) and R, version 3.4.4(R Foundation for Statistical Computing, Vienna, Austria).

Results
Baseline characteristics
The distribution of sVEGFR-2 values is shown in Figure S1. We
divided the patients into four groups based on the quartile ofthe sVEGFR-2 levels (Quartile 1: sVEGFR-2 < 5259 pg/mL;
Quartile 2: 5259 pg/mL ≤ VEGFR-2 < 6120 pg/mL; Quartile
3: 6120 pg/mL ≤ sVEGFR-2 < 7210 pg/mL; and Quartile 4:sVEGFR-2 ≥ 7210 pg/mL). The baseline characteristics of theentire cohort and of the patients divided into quartiles ofsVEGFR-2 levels are shown in Tables 1 and S1. Patients with
Quartile 1 sVEGFR-2 levels were older and had higher rates
of female sex, lower body mass index, AF, and anaemia, while
they had lower prevalence of diabetes and dyslipidaemia
compared with those with Quartiles 2–4 sVEGFR-2 levels.
Prevalence of HFpEF was higher in those with Quartile 1
sVEGFR-2 levels than in those with Quartiles 2–4 sVEGFR-2
levels.
ESC Heart Failure 2021; 8: 4187–4198
DOI: 10.1002/ehf2.13555

75.5 (12.6)
601 (58.7)
22.2 (4.9)
114.8 (17.8)
65.6 (12.6)
70.1 (13.6)
139 (13.6)
429/186/409(41.9/18.2/39.9)
252 (24.6)
777 (75.9)396 (38.7)
418 (40.8)
322 (31.4)
319 (31.2)
181 (17.7)
533 (52.1)
527 (51.5)
577 (56.4)
77 (7.5)
176 (17.2)
725 (70.8)
752 (73.4)
871 (85.1)
441 (43.1)
1493 [664–3395]
21.9 [12.1–47.4]
2.48 [0.83–8.87]
377 [223–632]

Entire cohort1024
80 (10.7)
132 (51.6)
20.9 (4.1)
112.8 (17)63.8 (12.9)
69.5 (13.9)
34 (13.3)
127/51/78(49.6/19.9/30.5)
48 (18.8)
189 (73.8)
83 (32.4)
81 (31.6)
80 (31.3)
74 (28.9)33 (12.9)
153 (59.8)
128 (50.0)
174 (68.0)
22 (8.6)
51 (19.9)
176 (68.8)
167 (65.2)
213 (83.2)
111 (43.4)
1404 [674–3493]
22.5 [13.4–47.8]
2.74 [0.87–9.52]
324 [189–521]

Quartile 1
25677.3 (11.8)
133 (52.0)
22 (4.8)
115.5 (17.3)
66.7 (13.0)
70.3 (13.6)
38 (14.9)
103/45/108(40.2/17.6/42.2)
69 (27.0)
196 (76.6)
99 (38.7)
118 (46.1)
93 (36.3)
87 (34.0)
48 (18.8)
144 (56.3)
143 (55.9)
143 (55.9)
23 (9.0)45 (17.6)
183 (71.5)
189 (73.8)225 (87.9)
117 (45.7)
1591 [680–3560]
21.8 [12.0–45.6]
2.35 [0.78–9.68]
401 [231–666]

Quartile 2
25674.7 (11.9)
167 (65.2)
22.5 (4.4)
114.8 (18.6)
65 (12.5)
69.8 (13.8)
36 (14.1)
104/48/104(40.6/18.8/40.6)
65 (25.4)
202 (78.9)
91 (35.5)
99 (38.7)
83 (32.4)
77 (30.1)
48 (18.8)135 (52.7)
134 (52.3)
138 (53.9)
21 (8.2)
37 (14.5)
172 (67.2)
195 (76.2)
212 (82.8)
106 (41.4)
1656 [706–3289]
20.0 [10.5–44.6]
2.05 [0.86–7.62]
366 [226–636]

Quartile 3
25670.2 (13.7)
169 (66.0)
23.3 (5.7)
116.2 (18)66.7 (11.9)
70.9 (13.2)
31 (12.1)
95/42/119(37.1/16.4/46.5)
70 (27.3)
190 (74.2)
123 (48)120 (46.9)
66 (25.8)
81 (31.6)
52 (20.3)
101 (39.5)
122 (47.7)
122 (47.7)
11 (4.3)
43 (16.8)
194 (75.8)201 (78.5)
221 (86.3)
107 (41.8)
1389 [598–3201]
23.9 [13.8–52.6]
3.11 [0.83–8.76]
438 [243–672]

Quartile 4
256

0.04
0.8
0.001
0.005
0.1
0.7
0.04
<0.0010.5<0.001
0.06
0.2
0.2<0.001
0.7
0.5
0.5
0.8
0.8
0.001

<0.001<0.001<0.001
0.07
0.052
0.3
0.6
<0.001<0.001<0.001<0.001
0.2
0.02
0.7
0.8
0.02
0.08
0.5
0.002
<0.0010.08
0.6
0.1<0.001
0.3<0.001
0.1
0.4
0.1
0.005
0.3
0.8
0.7
0.3
0.4
<0.001

P for
trend

P value

Continuous variables are expressed as the mean (standard deviation) or median [interquartile range] according to the distributions. Categorical variables are presented as numbers(percentages).
AF, atrial ﬁbrillation; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; DBP, diastolic blood pressure; EF, ejection fraction; Hb, haemoglobin; HF, heart failure,
HFmrEF, HF with mid-range EF; HFpEF, HF with preserved EF; HFrEF, HF with reduced EF; hs-CRP, high-sensitivity C-reactive protein; hs-cTnI, high sensitivity cardiac troponin I; MRA, mineral corticoid receptor antagonists; NT-proBNP, N-terminal pro-brain natriuretic peptide; RAS-I, renin angiotensin system inhibitor; SBP, systolic blood pressure; VEGF, vascular endothelial growth factor; VEGFR-2, soluble vascular endothelial growth factor receptor 2.
aAnaemia was deﬁned as a haemoglobin level <13 g/dL in male participants and <12 g/dL in female participants.

Ischaemic aetiology
Hypertension
Diabetes
Dyslipidaemia
Prior HF hospitalization
Coronary artery disease
Old myocardial infarction
AF
CKDaAnaemia
COPD
Cerebrovascular disease
RAS-I
Beta blockers
Loop diuretics
MRA
NT-proBNP (pg/mL)
hs-cTnI (pg/mL)
hs-CRP (mg/L)
VEGF (pg/mL)

Age (years)
Male gender2
Body mass index (kg/m )SBP (mmHg)
DBP (mmHg)
Pulse rate (bpm)
NYHA 3/4
HFpEF/mrEF/rEF

VariableN

Table 1 Baseline characteristics according to quartiles of sVEGFR-2

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sVEGFR-2 and prognosis in CHF

In simple regression analyses, the sVEGFR-2 level was inversely correlated with EF, and positively correlated with
the VEGF levels, but it was not signiﬁcantly correlated withthe levels of established CV biomarkers (NT-proBNP, hs-cTnI,and hs-CRP) (Table S2). Stepwise multiple regression analysis
revealed that independent determinants of the sVEGFR-2
level were lower age, male gender, presence of diabetes,
absence of AF and anaemia, and higher levels of NT-proBNP(Table S2).

Incidence of outcomes and Cox regression
analysesDuring the 720 day follow-up, a total of 113 (11.0%) CV
deaths, 211 (20.6%) all-cause deaths, 350 (34.2%) MACE,
and 309 (30.2%) HF-related hospitalizations occurred(Table 2). Figure 1 shows the cumulative incidence ofoutcomes and unadjusted Cox proportional HRs according
to quartiles of sVEGFR-2. The patients with Quartile 1
sVEGFR-2 levels showed the greatest risks of CV death[P = 0.01; HR, 1.54; 95% conﬁdence interval (CI), 0.96–2.51(vs. Quartile 2); HR, 1.75; 95% CI, 1.07–2.91 (vs. Quartile 3);
HR, 2.32; 95% CI, 1.37–4.09 (vs. quartile 4)] and all-cause
death [P = 0.004; HR, 1.51; 95% CI, 1.06–2.18 (vs. Quartile2); HR, 1.57; 95% CI, 1.09–2.26 (vs. Quartile 3); HR, 1.94;
95% CI, 1.32–2.87 (vs. quartile 4)], but did not show a significantly higher risk of MACE (P = 0.7) or HF-related hospitalization (P = 0.97) than the other quartile groups. In contrast,
quartiles of VEGF were not signiﬁcantly associated with therisk of CV death, all-cause death, MACE, or HF-related hospitalization (Figure S2). Because there was an apparent threshold effect between Quartile 1 and Quartile 2 in the incidenceof all-cause death and CV death, sVEGFR-2 was modelled as adichotomous variable in subsequent analyses by applying athreshold of Quartile 1 vs. Quartiles 2–4. In competing risk
analyses, the patients with Quartile 1 sVEGFR-2 also showedsigniﬁcantly higher incidence of CV death than those with
Quartiles 2–4 (HR, 1.77; 95% CI 1.21–2.56), but not that ofMACE (HR, 1.13; 95% CI, 0.89–1.43) or HF-related hospitalization (HR, 1.00; 95% CI, 0.77–1.29) (Figure S3).
Figure 2 shows the unadjusted and adjusted HRs of low
sVEGFR-2 levels below the 25th percentile for the various
outcomes. After adjusting for traditional risk factors, a low

sVEGFR-2 level below the 25th percentile was signiﬁcantly
associated with the risk of CV death, but not with the riskof all-cause death, MACE, or HF-related hospitalization(Figure 2, Model 1). After additional adjustment for other potential clinical confounders, a low sVEGFR-2 level below the25th percentile was signiﬁcantly associated with the risk of
CV death, but not with the risk of all-cause death, MACE, or
HF-related hospitalization (Figure 2, Model 2). Even after
additional adjustment for medications for HF, these associations were consistent (Figure 2, Model 3). Importantly, after
additional adjustment for established CV biomarkers, a low
sVEGFR-2 level below the 25th percentile was signiﬁcantly
associated with the risk of CV death (HR, 1.79; 95% CI,
1.16–2.74) and all-cause death (HR, 1.43; 95% CI,
1.04–1.94), but not with the risk of MACE (HR, 1.11; 95%
CI, 0.86–1.43) or HF-related hospitalization (HR, 1.03; 95%
CI, 0.78–1.35) (Figure 2, Model 4, Table S3).

Subgroup analyses
Figure 3 shows the results of subgroup analyses on the association of low sVEGFR-2 below the 25th percentile with CV
death and all-cause death. A low sVEGFR-2 level below the25th percentile was signiﬁcantly associated with all-cause
death in men, but not in women. The risk for CV deathtended to be higher in those with a low sVEGFR-2 level below
the 25th percentile compared with those with a highsVEGFR-2 level in men (HR, 1.67; 95% CI, 0.95–2.86), but
not in women (HR, 1.25; 95% CI, 0.63–2.38).
Interestingly, a low sVEGFR-2 level below the 25th percentile was signiﬁcantly associated with CV death (HR, 1.76; 95%
CI, 1.07–2.85) and all-cause death (HR, 1.49; 95% CI, 1.03–
2.15) in the high-NT-proBNP (>50th percentile) group, but
not with either CV death (HR, 1.33; 95% CI, 0.50–3.52) or
all-cause death (HR, 1.07; 95% CI, 0.56–1.99) in the low-NTproBNP group after adjustment for potential clinical confounders (Figure 3).
Figure S4 shows the results of Kaplan–Meier analysis basedon the subgroups of EF category. A low sVEGFR-2 level below
the 25th percentile was associated with CV death (HR, 1.99;
95% CI, 1.20–3.22) and all-cause death (HR, 1.78; 95% CI,
1.24–2.53) in patients with HFpEF/HFmrEF, but not in those

Table 2 Incidence of events according to quartiles of sVEGFR-2
Event

Entire cohort

Quartile 1

Quartile 2

Quartile 3

Quartile 4

CV death
All-cause death
MACE (CV death + HF hospitalization)
HF hospitalization

113 (11.0)
211 (20.6)
350 (34.2)
309 (30.2)

41 (16.0)
72 (28.1)
94 (36.7)
77 (30.1)

28 (10.9)50 (19.5)
85 (33.2)
74 (28.9)

25 (9.8)
49 (19.1)
85 (33.2)
77 (30.1)

19 (7.4)
40 (15.6)
86 (33.6)
81 (31.6)

Variables are presented as numbers (percentages).
CV, cardiovascular; HF, heart failure; MACE, major adverse cardiovascular events deﬁned as a composite of CV death and HF-related hospitalization; sVEGFR-2, soluble vascular endothelial growth factor receptor 2.

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Figure 1 Incidence of CV death (A), all-cause death (B), MACE (C), and HF-related hospitalization (D) according to the quartiles of baseline sVEGFR-2
levels during the follow-up period. CI, conﬁdence interval; CV, cardiovascular; HF, heart failure; HR, hazard ratio, MACE, major adverse cardiovascular
events deﬁned as a composite of CV death and HF-related hospitalization; Q, quartile; sVEGFR-2, soluble vascular endothelial growth factor receptor 2.

with HFrEF (HR for CV death, 1.29; 95% CI, 0.65–2.41; HR for
all-cause death, 1.38; 95% CI, 0.84–2.21).
Figure S5 shows Kaplan–Meier analysis based on the
aetiology of HF (ischaemic or non-ischaemic). A lowsVEGFR-2 level below the 25th percentile was associated withCV death (HR, 1.99; 95% CI, 1.28–3.03) and all-cause death(HR, 1.66; 95% CI, 1.18–2.29) in patients with non-ischaemic
aetiology, but not in those with ischaemic aetiology (HR for
CV death, 0.43; 95% CI, 0.12–1.27; HR for all-cause death,
1.32; 95% CI, 0.66–2.54).

Risk stratiﬁcation by NT-proBNP and sVEGFR-2
Figure 4 shows the cumulative incidence (A and B) andunadjusted and adjusted HRs (C) for CV death and all-cause
death in patients divided into 4 groups based on the medianof NT-proBNP and the 25th percentile of sVEGFR-2 levels.
Notably, the high-NT-proBNP/low-sVEGFR-2 group exhibitedthe highest risks of CV death and all-cause death among thefour groups, even after adjustment for potential clinicalconfounders and the established CV biomarkers (Figure 4C).
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Figure 2 Multivariate Cox proportional hazard analysis for CV death, all-cause death, MACE, and HF-related hospitalization. Model 1: adjusted for age,sex, body mass index, and traditional cardiovascular risk factors (hypertension, diabetes, and dyslipidaemia), as well as established risk factors for HF[prior HF hospitalization, left ventricular dysfunction (ejection fraction < 40%), and NYHA class 3/4]. Model 2: adjusted for the covariates included inModel 1 and other CV risk factors (CAD, old myocardial infarction, AF, CKD, anaemia, chronic obstructive pulmonary disease, and cerebrovascular disease). Model 3: adjusted for the covariates included in Model 2 and prescription of RAS-I, beta blockers, loop diuretics, and MRA. Model 4: adjusted forthe covariates included in Model 3 and CV biomarkers [NT-proBNP, hs-cTnI, and hs-CRP (>1 mg/L)]. AF, atrial ﬁbrillation; BMI, body mass index; CAD,
coronary artery disease; CKD, chronic kidney disease; hs-CRP, high sensitivity C-reactive protein; hs-cTnI, high sensitivity cardiac troponin I; MRA, mineral corticoid receptor antagonists; NT-proBNP, N-terminal pro-brain natriuretic peptide; RAS-I, renin angiotensin inhibitor. Other abbreviations aredeﬁned in Figure 1.

These ﬁndings suggest the incremental prognostic valueof sVEGFR-2 in combination with NT-proBNP in patientswith CHF.

Discussion
This is the ﬁrst dedicated and large-scale prospective cohort
study to demonstrate that a low sVEGFR-2 level below the25th percentile is signiﬁcantly associated with the risk of CV

and all-cause mortality in patients with CHF. These
associations remain signiﬁcant after adjustment for potential
clinical confounders and the established CV biomarkers of
NT-proBNP, hs-cTnI, and hs-CRP. Furthermore, the additionof a low sVEGFR-2 level below the 25th percentile to themodel with potential clinical confounders signiﬁcantly improved the prediction of CV and all-cause mortality in patientswith high NT-proBNP levels. These ﬁndings suggest that themeasurement of sVEGFR-2 provides prognostic information
about CV and all-cause mortality beyond potential clinicalconfounders and the established CV biomarkers in clinical
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M. Iguchi et al.

Figure 3 Multivariate-adjusted stratiﬁed analyses of the associations of low sVEGFR-2 below the 25th percentile and the risks of CV death andall-cause death. Data were adjusted for the covariates included in Model 3 in Figure 2. *We divided the patients according to the median of NTproBNP. EF, ejection fraction; LV, left ventricular. Other abbreviations are deﬁned in Figures 1 and 2.

settings. The strengths of our investigation include the large
sample size, multicentre prospective design, and highfollow-up rate.

The underlying mechanism of the relationship between alow sVEGFR-2 value and poor prognosis in patients with CHF
should be considered. VEGFR-2 is mainly expressed in
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4195

Figure 4 Incidence of CV death (A) and all-cause death (B) according to the combination of baseline sVEGFR-2 (lowest quartile) and NT-proBNP (above
the median) levels during the follow-up period, and unadjusted and adjusted hazard ratios of the combination of sVEGFR-2 and NT-proBNP for CV
death and all-cause death (C). The covariates included in each model are shown in Figure 2. Abbreviations are as deﬁned in Figures 1 and 2.

endothelial cells,14 and a naturally occurring sVEGFR-2 wasproduced by proteolytic hydrolysis of membrane-bound
VEGFR-2 or alternative splicing of VEGFR-2.7,15 sVEGFR-2 protein and messenger RNA were found to be present in various
tissues, including the skin, heart, spleen, kidney, ovary, andplasma in wild-type mice.16 The VEGF/VEGFR-2 signalling
pathway plays an important role in endothelial dysfunction,17

which is one of the characteristic pathophysiological featuresof CHF.18 Endothelium–cardiomyocyte interactions playessential roles in CV homeostasis; deranged endotheliumrelated signalling pathways have been implicated in the pathophysiology of HF.19 A recent study demonstrated that crosstalk between the endothelial VEGFR-2 and cardiomyocyte
ErbB signalling pathways is required for adaptive cardiac
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M. Iguchi et al.

hypertrophy.6 On the other hand, systemic vasoconstriction
associated with endothelin dysfunction has been suggested
to play a central role in HF pathogenesis: dysfunction of
the endothelium leads to increased vascular stiffness andimpaired arterial distensibility, augmenting myocardialdamage.20,21 Taken together, these ﬁndings indicate that a decrease in circulating sVEGFR-2 may reﬂect down-regulation of
membrane-bound VEGFR-2 in cardiac and systemic endothelial cells, leading to a vulnerability to decompensated HF.
Further investigation is necessary to clarify the mechanisticrole of sVEGFR-2 in HF.
In the present study, the associations of low sVEGFR-2 withCV death and all-cause death were pronounced in patientswith HFpEF. Not only cardiac microvascular rarefaction but
also peripheral vascular dysfunction has been associated with
impairment of cardiac reserve function in HFpEF.22–25 Theseprevious and our present ﬁndings may also support the ideathat a low sVEGFR-2 reﬂects both cardiac and vascular endothelial dysfunction, leading to a deterioration of the prognosis in HFpEF.
The association between a low sVEGFR-2 value andall-cause death was signiﬁcant in men, but not in women.
The risk for CV death also tended to be higher in patients withlow sVEGFR-2 compared with those with high sVEGFR-2 inmen, but not in women. These results suggest the possibilityof a sex difference in the pathophysiology of sVEGFR-2. Previous studies suggested a sex-based difference in the endogenous expression of VEGF.26,27 Thus, systemic or CV
expression of VEGF may affect the circulating sVEGFR-2 level
and/or its association with prognosis in CHF. Future studies
will be needed to deﬁne the mechanisms underlying the sex
difference in the association between the sVEGFR-2 leveland prognosis in CHF.
The associations of a low VEGFR-2 value with CV andall-cause deaths became prominent when adjusting for or
stratifying based on NT-proBNP, a marker of increased left
ventricular wall stress.28 These ﬁndings may suggest that endothelial dysfunction itself does not lead to HF, but in combination with an increased left ventricular wall stress, it canlead to decompensated HF.
The VEGF/VEGFR2 signalling pathway plays a crucial role intumour angiogenesis29 as well as CV endothelial integrity.
Since HF was subsequently noted in 2–4% of patients withcancers who were taking VEGF signalling pathway
inhibitors,30 it would be of interest to determine whether
the sVEGFR-2 level can predict incident HF in combination
with NT-proBNP among patients with cancer who are receiving VEGF signalling pathway inhibitors.

therefore, they only reﬂect association and not causality.
Second, the sources and the detailed physiological and pathological roles of sVEGFR-2 have not been elucidated.7 Further
investigations will be needed to answer these questions.
Third, we did not repeatedly measure the sVEGFR-2 levels
during the follow-up period. The predictive signiﬁcance of achange in sVEGFR-2 levels in patients with HF is unclear.
Fourth, we did not measure the levels of VEGF-C, a central
regulator of lymphangiogenesis, and the other ligand forsVEGFR-2. Recently, we demonstrated that low VEGF-C is inversely and independently associated with the risk of
all-cause mortality in patients with suspected or known coronary artery disease.31 Future studies will deﬁne the prognostic value of VEGF-C in patients with CHF. Finally, because the
PREHOSP-CHF study cohort consists exclusively of Asian individuals with HF, our results may not be generalizable to general Asian populations, or to other ethnic groups. Despite
these limitations, the present study provides not only betterrisk stratiﬁcation in patients with CHF but also deeper insight
into the role of angiogenesis in the mechanisms of HF.

Study limitations

Funding

The present study has several limitations. First, the results
were derived from a prospective observational study;

The PREHOSP-CHF study is supported by a Grant-in-Aid forClinical Research from the National Hospital Organization.

Conclusions
In a multicentre prospective cohort, a low sVEGFR-2 value
was independently associated with CV death and all-cause
death among patients with CHF, especially in those with highNT-proBNP levels.

Acknowledgements
We thank Mr Shuichi Ura, who substantially contributed tothis work but passed away before this manuscript wasdrafted. We also thank the other members, co-operators,
and participants of the PREHOSP-CHF study for their valuable
contributions. The other members of the PREHOSP-CHF
study group are listed in the Appendix of the Supporting
Information.
The hs-cTnI values were measured at Abbott Japan LLC(Minato-ku, Tokyo, Japan).

Conﬂict of interest
None of the authors has any relationship relevant to thecontent of this paper to disclose.

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sVEGFR-2 and prognosis in CHF

Supporting information
Additional supporting information may be found online in the
Supporting Information section at the end of the article.
Figure S1. Distribution of the sVEGFR-2 levels. sVEGFR-2 = soluble vascular endothelial growth factor receptor 2.
Figure S2. Incidence of CV death (A), all-cause death (B),
MACE (C), and HF-related hospitalization (D) according to
the quartiles of baseline VEGF levels during the follow-up period. VEGF = vascular endothelial growth factor,
CV = cardiovascular, MACE = major adverse cardiovascular
events deﬁned as a composite of CV death and HF-related
hospitalization, HF = heart failure, HR = hazard ratio,
CI = conﬁdence interval.
Figure S3. Competing risk of death adjusted cumulative hazard curves for incident CV death (A), MACE (B), andHF-related hospitalization (C) in patients with and without
low sVEGFR-2 levels below the 25th percentile during thefollow-up period. sVEGFR-2 = soluble vascular endothelial
growth factor receptor 2, CV = cardiovascular,
MACE = major adverse cardiovascular events deﬁned as acomposite of CV death and HF-related hospitalization,
HF = heart failure, HR = hazard ratio, CI = conﬁdence interval.

Figure S4. Incidence of CV death (A) and all-cause death (B)between patients with and those without low sVEGFR-2
levels below the 25th percentile in the HFpEF/mrEF and
HFrEF groups. Adjusted HR was calculated using the covariates included in model 3. sVEGFR-2 = soluble vascular endothelial growth factor receptor 2, CV = cardiovascular,
HR = hazard ratio, CI = conﬁdence interval, HF = heart failure,
HFpEF = HF with preserved ejection fraction (EF),HFmrEF = HF with mid-range EF, HFrEF = HF with reduced EF.
Figure S5. Incidence of CV death (A) and all-cause death (B)between patients with and those without low sVEGFR-2
levels below the 25th percentile in the groups with HF withischemic etiology and HF with non-ischemic etiology. Adjusted HR was calculated using the covariates included inmodel 3. sVEGFR-2 = soluble vascular endothelial growth factor receptor 2, CV = cardiovascular, HR = hazard ratio,
CI = conﬁdence interval, HF = heart failure.
Table S1. Baseline characteristics according to quartiles ofsVEGFR-2.
Table S2. Simple and stepwise multiple regression analyses
for the sVEGFR-2 level.
Table S3. Multiple Cox proportional hazard analysis for clinical outcomes.

References
1. Roger VL. Epidemiology of heart failure.
Circ Res. 2013; 113: 646–659.
2. Oka T, Akazawa H, Naito AT, Komuro I.
Angiogenesis and cardiac hypertrophy.
Circ Res. 2014; 114: 565–571.
3. Shiojima I, Sato K, Izumiya Y, Schiekofer
S, Ito M, Liao R, Colucci WS, Walsh K.
Disruption of coordinated cardiac hypertrophy and angiogenesis contributes tothe transition to heart failure. J Clin Invest. 2005; 115: 2108–2118.
4. Izumiya Y, Shiojima I, Sato K, Sawyer
DB, Colucci WS, Walsh K. Vascularendothelial growth factor blockade promotes the transition from compensatory
cardiac hypertrophy to failure in response to pressure overload. Hypertension. 2006; 47: 887–893.
5. Taimeh Z, Loughran J, Birks EJ, Bolli R.
Vascular endothelial growth factor inheart failure. Nat Rev Cardiol. 2013; 10:519–530.
6. Kivelä
R,
Hemanthakumar
KA,
Vaparanta K, Robciuc M, Izumiya Y,
Kidoya H, Takakura N, Peng X, Sawyer
DB, Elenius K, Walsh K, Alitalo K.
Endothelial cells regulate physiologicalcardiomyocyte growth via VEGFR2mediated paracrine signaling. Circulation. 2019; 139: 2570–2584.
7. Stevens M, Oltean S. Modulation of receptor tyrosine kinase activity throughalternative splicing of ligands and receptors in the VEGF-A/VEGFR Axis. Cells.
2019; 8: 288.
8. Abou-Fayçal C, Hatat AS, Gazzeri S,
Eymin B. Splice variants of the RTK family: their role in tumour progression andresponse to targeted therapy. Int J Mol
Sci 2017; 18: 383.
9. Ebos JM, Bocci G, Man S, Thorpe PE,
Hicklin DJ, Zhou D, Jia X, Kerbel RS. A
naturally occurring soluble form of vascular endothelial growth factor receptor
2 detected in mouse and human plasma.
Mol Cancer Res. 2004; 2: 315–326.
10. Sallinen H, Heikura T, Koponen J,
Kosma VM, Heinonen S, Ylä-Herttuala
S, Anttila M. Serum angiopoietin-2 andsoluble VEGFR-2 levels predict malignancy of ovarian neoplasm and poor
prognosis in epithelial ovarian cancer.
BMC Cancer. 2014; 14: 696.
11. Jantus-Lewintre E, Sanmartín E, Sirera
R, Blasco A, Sanchez JJ, Tarón M, Rosell
R, Camps C. Combined VEGF-A and
VEGFR-2 concentrations in plasma: diagnostic and prognostic implications inpatients with advanced NSCLC. Lung
Cancer. 2011; 74: 326–331.
12. Ebos JM, Lee CR, Bogdanovic E, Alami J,
Van Slyke P, Francia G, Xu P, Mutsaers
AJ, Dumont DJ, Kerbel RS. Vascular endothelial
growthfactor-mediateddecrease in plasma soluble vascular endothelial growth factor receptor-2 levels
as a surrogate biomarker for tumor
growth. Cancer Res. 2008; 68: 521–529.
13. Wada H, Ura S, Kitaoka S, SatohAsahara N, Horie T, Ono K, Takaya T,
Takanabe-Mori R, Akao M, Abe M,
Morimoto T, Murayama T, Yokode M,
Fujita M, Shimatsu A, Hasegawa K. Distinct characteristics of circulating vascular endothelial growth factor-a and C
levels in human subjects. PLoS One.
2011; 6: e29351.
14. Abhinand CS, Raju R, Soumya SJ, Arya
PS, Sudhakaran PR. VEGF-A/VEGFR2
signaling network in endothelial cells
relevant to angiogenesis. J Cell Commun
Signal 2016; 10: 347–354.
15. Albuquerque RJC, Hayashi T, Cho WG,
Kleinman ME, Dridi S, Takeda A, Bafﬁ
JZ, Yamada K, Kaneko H, Green MG,
Chappell J, Wilting J, Weich HA,
Yamagami S, Amano S, Mizuki N,
Alexander JS, Peterson ML, Brekken
RA, Hirashima M, Capoor S, Usui T,
Ambati BK, Ambati J. Alternatively
spliced vascular endothelial growth
factor receptor-2 is an essential endogenous inhibitor of lymphatic vessel
growth. Nat Med. 2009; 15: 1023–1030.
16. Pavlakovic H, Becker J, Albuquerque R,
Wilting J, Ambati J. Soluble VEGFR-2:

ESC Heart Failure 2021; 8: 4187–4198
DOI: 10.1002/ehf2.13555

4198
an antilymphangiogenic variant of VEGF
receptors. Ann N Y Acad Sci. 2010; 1207:
E7–E15.
17. Kliche S, Waltenberger J. VEGF receptor
signaling and endothelial function.
IUBMB life. 2001; 52: 61–66.
18. Alem MM. Endothelial dysfunction inchronic heart failure: assessment, ﬁndings, signiﬁcance, and potential therapeutic targets. Int J Mol Sci 2019; 20:3198.
19. Lim SL, Lam CS, Segers VF, Brutsaert
DL, De Keulenaer GW. Cardiac
endothelium-myocyte interaction: clinical opportunities for new heart failure
therapies regardless of ejection fraction.
Eur Heart J. 2015; 36: 2050–2060.
20. Marti
CN,
Gheorghiade
M,
Kalogeropoulos AP, Georgiopoulou VV,
Quyyumi AA, Butler J. Endothelial dysfunction, arterial stiffness, and heart
failure. J Am Coll Cardiol. 2012; 60:
1455–1469.
21. Redﬁeld MM, Jacobsen SJ, Borlaug BA,
Rodeheffer RJ, Kass DA. Age- andgender-related ventricular-vascular stiffening: a community-based study. Circulation. 2005; 112: 2254–2262.
22. Mohammed SF, Hussain S, Mirzoyev SA,
Edwards WD, Maleszewski JJ, Redﬁeld
MM. Coronary microvascular rarefaction and myocardial ﬁbrosis in heart

M. Iguchi et al.
failure with preserved ejection fraction.
Circulation. 2015; 131: 550–559.
23. Kishimoto S, Kajikawa M, Maruhashi T,
Iwamoto Y, Matsumoto T, Iwamoto A,
Oda N, Matsui S, Hidaka T, Kihara Y,
Chayama K, Goto C, Aibara Y,
Nakashima A, Noma K, Higashi Y. Endothelial dysfunction and abnormal vascular structure are simultaneously present
in patients with heart failure with preserved ejection fraction. Int J Cardiol.
2017; 231: 181–187.
24. Lyle MA, Brozovich FV. HFpEF, a diseaseof the vasculature: a closer look at theother half. Mayo Clin Proc. 2018; 93:
1305–1314.
25. Cuijpers I, Simmonds SJ, van Bilsen M,
Czarnowska E, González Miqueo A,
Heymans S, Kuhn AR, Mulder P,
Ratajska A, Jones EAV, Brakenhielm E.
Microvascular and lymphatic dysfunction in HFpEF and its associated comorbidities. Basic Res Cardiol. 2020; 115:39.
26. Crisostomo PR, Wang M, Herring CM,
Markel TA, Meldrum KK, Lillemoe KD,
Meldrum DR. Gender differences in injury induced mesenchymal stem cell apoptosis and VEGF, TNF, IL-6 expression:role of the 55 kDa TNF receptor(TNFR1). J Mol Cell Cardiol. 2007; 42:
142–149.

27. Malamitsi-Puchner A, Tziotis J, Tsonou
A, Protonotariou E, Sarandakou A,
Creatsas G. Changes in serum levels ofvascular endothelial growth factor inmales and females throughout life. J
Soc Gynecol Investig. 2000; 7: 309–312.
28. Richards M, Troughton RW. NT-proBNP
in heart failure: therapy decisions andmonitoring. Eur J Heart Fail. 2004; 6:
351–354.
29. Carmeliet P. VEGF as a key mediator ofangiogenesis in cancer. Oncology. 2005;
69: 4–10.
30. Dobbin SJH, Cameron AC, Petrie MC,
Jones RJ, Touyz RM, Lang NN. Toxicity
of cancer therapy: what the cardiologist
needs to know about angiogenesis inhibitors. Heart. 2018; 104: 1995–2002.
31. Wada H, Suzuki M, Matsuda M, Ajiro Y,
Shinozaki T, Sakagami S, Yonezawa K,
Shimizu M, Funada J, Takenaka T,
Morita Y, Nakamura T, Fujimoto K,
Matsubara H, Kato T, Unoki T, Takagi D,
Ura S, Wada K, Iguchi M, Masunaga N,
Ishii M, Yamakage H, Shimatsu A, Kotani
K, Satoh-Asahara N, Abe M, Akao M,
Hasegawa K. VEGF-C and mortality inpatients with suspected or known coronary artery disease. J Am Heart Assoc
2018; 7: e010355.

ESC Heart Failure 2021; 8: 4187–4198
DOI: 10.1002/ehf2.13555

Japanese Journal of Clinical Oncology, 2021, 51(10)1523–1533
https://doi.org/10.1093/jjco/hyab104
Advance Access Publication Date: 13 July 2021
Original Article

Original Article

Cancer-related FGFR2 overexpression and gene
ampliﬁcation in Japanese patients with gastriccancerKeiko Minashi 1 , Takeshi Yamada2 , Hisashi Hosaka3 , Kenji Amagai4 ,Yoshiaki Shimizu5 , Hirokazu Kiyozaki6 , Mikio Sato7 , Atsuko Soeda8 ,Shinji Endo9 , Hiroyasu Ishida10 , Toshiro Kamoshida11 ,Yoshinori Sakai12 , and Kohei Shitara13
1
Clinical Trial Promotion Department, Chiba Cancer Center, Chiba, Japan, 2 Department of Gastroenterology,
University of Tsukuba Hospital, Tsukuba, Japan, 3 Department of Gastroenterology, Gunma Prefectural Cancer
Center, Ota, Japan, 4 Department of Gastroenterology, Ibaraki Prefectural Central Hospital, Ibaraki Cancer Center,
Kasama, Japan, 5 Department of Surgery, Narita Red Cross Hospital, Narita, Japan, 6 Department of Surgery,
Saitama Medical Center, Jichi Medical University, Saitama, Japan, 7 Department of Gastroenterology, Ryugasaki
Saiseikai Hospital, Ryuugasaki, Japan, 8 Department of Gastroenterology, Tsukuba Memorial Hospital, Tsukuba,
9
Department of Gastroenterology and Hepatology, Shinmatsudo Central General Hospital, Matsudo, Japan,
10
Department of Gastroenterology, National Health Organization, Mito Medical Center, Ibaraki, Japan, 11 Department
of Gastroenterology, Hitachi, Ltd., Hitachi General Hospital, Hitachi, Japan, 12 Department of Gastroenterology,
Tsuchiura Kyodo General Hospital, Tsuchiura, Japan and 13 Department of Gastrointestinal Oncology, National
Cancer Center Hospital East, Kashiwa, Japan*For reprints and all correspondence: Keiko Minashi, Clinical Trial Promotion Department, Chiba Cancer Center, 666-2
Nitona-cho Chuo-ku Chiba, 260-8717 Japan. E-mail: kminashi@chiba-cc.jp
Received 1 February 2021; Editorial Decision 10 June 2021; Accepted 18 June 2021

Abstract
Objective: Fibroblast growth factor receptor 2 (FGFR2) has been proposed as a novel druggabletarget in unresectable gastric cancer. FGFR2 alteration has been reported as associated withpoor prognosis even in patients with gastric cancer who received systemic chemotherapy. This
study aimed to evaluate the frequency of FGFR2 overexpression and gene ampliﬁcation in clinicalspecimens from Japanese patients with recurrent or unresectable gastric cancer.
Methods: This observational study enrolled patients who were histologically or cytologicallyconﬁrmed with unresectable HER2-negative or unknown gastric or gastroesophageal junctional
adenocarcinoma treated with at least one previous chemotherapy. FGFR2 overexpression and gene
ampliﬁcation in the specimens were evaluated by immunohistochemical staining and ﬂuorescencein situ hybridization methods, respectively.
Results: In a total of 173 eligible cases, FGFR2 immunohistochemistry score was evaluated as
0, 1, 2, 3 and 4 for 20, 80, 35, 28 and 10 cases, respectively. In 151 evaluable cases with FGFR2
immunohistochemistry scores of 1–4, FGFR2 copy number expressed as ﬂuorescence in situ
hybridization signals were detected as <4, ≥4 < 10 and ≥10 copies for 123, 16 and 12 cases,
respectively. FGFR2 copy number showed an increasing tendency along with higher FGFR2
immunohistochemistry scores in the corresponding specimen. The response rate and time totreatment failure for ﬁrst line chemotherapy did not have any obvious relationship to FGFR2
immunohistochemistry score and FGFR2 copy number.
© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium,provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

1523

1524

FGFR2 overexpression and gene ampliﬁcation in gastric cancer

Conclusions: Although FGFR2 overexpression and gene ampliﬁcation were shown in Japanesepatients with unresectable gastric cancer, these alterations did not impact the effects of cytotoxic
agents as ﬁrst line chemotherapy.
Key words: FGFR2 overexpression, FGFR2 gene ampliﬁcation, gastric cancer, immunohistochemical staining, ﬂuorescent in situ
hybridization

Introduction
Gastric cancer (GC) is the fifth most prevalent cancer and thethird leading cause of cancer-related death worldwide (1). Althoughsurgery is the treatment of choice for GC, prognosis with advanced
GC is still poor (2). It has been reported that 22–51% of GC
patients who received radical surgery with curative intent develop
recurrent disease (3,4). In patients with unresectable advanced orrecurrent lesions, systemic chemotherapy can prolong median survival time to 13–14 months (5,6). Trastuzumab in patients with
HER2-positive advanced GC, and an antiangiogenic agent (ramucirumab) and immune checkpoint inhibitors (nivolumab and pembrolizumab) introduced as later-line therapy in non-selective patientswith metastatic GC have demonstrated modest survival benefits(7–12). Despite improved outcomes with these targeted molecular therapies, however, prognosis with advanced GC still remains
wanting, and there is a critical need to develop more efficacious
therapeutic agents.
The fibroblast growth factor (FGF)/FGF receptor (FGFR)
signaling axis plays an important role in normal organ, vascularand skeletal development. On the other hand, activating FGFR
gene abnormalities are reported in various tumor types, in which
many of these FGFR abnormalities are considered a driving event(13–15). Genetic modifications or overexpression of FGFRs havebeen associated with tumorigenesis and disease progression inbreast, lung, gastric, hematologic and other malignancies. The cancer
types known to be connected to genetic abnormalities in FGFR
include breast cancer [FGFR1 and FGFR2 gene amplifications at anincidence of 10 and ∼1%, respectively; (16)], squamous cell lungcancer [FGFR1 gene amplifications at an incidence of 20%; (17)],
endometrial cancer [FGFR2 activating mutation at an incidence of12%; (16)], intrahepatic cholangiocarcinoma [FGFR2 gene fusionsat an incidence of 14%; (18)], bladder cancer [FGFR3 activating
mutation at an incidence of 50–60% for non-muscle invasive type;(16)], myeloma [FGFR3 translocation at an incidence of 15%; (16)]and glioma [FGFR3 gene fusions at an incidence of 8%; (19)]. It
has also been reported that FGFR2 gene amplification and FGFR2
overexpression is found in 1.8–15% (20) and 2.5–61.4% (21) of
GC, respectively, and is associated with poor prognosis (22,23).
In cases with diffuse type GC, up to a 10% incidence of FGFR2
gene amplification in those with relatively poor prognosis has beenreported (15). It has also been reported that FGFR2 and HER2
gene amplifications are mutually exclusive (24). Therefore, FGFR2
amplification has attracted significant interest as a therapeutic target
for FGFR2-amplified GC, and several development projects are
ongoing (25). In this context, clarifying the frequency of FGFR2
gene amplification and FGFR2 overexpression in GC may greatly
contribute to the development of FGFR2 inhibitors as a novel
therapeutic option. To illuminate the significance of developing
FGFR2 inhibitors for GC, we aimed in this study to find thefrequency of FGFR2 gene amplification and FGFR2 overexpression

in clinical specimens from HER2 negative/unknown Japanesepatients with recurrent or unresectable GC.

Patients and methods
Study design
This study was a multicenter observational study.

Study population
This study included patients who were diagnosed with unresectable
gastric or gastroesophageal junctional adenocarcinoma confirmedby histological or cytological methods. Patients who were diagnosed either to be seen as refractory for at least one systemic
chemotherapy or as recurrent during or within 6 months after
postoperative adjuvant chemotherapy/chemoradiation therapy were
eligible. The other criteria for eligibility were as follows: (i) negativeor unknown for HER2/neu status, (ii) age ≥ 20 years at written
informed consent before enrollment in this study and (iii) clinical
GC specimens at diagnosis or surgical resection were available.
Patients whom the investigator judged to be ineligible for this study
were excluded. It has been reported that FGFR2 and HER2 gene
amplifications are almost always mutually exclusive (24), so we
excluded HER2 positive patients to focus on FGFR2 amplification inthis study.
The World Medical Association Declaration of Helsinki on medical research protocols and ethics was followed throughout the study.
Authorization for the use of the clinical specimens for research
purposes was obtained from the institutional review board at eachstudy location.

Study data collection on chemotherapy
In this study, we collected (regimen, duration, efficacy, etc.) dataon only one regimen of chemotherapy received first after a diagnosis of unresectable or recurrent GC. Response rates and timeto treatment failure (TTF) for chemotherapy prior to enrollment
were calculated from case report data extracted from backgroundmedical records for each case with first line chemotherapy. Cases
with first line chemotherapy were defined as those who had: noncurative resection, received first line chemotherapy and had first line
chemotherapy data (on regimens, duration, efficacy, etc.); those who
received first line chemotherapy and had first line chemotherapy
data (on regimens, duration, efficacy, etc.); those who had curative
resection but did not receive adjuvant chemotherapy, had recurrence,
received first line chemotherapy and had first line chemotherapy data(on regimens, duration, efficacy, etc.) and; those who had curative
resection and recurrence 6 months after adjuvant chemotherapy,
received first line chemotherapy and had first line chemotherapy data(on regimens, duration, efficacy, etc.).

Jpn J Clin Oncol, 2021, Vol. 51, No. 10

1525

Figure 1. Representative immunohistochemical (IHC) images for the expression of ﬁbroblast growth factor receptor 2 (FGFR2) protein in the gastriccancer clinical specimens in this study. Images a, b, c, d and e show IHC scoreexpressions of 0, 1, 2, 3 and 4, respectively. See text for score deﬁnitions.
Magniﬁcation: ×20 objective.

FGFR2 immunohistochemistry
To evaluate FGFR2 protein expression, immunohistochemistry(IHC) staining was performed using rabbit anti-FGFR2 polyclonal
antibody (FGFR2 IHC kit, Nichirei Biosciences Inc., Tokyo, Japan)
with 4 μm sections from formalin-fixed and paraffin-embedded
tumor specimens. The staining intensity of each tumor cell andproportion of tumor cells with FGFR2 overexpression in each section
was scored by two independent observers as follows: Score 0, <10%of tumor cells expressed weakly with FGFR2 but none expressed
highly; Score 1, ≥10% of tumor cells expressed weakly with FGFR2
but none expressed highly; Score 2, <10% of tumor cells expressed
highly with FGFR2; Score 3, ≥10%—<50% of tumor cells expressed
highly with FGFR2 and Score 4, ≥50% of tumor cells expressed
highly with FGFR2 (Fig. 1). The percentage of positive FGFR2 cells
was calculated based on the positive area of the tumor cell region. The
strong expression ant weak expression was evaluated based on thestainability of the core with strong expression and weak expressionof CBA (cell block array) determined in the validation test.

FGFR2 ﬂuorescence in situ hybridization
To evaluate FGFR2 gene amplification, we used the fluorescence insitu hybridization (FISH) method with the 4 μm serial sections fromthe tumor specimens used for IHC examination. For this analysis, weused the tumor specimens with FGFR2 IHC scores of 1–4 because
it is known that a tumor specimen with a IHC score of 0 rarely
shows FGFR2 gene amplification (26). More specifically, a human
FGFR2 gene probe prepared from genomic sequences of bacterial
artificial chromosome clones RP11-7P17 and RP11-62L18 using
FGFR2 reverse and forward primer genes (Hokkaido System Science
Co., Ltd., Sapporo, Japan) was fluorescently labeled in orange bynick translation. A human centromere 10 (CEP 10) gene probe(Vysis CEP 10 SpectrumGreen Probe, Abbott Molecular Inc., Des
Plaines, USA) as reference, since the FGFR2 gene is localized onhuman chromosome 10, was fluorescently labeled in green. Afterhybridization, single sets of 20 tumor cells in each section were
evaluated for their average number of FGFR2 signals and CEP 10
signals per tumor cell by two independent observers. A ratio of
FGFR2 signals to CEP 10 signals (FGFR2/CEP10) was calculated
for each section. A representative FISH image of the FGFR2 signals
is shown in Fig. 2.

Figure 2. Representative FGFR2 ﬂuorescence in situ hybridization (FISH)
image in the clinical specimen of gastric cancer in this study. Each orange
ﬂuorescence image represented FGFR2 gene. (a) This ﬁgure showed 40
FGFR2 signals per tumor cell as well as clusters of FGFR2 signals (triangle
arrows show representative examples). (b) This ﬁgure showed 13 FGFR2
signals per tumor cell.

Statistical analysis
Statistical significance in the distribution of baseline characteristics according to the FGFR2 IHC score or FGFR2 copy number
expressed by FISH signals per tumor cell was analyzed by χ 2 -test
or Fisher’s exact test with P < 0.05 for the two-side significancelevel. In cases having data on TTF and best response with firstline chemotherapy prior to enrollment, Kaplan–Meier plots for the
TTF were drawn according to the FGFR2 IHC score or FGFR2
copy number, and significance between the plots was analyzed using
Logrank tests.

Results
Disposition and characteristics of cases
Among a total of 176 cases were enrolled maximally during theenrollment period from June 2018 to March 2020 (defined as thefull analysis set, FAS); 3 cases did not meet inclusion criteria andwere excluded, with the remaining 173 cases being defined as the perprotocol set (PPS). Within the PPS, 140 cases having data with which
to calculate TTF for a first line chemotherapy regimen just prior toenrollment were defined as the first line chemotherapy set (FLCS)(Table 1 and Fig. 3).
The primary analysis set was the PPS, consisting of 132 (76.3%)
males and 41 (23.7%) females. Mean ± standard deviation for age
was 67.4 ± 10.1 years (range 34–83 years). In the PPS, 92 cases(53.2%) had a primary tumor lesion at the enrollment. Primarytumors were located in the upper stomach (41 cases, 23.7%), middle

1526

FGFR2 overexpression and gene ampliﬁcation in gastric cancer

Table 1. Structured analysis population proportions: FGFR2 IHC score
Analysis set

All enrolled patientsPPS
Patients excluded from PPSFLCS

FGFR2 by IHC

Total

Score 0Score 1

Score 2

Score 3

Score 421
20 (95.2%)
1 (4.8%)13 (61.9%)

82
80 (97.6%)
2 (2.4%)65 (79.3%)

35
35 (100.0%)
0 (0.0%)29 (82.9%)28
28 (100.0%)
0 (0.0%)24 (85.7%)

10
10 (100.0%)
0 (0.0%)9 (90.0%)176
173 (98.3%)
3 (1.7%)140 (79.5%)

Abbreviations: PPS, per protocol set; FLCS, first line chemotherapy set.

Figure 3. Patients ﬂow diagram. FAS, full analysis set; PPS, per protocol set; FLCS, ﬁrst line chemotherapy set; IHC, immunohistochemistry; ISH, in situ
hybridization; TTF, time to treatment failure. ∗ 1 One patient was excluded from PPS due to deviation of inclusion criteria, ‘after primary chemotherapy’. Twopatients were excluded from PPS due to deviation of inclusion criteria, ‘the patient obtained written informed consent form’. ∗ 2 Patients with IHC score 0, 1, 2, 3
or 4. ∗ 3 Twenty patients with IHC score 0 and 2 patients with IHC score 1, 2, 3 or 4 who have no ISH data due to specimen failure. ∗ 4 FLCS was composed withpatients who had: non-curative resection, received ﬁrst line chemotherapy and had ﬁrst line chemotherapy data (on regimens, duration, efﬁcacy, etc.); those who
received ﬁrst line chemotherapy and had ﬁrst line chemotherapy data (on regimens, duration, efﬁcacy, etc.); those who had curative resection but did not receive
adjuvant chemotherapy, had recurrence, received ﬁrst line chemotherapy and had ﬁrst line chemotherapy data (on regimens, duration, efﬁcacy, etc.) and; those
who had curative resection and recurrence 6 months after adjuvant chemotherapy, received ﬁrst line chemotherapy and had ﬁrst line chemotherapy data (onregimens, duration, efﬁcacy, etc.). ∗ 5 Thirty-three patients were excluded from FLCS for the following reasons. Two patients had no data for the duration of ﬁrst
line chemotherapy. Thirty-one patients had curative resection and recurrence during adjuvant chemotherapy or within 6 months after adjuvant chemotherapy,
received second line chemotherapy.

stomach (49 cases, 28.3%), lower stomach (62 cases, 35.8%), esophagogastric junction (16 cases, 9.2%) or other (5 cases, 2.9%). Tumor
specimens for 168 cases (97.1%) were from a primary lesion and theremaining 5 (2.9%) from a metastatic lesion. The most frequent histological types were: poorly differentiated adenocarcinoma (75 cases,
89.3%); signet ring cell carcinoma (7 cases, 8.3%) and mucinousJpn J Clin Oncol, 2021, Vol. 51, No. 10

1527

Table 2. Baseline characteristics of cases according to FGFR2 IHC score
FGFR2 by IHC
Category

P value∗

Score 0Score 1

Score 2

Score 3

Score 4

Total

20

80

35

28

10

173

65.2
11.7
34
66.5
80

67.6
9.6
42
69
83

66.0
12.9
35
70
83

69.3
6.2
49
69.5
79

69.4
9.1
47
71.5
80

67.4
10.1
34
69
83

7 (35.0%)
13 (65.0%)

26 (32.5%)
54 (67.5%)

12 (34.3%)
23 (65.7%)

5 (17.9%)23 (82.1%)

2 (20.0%)8 (80.0%)52 (30.1%)
121 (69.9%)

0.53237 (35.0%)
13 (65.0%)

64 (80.0%)
16 (20.0%)

27 (77.1%)
8 (22.9%)25 (89.3%)
3 (10.7%)

9 (90.0%)
1 (10.0%)132 (76.3%)
41 (23.7%)

0.00048 (40.0%)
12 (60.0%)42 (52.5%)
38 (47.5%)

18 (51.4%)
17 (48.6%)

16 (57.1%)
12 (42.9%)8 (80.0%)
2 (20.0%)

92 (53.2%)
81 (46.8%)

0.35285 (25.0%)

17 (21.3%)

12 (34.3%)

6 (21.4%)

1 (10.0%)41 (23.7%)

0.23828 (40.0%)
6 (30.0%)0 (0.0%)26 (32.5%)
26 (32.5%)
9 (11.3%)

7 (20.0%)
13 (37.1%)
3 (8.6%)7 (25.0%)
13 (46.4%)
2 (7.1%)1 (10.0%)
4 (40.0%)
2 (20.0%)49 (28.3%)
62 (35.8%)
16 (9.2%)

1 (5.0%)2 (2.5%)0 (0.0%)0 (0.0%)2 (20.0%)

5 (2.9%)13 (65.0%)
3 (15.0%)
4 (20.0%)39 (48.8%)
38 (47.5%)
2 (2.5%)16 (45.7%)
18 (51.4%)
1 (2.9%)11 (39.3%)
15 (53.6%)
2 (7.1%)5 (50.0%)
4 (40.0%)
1 (10.0%)

84 (48.6%)
78 (45.1%)
10 (5.8%)

0 (0.0%)1 (1.3%)0 (0.0%)0 (0.0%)0 (0.0%)1 (0.6%)

12 (92.3%)

35 (89.7%)

15 (93.8%)9 (81.8%)

4 (80.0%)75 (89.3%)

1 (7.1%)4 (10.3%)

0 (0.0%)2 (18.2%)

0 (0.0%)7 (8.3%)0 (0.0%)0 (0.0%)1 (6.7%)0 (0.0%)1 (20.0%)

2 (2.4%)0 (0.0%)3 (100.0%)

12 (31.6%)
24 (63.2%)

3 (16.7%)
14 (77.8%)

4 (26.7%)
10 (66.7%)

1 (25.0%)
3 (75.0%)

20 (25.6%)
54 (69.2%)

0 (0.0%)2 (5.3%)1 (5.6%)1 (6.7%)0 (0.0%)4 (5.1%)

99% CI∗∗NAge (years)
Mean
Std
Min
MedianMax
Age category (years)<65
65
GenderMale
FemalePrimary tumor (at registration)
Yes
No
Primary site
Upper stomach
Middle stomach
Lower stomach
Esophagogastricjunction
OthersMain tissue type
Diffuse type
Intestinal type
Unspecified
adenocarcinoma
OthersDiffuse type
Poorly differentiatedadenocarcinoma
Signet-ring cell
carcinoma
Mucinous
adenocarcinoma
Intestinal type
Well differentiatedModerately
differentiatedPapillaryadenocarcinoma[0.2272,
0.2492]

0.05670.18930.9331Analysis set: per protocol set.
∗ P value of Fisher’s exact test.
∗∗ In case of estimation by Monte Carlo Method, 99% confidence interval (CI) is also described together with the P value.

carcinoma (2 cases, 2.4%); well differentiated adenocarcinoma (20cases, 25.6%); moderately differentiated adenocarcinoma (54 cases,
69.2%) and papillary adenocarcinoma (4 cases, 5.1%; Table 2).
None had been reported as positive for HER2/neu.

FGFR2 IHC score
Of the 173 PPS cases, FGFR2 IHC score was evaluated as 0, 1, 2, 3
and 4 for 20 (11.6%), 80 (46.2%), 35 (20.2%), 28 (16.2%) and 10(5.8%) cases, respectively (Table 1). Looking at the distribution of
baseline characteristics in the PPS according to FGFR2 IHC score,
there were no significant differences in age, presence of primarytumor at registration or primary site of tumor and main tissue type,
except for gender composition by which the proportion of females
was higher than males at Score 0 and that of males was higher
than females at Scores 1–4 (P = 0.0004; Table 2). The distributionof gender composition in the FLCS similarly showed a significant

1528

FGFR2 overexpression and gene ampliﬁcation in gastric cancer

Table 3. Structured analysis population proportions: FGFR2 copy number
Analysis set

FGFR2 copy number (copies/cell)

All enrolled patientsPPS
Patients excluded from PPS
FLCS

Total

<4

≥4, <10≥10124
123 (99.2%)
1 (0.8%)101 (81.5%)

17
16 (94.1%)
1 (5.9%)15 (88.2%)

12
12 (100.0%)
0 (0.0%)10 (83.3%)

153
151 (98.7%)
2 (1.3%)126 (82.4%)

Table 4. Relationship between FGFR2 IHC score and FGFR2 signals
FGFR2 by IHC
Score 0Score 1

Score 2

Score 3

Score 428
(82.4%)
4 (11.8%)

21(75.0%)
5 (17.9%)

1 (10.0%)123 (81.5%)–

73(92.4%)
6 (7.6%)

1 (10.0%)

16 (10.6%)–

0 (0.0%)2 (5.9%)2 (7.1%)8 (80.0%)

12 (7.9%)

FGFR2 copy number (copies/cell)
<4
–
≥4,
<10
≥10

Total

Analysis set: per protocol set.

difference (P = 0.0036), whereas no significance was observed inother baseline FLCS characteristics according to FGFR2 IHC score(data not shown).

FGFR2 copy number
In the 151 cases of the PPS with FGFR2 IHC scores of 1–4, except
for 2 cases who had no FISH result due to specimen failure, FGFR2
copy numbers per tumor cell were detected as <4, ≥4 < 10 and ≥ 10
for 123 cases, 16 cases and 12 cases, respectively (Table 3). FGFR2
copy number was moderately correlated with FGFR2/CEP10 ratio(r = 0.41 and P < 0.0001). In these 151 cases, the proportions
that showed a ≥4 FGFR2 copy number per tumor cell according to
FGFR2 IHC scores of 1, 2, 3 and 4 were 6/79 (7.6%), 6/34 (17.7%),
7/28 (25.0%) and 9/10 (90.0%), respectively, and that showed a≥10 FGFR2 copy number per tumor cell were 0/79 (0.0%), 2/34
(5.9%), 2/28 (7.1%) and 8/10 (80.0%), demonstrating an increased
tendency for the proportion of cases with amplified FGFR2 copy
number per tumor cell along with FGFR2 IHC score (Table 4). In
addition, the mean ± standard deviation for FGFR2 copy number
per tumor cell according to FGFR2 IHC scores of 1, 2, 3 and 4
were 2.4 ± 0.6 (79 cases), 4.2 ± 6.1 (34 cases), 5.8 ± 11.9 (28cases) and 25.5 ± 15.6 (10 cases), respectively, demonstrating thatthe average number of FGFR2 copies increased along with FGFR2
IHC score and the average number of FGFR2 copies at IHC score 2
exceeded 4. Looking at the distribution of baseline characteristics in
the PPS according to FGFR2 copy number, there were no significant
differences in age, gender, presence of primary tumor at registrationor main tissue type except with primary site of tumor (P = 0.0387) inwhich the proportion of upper or middle stomach primary sites with
FGFR2 copy number category of ≥10 seemed lower than those of<10 categories. Although not significant (P = 0.0956), the proportionof diffuse type primary tumors with a FGFR2 copy number categoryof ≥10 seemed higher than those of <10 categories (Table 5).

Response to chemotherapy prior to enrollment
according to FGFR2 IHC score
In the FLCS, the proportion of cases with pyrimidine fluoride plus
a platinum anticancer agent as the first line chemotherapy regimen prior to enrollment was 116 cases (82.9%) and other agentsaccounted for 24 cases (17.1%). Response to chemotherapy regimen
prior to enrollment according to FGFR2 IHC score is summarizedin Table 6. Response rates for first line chemotherapy according to
FGFR2 IHC scores of 0, 1, 2, 3 and 4 were 15.4, 33.8, 34.5, 37.5
and 55.6%, respectively (P = 0.4142). In addition, median values for
TTF and Kaplan–Meier plots for TTF with first line chemotherapy(Table 6 and Fig. 4) revealed no statistical differences by FGFR2 IHC
score (P = 0.3456, Logrank test).

Response to chemotherapy prior to enrollment
according to FGFR2 copy number
Response to the chemotherapy regimens according to FGFR2 copy
number is summarized in Table 7. The response rate for first line
chemotherapy according to FGFR2 copy number categories of<4, ≥4 < 10 and ≥10 were 33.7, 60.0 and 30.0%, respectively(P = 0.1464). In addition, the TTF with first line chemotherapy
revealed no statistical difference by FGFR2 copy number (P = 0.4607,
Logrank test; Table 7 and Fig. 4).

Discussion
FGFR2 overexpression and FGFR2 gene amplification have been
identified as a novel oncogenic (15) and druggable target (27) in cancers including GC. In addition, FGFR2 overexpression and FGFR2
gene amplification have been reported as associated with poor prognosis and lower response to chemotherapy in GC (22,23). Furthermore, bemarituzumab, a novel FGFR2b inhibitor, plus chemotherapy demonstrated significant progression-free and overall survival
benefit compared with placebo plus chemotherapy in patients with

Jpn J Clin Oncol, 2021, Vol. 51, No. 10

1529

Figure 4. Kaplan–Meier plots of the TTF for ﬁrst line chemotherapy. The upper and lower panel represented Kaplan–Meier plots according to FGFR2
IHC score 0–4 (P = 0.3456, Logrank test) and FGFR2 copy number category of <4, ≥4 < 10 and ≥ 10 copies/cell (P = 0.4607, Logrank test),
respectively.

1530

FGFR2 overexpression and gene ampliﬁcation in gastric cancer

Table 5. Baseline characteristics of cases according to FGFR2 copy number
CategoryN
Age (years)
Mean
Std
Min
MedianMax
Age category (years)<65
65
GenderMale
FemalePrimary tumor (at registration)
Yes
No
Primary site
Upper stomach
Middle stomach
Lower stomach
Esophagogastric junction
OthersMain tissue type
Diffuse type
Intestinal type
Unspecified adenocarcinoma
OthersDiffuse type
Poorly differentiatedadenocarcinoma
Signet-ring cell carcinoma
Mucinous adenocarcinoma
Intestinal type
Well differentiatedModerately differentiatedPapillary adenocarcinoma
Specimen collection sites
Primary tumor
Liver
Lung
Abdominal lymph nodes
Peritoneal dissemination
OthersP value∗

FGFR2 copy number (copies/cell)<4≥4, <10

≥10

123

16

12

67.2
10.2
35
69
83

70.3
8.2
46
72.5
77

67.4
9.4
47
70
80

39 (31.7%)
84 (68.3%)

2 (12.5%)
14 (87.5%)

4 (33.3%)
8 (66.7%)

0.319499 (80.5%)
24 (19.5%)

14 (87.5%)
2 (12.5%)

10 (83.3%)
2 (16.7%)

0.920167 (54.5%)
56 (45.5%)

9 (56.3%)
7 (43.8%)

8 (66.7%)
4 (33.3%)

0.740427 (22.0%)
34 (27.6%)
48 (39.0%)
12 (9.8%)
2 (1.6%)6 (37.5%)
6 (37.5%)
3 (18.8%)
1 (6.3%)0 (0.0%)1 (8.3%)1 (8.3%)5 (41.7%)
3 (25.0%)
2 (16.7%)

0.038756 (45.5%)
63 (51.2%)
4 (3.3%)0 (0.0%)7 (43.8%)
7 (43.8%)
1 (6.3%)1 (6.3%)8 (66.7%)
3 (25.0%)
1 (8.3%)0 (0.0%)0.095649 (87.5%)

7 (100.0%)

7 (87.5%)

0.48396 (10.7%)
1 (1.8%)0 (0.0%)0 (0.0%)0 (0.0%)1 (12.5%)

13 (20.6%)
47 (74.6%)
3 (4.8%)4 (57.1%)
2 (28.6%)
1 (14.3%)

1 (33.3%)
2 (66.7%)
0 (0.0%)0.0736119 (96.7%)
0 (0.0%)0 (0.0%)1 (0.8%)2 (1.6%)
1 (0.8%)16 (100.0%)
0 (0.0%)0 (0.0%)0 (0.0%)0 (0.0%)0 (0.0%)12 (100.0%)
0 (0.0%)0 (0.0%)0 (0.0%)0 (0.0%)0 (0.0%)1

Analysis set: per protocol set.
∗ P value of Fisher’s exact test.

advanced GC (28). Thus, we aimed in this multicenter observational study to clarify the frequency of FGFR2 overexpression and
FGFR2 gene amplification using IHC and FISH methods as well
as reliable baseline factors in Japanese patients with recurrent or
unresectable GC.
In the present study, the proportion of the cases with FGFR2
overexpression as expressed by IHC scores of ≥1, ≥2, ≥3 or 4 wasrevealed to be 88.4, 42.2, 22.0 or 5.8%, respectively. It has beenreported in a meta-analysis of studies on FGFR2 overexpression that
GC patients have a wide range of FGFR2 overexpression frequencies

from 2.5 to 61.4% (21). The frequency of FGFR2 overexpression
found in the present study conducted in Japanese GC patients wasdemonstrated to be no less than in those studies.
It has been recognized that FGFR2 overexpression is often led
by FGFR gene amplification (15). There have been multiple reports
to-date that FGFR2 gene amplification is associated with FGFR2
overexpression in gastric cancer (21,29), and FGFR2 protein overexpression has been noted to strongly correlate with FGFR2 gene
amplification, according to a report by Ahn et al. (26). On the other
hand, Tuner et al. reported that FGFR2 overexpression was resultJpn J Clin Oncol, 2021, Vol. 51, No. 10

1531

Table 6. Response to chemotherapy prior to enrollment according to FGFR2 IHC score
Category

P value∗

FGFR2 by IHC
Score 0N
13Best overall response: first line chemotherapy
Complete response (CR)
0 (0.0%)Partial response (PR)
2 (15.4%)
Stable disease (SD)
8 (61.5%)
Non-CR/Non-PD
1 (7.7%)
Progressive disease (PD)
1 (7.7%)Not evaluable (NE)
1 (7.7%)
Response rate (CR + PR)
2 (15.4%)
95% Confidence interval (%)[4.3, 42.2]
Disease control rate
11 (84.6%)(CR + PR + SD + Non-CR/Non-PD)
95% Confidence interval (%)[57.8, 95.7]
Time to treatment failure (TTF): first line chemotherapy
N
13
Median TTF
176
95% Confidence interval (%)[64.0, 202.0]

Score 1

Score 2

Score 3

Score 4

65

29

24

9

1 (1.5%)21 (32.3%)
17 (26.2%)
13 (20.0%)
11 (16.9%)
2 (3.1%)22 (33.8%)[23.5, 46.0]
52 (80.0%)

0 (0.0%)10 (34.5%)
8 (27.6%)2 (6.9%)9 (31.0%)0 (0.0%)10 (34.5%)[19.9, 52.7]
20 (69.0%)

0 (0.0%)9 (37.5%)
6 (25.0%)
5 (20.8%)
4 (16.7%)
0 (0.0%)9 (37.5%)[21.2, 57.3]
20 (83.3%)

0 (0.0%)5 (55.6%)
2 (22.2%)
1 (11.1%)
1 (11.1%)
0 (0.0%)5 (55.6%)[26.7, 81.1]
8 (88.9%)[68.7, 87.9][50.8, 82.7][64.1, 93.3][56.5, 98.0]

65
29
211
157
[162.0, 289.0] [92.0, 218.0]

0.50740.4142
0.663

24
9
225
112
[157.0, 288.0] [50.0, 401.0]

Analysis set: FLCS (n = 140).
∗ P value of Fisher’s exact test.

of abnormal transcriptional upregulation of the FGFR2 gene (16).
We also evaluated FGFR2 gene amplification in this study. Because
the FGFR2 gene is known to localize on human chromosome 10, we
evaluated the number of FGFR2 copies per tumor cell on a basis of 4
copies/cell, or the equivalent of 2 times 2 copies/cell in normal cells,
and set 3 categories for FGFR2 copy number per tumor cell, i.e. <4,≥4 < 10 and ≥ 10 copies/cell. As a result, FGFR2 copy numbers of<4, ≥4 < 10 and ≥10 copies/cell were observed in 123, 16 and 12
cases out of 151 cases with an FGFR2 IHC score of ≥1, respectively.
In addition, although no statistically significant difference was noted,
the fact that an increasing tendency was observed in the proportionof cases who showed amplified FGFR2 copy number per tumor
cell along with their FGFR2 IHC score suggests a relationshipbetween IHC score (FGFR2 overexpression) and FGFR2 copy number expressed by FISH signals (FGFR2 gene amplification). Taking
these results into account, we consider it possible to estimate the
FGFR2 gene amplification with high reliability in clinically available
GC specimen screening samples using the IHC method, which is moreconvenient than the FISH method.
Although many questions on the role of FGFR2 overexpression
and FGFR2 gene amplification in the pathogenesis and progressionof GC have yet to be answered, it has been reported that a GC cell line
established from GC patient with FGFR2 gene amplification demonstrates significant inhibition of tumor cell growth and survival by
the induction of FGFR2 downregulation (30). Those results suggestthat tumor progression in GC patients with FGFR2 overexpression
and FGFR2 gene amplification may in large part be associated withthese FGFR abnormalities, and thus the establishment of optional
chemotherapies that target these molecular factors would be highly
desirable.
We also examined relationships between baseline characteristics
and response to first line chemotherapy prior to enrollment, with
FGFR2 IHC score and FGFR2 copy number, to investigate predictive
factors for FGFR2 overexpression and FGFR2 gene amplification.
For gender composition, the proportion of females with an FGFR2

IHC score of 0 was higher, whereas the proportion of males with
FGFR2 IHC scores of 1–4 was higher, and an imbalance was thus
observed. However, no difference was shown by way of FGFR2 copy
number. In addition, no gender effects on FGFR2 overexpression
have been observed in other studies of FGFR2 overexpression in the
primary tumors of GC patients by IHC (26,31). Our examinationof other baseline characteristics revealed no relationships between
FGFR2 IHC score and FGFR2 copy number, and was consistent
with other studies on FGFR2 overexpression (26,31) and FGFR2
gene amplification (32,33) involving GC patients. Furthermore, we
found no relationship to first line chemotherapy response in thisstudy. At this point, it is widely recognized that a high-level FGFR2
gene amplification and FGFR2 overexpression is associated withdecreased overall survival and lower response to chemotherapy(30,34). Because our present study was small-sized, limited to HER2
negative cases, did not control for background chemotherapy regimen and did not evaluate overall survival, there are still issues to beinvestigated by way of confirming the association of FGFR2 with the
response to chemotherapy.
Based on the above considerations, we believe it essential toclarify FGFR2 protein overexpression and/or FGFR2 gene amplification in GC patients to confirm altered FGFR2 expression, andto develop the potential molecular-targeting therapeutic agents with
FGFR2 inhibitors.

Conclusions
The present multicenter observational study took a detailed look at
the frequency of FGFR2 overexpression and FGFR2 gene amplification in Japanese patients with GC, and the effect of cytotoxic agentswere similar regardless of whether patients had FGFR overexpression
and gene amplification. These findings may contribute the development of promising therapeutic option for patients with recurrent or
unresectable GC.

1532

FGFR2 overexpression and gene ampliﬁcation in gastric cancer

Table 7. Response to chemotherapy prior to enrollment according to FGFR2 copy number
Category

P value∗

FGFR2 copy number (copies/cell)
<4

N
101
Best overall response: first line chemotherapy
Complete response (CR)
1 (1.0%)Partial response (PR)33 (32.7%)
Stable disease (SD)28 (27.7%)
Non-CR/Non-PD
16 (15.8%)
Progressive disease (PD)
21 (20.8%)
Not evaluable (NE)
2 (2.0%)
Response rate (CR + PR)
34 (33.7%)
95% Confidence interval (%)[25.2, 43.3]
Disease control rate
78 (77.2%)(CR + PR + SD + Non-CR/Non-PD)
95% Confidence interval (%)[68.1, 84.3]
Time to treatment failure (TTF): first line chemotherapy
N
101
Median TTF
198
95% Confidence interval (%)[157.0, 218.0]

4,<10

10

15

10

0 (0.0%)9 (60.0%)
2 (13.3%)
3 (20.0%)
1 (6.7%)0 (0.0%)9 (60.0%)[35.7, 80.2]
14 (93.3%)

0 (0.0%)3 (30.0%)
3 (30.0%)
2 (20.0%)
2 (20.0%)0 (0.0%)3 (30.0%)[10.8, 60.3]
8 (80.0%)[70.2, 98.8][49.0, 94.3]

15267[135.0, 413.0]

10
124.5
[50.0, 224.0]

0.70980.1464
0.417

Analysis set: FLCS with IHC score 1–4 (n = 126).
∗ P value of Fisher’s exact test.

Funding
This work was supported by Taiho Pharmaceutical Co., Ltd., Tokyo,
Japan.

Conﬂict of interest statement
Keiko Minashi received research grant from Astellas Pharma Inc.,
Daiichi Sankyo Co., Ltd., Mediscience Planning Inc., Merck Biopharma Co., Ltd., MSD K.K., Ono Pharmaceutical Co., Ltd., Taiho
Pharmaceutical Co., Ltd. Takeshi Yamada received lecture fee from
Daiichi Sankyo Co., Ltd., Eli Lilly Japan K.K., Merck Biopharma,
Novartis, Ono Pharmaceutical Co., Ltd., Taiho Pharmaceutical Co.,
Ltd., Yakult Honsha. Kohei Shitara reports paid consulting or advisory roles for AbbVie, Astellas Pharma Inc., Bristol-Myers Squibb
K.K., Eli Lilly Japan K.K., GSK, MSD K.K., Novartis, Ono Pharmaceutical Co., Ltd., Pfizer, Taiho Pharmaceutical Co., Ltd. and Takeda;
honoraria from AbbVie, Novartis and Yakult; and research funding
from Astellas Pharma Inc., Chugai Pharmaceutical Co., Ltd., Eli Lilly
Japan K.K., Daiichi Sankyo Co. Ltd., MSD K.K., Ono Pharmaceutical Co., Ltd., Sumitomo Dainippon Pharma, Taiho PharmaceuticalCo., Ltd. and Medi Science, outside the submitted work.

References
1.

2.

3.

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global
cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin
2018;68:394–424.
Smyth EC, Verheij M, Allum W, Cunningham D, Cervantes A, Arnold D.
Gastric cancer: ESMO clinical practice guidelines for diagnosis, treatment
and follow-up. Ann Oncol 2016;27:v38–49.
Cardoso R, Coburn NG, Seevaratnam R, et al. A systematic review ofpatient surveillance after curative gastrectomy for gastric cancer: a brief
review. Gastric Cancer 2012;15:S164–7.

4.

Spolverato G, Ejaz A, Kim Y, et al. Rates and patterns of recurrence
after curative intent resection for gastric cancer: a United States multiinstitutional analysis. J Am Coll Surg 2014;219:664–75.
5. Koizumi W, Narahara H, Hara T, et al. S-1 plus cisplatin versus S-1 alone
for first-line treatment of advanced gastric cancer (SPIRITS trial): a phase
III trial. Lancet Oncol 2008;9:215–21.
6. Yamada Y, Higuchi K, Nishikawa K, et al. Phase III study comparing
oxaliplatin plus S-1 with cisplatin plus S-1 in chemotherapy-naive patientswith advanced gastric cancer. Ann Oncol 2015;26:141–8.
7. Bang YJ, Van Cutsem E, Feyereislova A, et al. Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment
of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): a phase 3, open-label, randomised controlled trial. Lancet
2010;376:687–97.
8. Al-Batran SE, Van Cutsem E, Oh SC, et al. Quality-of-life and performance status results from the phase III RAINBOW study of ramucirumab
plus paclitaxel versus placebo plus paclitaxel in patients with previously
treated gastric or gastroesophageal junction adenocarcinoma. Ann Oncol
2016;27:673–9.
9. Shitara K, Bang YJ, Satoru Iwasa S, et al. Trastuzumab deruxtecan
in previously treated HER2-positive gastric cancer. N Engl J Med
2020;382:2419–30.
10. Kang YK, Boku N, Taroh Satoh T, et al. Nivolumab in patients withadvanced gastric or gastro-oesophageal junction cancer refractory to, or
intolerant of, at least two previous chemotherapy regimens (ONO-453812, ATTRACTION-2): a randomised, double-blind, placebo-controlled,phase 3 trial. Lancet 2017;390:2461–71.
11. Shitara K, Özgüroğlu M, Bang YJ, et al. Pembrolizumab versus paclitaxel
for previously treated, advanced gastric or gastro-oesophageal junction
cancer (KEYNOTE-061): a randomised, open-label, controlled, phase 3
trial. Lancet 2018;392:123–33.
12. Shitara K, Van Cutsem E, Bang YJ, et al. Efficacy and safety of pembrolizumab or pembrolizumab plus chemotherapy vs chemotherapy alone
for patients with first-line, advanced gastric cancer: the KEYNOTE-062
phase 3 randomized clinical trial. JAMA Oncol 2020;6:1571–80.
13. Eswarakumar VP, Lax I, Schlessinger J. Cellular signaling by fibroblast
growth factor receptors. Cytokine Growth Factor Rev 2005;16:139–49.

Jpn J Clin Oncol, 2021, Vol. 51, No. 10

14. Fukumoto S. Actions and mode of actions of FGF19 subfamily members.
Endocr J 2008;55:23–31.
15. Brooks AN, Kilgour E, Smith PD. Molecular pathways: fibroblast growth
factor signaling: a new therapeutic opportunity in cancer. Clin Cancer Res
2012;18:1855–62.
16. Turner N, Grose R. Fibroblast growth factor signaling: from development
to cancer. Nat Rev Cancer 2010;10:116–29.
17. Schildhaus HU, Nogova L, Wolf J, Buettner R. FGFR1 amplifications
in squamous cell carcinomas of the lung: diagnostic and therapeutic
implications. Transl Lung Cancer Res 2013;2:92–100.
18. Arai Y, Totoki Y, Hosoda F, Shirota T, Hama N, Nakamura H. Fibroblast
growth factor receptor 2 tyrosine kinase fusions define a unique molecularsubtype of cholangiocarcinoma. Hepatology 2014;59:1427–34.
19. Parker BC, Annala MJ, Cogdell DE, et al. The tumorigenic FGFR3-TACC3
gene fusion escapes miR-99a regulation in glioblastoma. J Clin Invest
2013;123:855–65.
20. Kim HS, Kim JH, Jang HJ. Pathologic and prognostic impacts of FGFR2
amplification in gastric cancer: a meta-analysis and systemic review.
J Cancer 2019;10:2560–7.
21. Kim HS, Kim JH, Jang HJ, Han B, Zang DY. Pathological and prognostic
impacts of FGFR2 overexpression in gastric cancer: a meta-analysis.
J Cancer 2019;10:20–7.
22. Hosoda K, Yamashita K, Ushiku H, et al. Prognostic relevance of FGFR2
expression in stage II/III gastric cancer with curative resection and S-1
chemotherapy. Oncol Lett 2018;15:1853–60.
23. Seo S, Park SJ, Ryu MH, et al. Prognostic impact of fibroblast growth
factor receptor 2 gene amplification in patients receiving fluoropyrimidine
and platinum chemotherapy for metastatic and locally advanced unresectable gastric cancers. Oncotarget 2017;8:33844–54.
24. Das K, Gunasegaran B, Tan IB, Deng N, Lim KH, Tan P. Mutually
exclusive FGFR2, HER2, and KRAS gene amplifications in gastric cancer
revealed by multicolour FISH. Cancer Lett 2014;353:167–75.

1533

25. Katoh M. FGFR inhibitors: effects on cancer cells, tumor microenvironment and whole-body homeostasis (Review). Int J Mol Med
2016;38:3–15.
26. Ahn S, Lee J, Hong M, et al. FGFR2 in gastric cancer: protein overexpression predicts gene amplification and high H-index predicts poor survival.
Mod Pathol 2016;29:1095–103.
27. Dai S, Zhou Z, Chen Z, Xu G, Chen Y. Fibroblast growth factor receptors(FGFRs): structures and small molecule inhibitors. Cell 2019;8:614.
28. Wainberg ZA, Enzinger PC, Kang YK, et al. Randomized double-blind
placebo-controlled phase 2 study of bemarituzumab combined with modified FOLFOX6 (mFOLFOX6) in first-line (1L) treatment of advanced
gastric/gastroesophageal junction adenocarcinoma (FIGHT). J Clin Oncol
2021;39:3_suppl.160.
29. Tokunaga R, Imamura Y, Nakamura K, et al. Fibroblast growth factor
receptor 2 expression, but not its genetic amplification, is associated withtumor growth and worse survival in esophagogastric junction adenocarcinoma. Oncotarget 2016;7:19748–61.
30. Xie L, Su X, Zhang L, et al. FGFR2 gene amplification in gastric cancer
predicts sensitivity to the selective FGFR inhibitor AZD4547. Clin Cancer
Res 2013;19:2572–83.
31. Hur JY, Chao J, Kim K, et al. High-level FGFR2 amplification is associatedwith poor prognosis and Lower response to chemotherapy in gastriccancers. Pathol Res Pract 2020;216:152878.
32. Su X, Zhan P, Gavine PR, et al. FGFR2 amplification has prognostic
significance in gastric cancer: results from a large international multicentre
study. Br J Cancer 2014;110:967–75.
33. Shoji H, Yamada Y, Okita N, et al. Amplification of FGFR2 gene inpatients with advanced gastric cancer receiving chemotherapy: prevalenceand prognostic significance. Anticancer Res 2015;35:5055–61.
34. Liu G, Xiong D, Xiao R, Huang Z. Prognostic role of fibroblast growth
factor receptor 2 in human solid tumors: a systematic review and metaanalysis. Tumour Biol 2017;39:1010428317707424.

Cerebral Microbleeds, Cerebrospinal
Fluid and Neuroimaging Markers inClinical Subtypes of Alzheimer’s Disease
MASAKI IKEDA1*, Hiroo Kasahara1, Sayaka Kodaira1, Kazuaki Nagashima1, Yukio Fujita1, Kouki
Makioka1, Eriko Takai1, Kimitioshi Hirayanagi1, Natsumi Furuta1, Etsuko Sanada1, Ayumi
Kobayashi1, Shun Nagamine1, Yasuo Harigaya2, Noriaki Hattori1, Tsuneo Yamazaki1, Yuichi
Tashiro3, Kazuhiro Kishi1, Hirotaka Shimada1, Takayuki Suto1, Yasujiro Sakai1, Yukiko Tanaka1,
Yuko Aihara1, Masakuni Amari1, Haruyasu Yamaguchi1, Koichi Okamoto1, Masamitsu
Takatama1, Kenji Ishii4, Tetsuya Higuchi1, Yoshito Tsushima1, Yoshio Ikeda1
1

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Gunma University, Japan, 2Maebashi Red Cross Hospital, Japan, 3Mito Medical Center, Japan, 4Tokyo
Metropolitan Institute of Gerontology, Japan
Submitted to Journal:
Frontiers in Neurology

In

Specialty Section:
Dementia and Neurodegenerative Diseases
Article type:
Original Research Article
Manuscript ID:
543866
Received on:
18 Mar 2020
Revised on:
24 Feb 2021
Journal website link:www.frontiersin.org

Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial
relationships that could be construed as a potential conflict of interest

Author contribution statement
MI, HK, NK, FY, MK, HK, FN, NS, HY, HN, TY, SY, YT, TY, AY, AM, YH, OK, TM and YI collected the clinical data, interpreted the data, andMI wrote the manuscript. ET analyzed genomic DNA from the patient’s blood samples and CSF biomarkers from the patient’s CSF. ES
and AK performed neuropsychological examinations. MI, SK, KI, TH and YT evaluated the neuroimaging information. KK, HS and TS
organized the neuroimaging systems. MI and YI performed the clinical data analysis and evaluated the specificities and neurologicalsignificances.

Keywords

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Alzheimer's disease, cerebral microbleeds, posterior cortical atrophy, CSF biomarkers, 99mTc ECD-SPECT, 11C PiB-PET, Logopenic
variant of primary progressive aphasia

Abstract

In

Word count:316

Lobar cerebral microbleeds (CMBs) in Alzheimer's disease (AD) are associated with cerebral amyloid angiopathy (CAA) due tovascular amyloid beta (Adeposits. However, the relationship between lobar CMBs and clinical subtypes of AD remains
unknown. Here, we enrolled patients with early- and late-onset amnestic dominant AD, logopenic variant of primary progressive
aphasia (lvPPA) and posterior cortical atrophy (PCA) who were compatible with the AD criteria. We then examined the levels of
cerebrospinal fluid (CSF) biomarkers (A 1‐42, A 1‐40, A 1‐38, phosphorylated tau 181 (P‐Tau), total tau (T‐Tau), neurofilament
light chain (NFL), and chitinase 3-like 1 protein (YKL-40)), analyzed the number and localization of CMBs, and measured the cerebral
blood flow (CBF) volume by 99mTc-ethyl cysteinate dimer single photon emission computerized tomography (99mTc ECD-SPECT), as
well as the mean cortical standard uptake value ratio by 11C-labeled Pittsburgh Compound B-positron emission tomography (11CPiB-PET). Lobar CMBs in lvPPA were distributed in the temporal, frontal, and parietal lobes with the left side predominance, while
the CBF volume in lvPPA significantly decreased in the left temporal area, where the number of lobar CMBs and the CBF volumes
showed a significant inversely correlation. The CSF levels of NFL in lvPPA were significantly higher compared to the other AD
subtypes and non-demented subjects. The numbers of lobar CMBs significantly increased the CSF levels of NFL in the total AD
patients, additionally, among AD subtypes, the CSF levels of NFL in lvPPA predominantly were higher by increasing number of lobar
CMBs. On the other hand, the CSF levels of A 1‐38, A 1‐40, A 1‐42, P‐Tau, and T‐Tau were lower by increasing number of lobar
CMBs in the total AD patients. These findings may suggest that aberrant brain hypoperfusion in lvPPA was derived from the brain
atrophy due to neurodegeneration, and possibly may involve the microcirculation aberration causing by lobar CMBs andcerebrovascular injuries, with the left side dominance, consequently leading to a clinical phenotype of logopenic variant.

Contribution to the field
Lobar CMBs in lvPPA were distributed in the temporal, frontal and parietal lobes with the left side predominance, while the CBF
volume in lvPPA significantly decreased in the left temporal area, where the number of lobar CMBs and the CBF volumes showed asignificant inversely correlation. Moreover, the CSF levels of NFL were significantly higher in lvPPA compared to the other AD
subtypes, and non-demented subjects. Increased numbers of lobar CMBs significantly increased the CSF levels of NFL, while
decreasing Abeta1-38. These findings may suggest the presence of aberrant metabolism in lvPPA derived from cerebrovascular
injuries due to CAA and neurodegeneration. Moreover, with higher lobar CMBs prevalence of lobar CMBs and lower CBF volume,with left side predominance, lvPPA may involve the axonal neurodegeneration associated with AD pathology.

Ethics statements
Studies involving animal subjects
Generated Statement: No animal studies are presented in this manuscript.

Studies involving human subjects
Generated Statement: The studies involving human participants were reviewed and approved by the Gunma University Ethical
Review Board for Medical Research Involving Human Subjects of Gunma University (Maebashi, Gunma, Japan), the Geriatrics
Research Institute and Hospital (Maebashi, Gunma, Japan) and Maebashi Red Cross Hospital (Maebashi, Gunma, Japan). . The
patients/participants provided their written informed consent to participate in this study.

Inclusion of identifiable human data
Generated Statement: No potentially identifiable human images or data is presented in this study.

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Data availability statement
Generated Statement: The authors acknowledge that the data presented in this study must be deposited and made publicly
available in an acceptable repository, prior to publication. Frontiers cannot accept a manuscript that does not adhere to our open
data policies.

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Microbleeds and Biomarkers in AD subtypes 1

Original Research

Cerebral Microbleeds, Cerebrospinal Fluid and Neuroimaging
Markers in Clinical Subtypes of Alzheimer’s Disease

Masaki Ikeda1,2,3*, Sayaka Kodaira4, Hiroo Kasahara1, Eriko Takai1, Kazuaki Nagashima1,
Yukio Fujita1, Kouki Makioka1, Kimitoshi Hirayanagi1, Natsumi Furuta1, Minori Furuta1,

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Etsuko Sanada1, Ayumi Kobayashi1, Yasuo Harigaya5, Shun Nagamine1, Noriaki Hattori6,
Yuichi Tashiro7, Kazuhiro Kishi8, Hirotaka Shimada8, Takayuki Suto8, Hisashi Tanaka9,In

Yasujiro Sakai2, Tsuneo Yamazaki10, Yukiko Tanaka11, Yuko Aihara12, Masakuni Amari2,
Haruyasu Yamaguchi2,13, Koichi Okamoto2, Masamitsu Takatama2, Kenji Ishii14, Tetsuya
Higuchi4, Yoshito Tsushima4 and Yoshio Ikeda1
1Departments of Neurology, Gunma University Graduate School of Medicine, Maebashi, Japan.

2Department of Neurology, Geriatrics Research Institute and Hospital, Maebashi, Japan.

3Faculty of Health & Medical Care, Division of Common Education (Neurology), Saitama

Medical University, Hidaka, Japan.
4Departments of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate

School of Medicine, Maebashi, Japan.
5Department of Neurology, Maebashi Red Cross Hospital, Maebashi, Japan.

Microbleeds and Biomarkers in AD subtypes 2

6Department of Neuropsychiatry, Jomo Hospital, Maebashi, Japan.

7Department of Neurology, Mito Medical Center, Mito, Japan.

8Department of Radiology, Gunma University Hospital, Maebashi, Japan.

9Department of Neuropsychiatry, Tanaka Hospital, Yoshioka, Japan.

10Department of Occupational therapy, Gunma University Graduate School of Health Sciences,

Maebashi, Japan.

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11Department of Geriatric Medicine, Uchida Hospital, Numata, Japan.

12Department of Neurology, Shinozuka Hospital, Fujioka, Japan.

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13Tokyo Center for Dementia Research and Practices, Suginami-ku, Tokyo, Japan.

14Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, Itabashi-ku, Tokyo,

Japan.

Running title: Microbleeds and Biomarkers in AD subtypes*Correspondence: Dr Masaki Ikeda. E-mail: ikedam8@gmail.com
Present position: Faculty of Health & Medical Care, Division of Common Education(Neurology), Saitama Medical University. 1397-1 Yamane, Hidaka, Saitama, 350-1241, Japan.
Key words:
Alzheimer’s disease, cerebral microbleeds, logopenic variant of primary progressive
aphasiaposterior cortical atrophy, CSF biomarkers, 99mTc ECD-SPECT, 11C PiB-PET

Microbleeds and Biomarkers in AD subtypes 3

ABSTRACT
Lobar cerebral microbleeds (CMBs) in Alzheimer's disease (AD) are associated with cerebral
amyloid angiopathy (CAA) due to vascular amyloid beta (A deposits. However, therelationship between lobar CMBs and clinical subtypes of AD remains unknown. Here, we
enrolled patients with early- and late-onset amnestic dominant AD, logopenic variant of primary
progressive aphasia (lvPPA) and posterior cortical atrophy (PCA) who were compatible with

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the AD criteria. We then examined the levels of cerebrospinal fluid (CSF) biomarkers (A1-42,
A1-40, A1-38, phosphorylated tau 181 (P-Tau), total tau (T-Tau), neurofilament light chain

In(NFL), and chitinase 3-like 1 protein (YKL-40)), analyzed the number and localization of
CMBs, and measured the cerebral blood flow (CBF) volume by 99mTc-ethyl cysteinate dimer
single photon emission computerized tomography (99mTc ECD-SPECT), as well as the meancortical standard uptake value ratio by 11C-labeled Pittsburgh Compound B-positron emission
tomography (11C PiB-PET). Lobar CMBs in lvPPA were distributed in the temporal, frontal,
and parietal lobes with the left side predominance, while the CBF volume in lvPPA significantly
decreased in the left temporal area, where the number of lobar CMBs and the CBF volumes
showed a significant inversely correlation. The CSF levels of NFL in lvPPA were significantly
higher compared to the other AD subtypes and non-demented subjects. The numbers of lobar
CMBs significantly increased the CSF levels of NFL in the total AD patients, additionally,

Microbleeds and Biomarkers in AD subtypes 4

among AD subtypes, the CSF levels of NFL in lvPPA predominantly were higher by increasing
number of lobar CMBs. On the other hand, the CSF levels of A1-38, A1-40, A1-42, P-Tau,and T-Tau were lower by increasing number of lobar CMBs in the total AD patients. Thesefindings may suggest that aberrant brain hypoperfusion in lvPPA was derived from the brain
atrophy due to neurodegeneration, and possibly may involve the microcirculation aberration
causing by lobar CMBs and cerebrovascular injuries, with the left side dominance,

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consequently leading to a clinical phenotype of logopenic variant.

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1 INTRODUCTION

Cerebral amyloid angiopathy (CAA) is caused by amyloid beta (A accumulation and ischaracterized by several pathological changes in the walls of small cortical and leptomeningeal
capillaries, arterioles, and arteries (1–4). Lobar cerebral microbleeds (CMBs) are aneuroimaging marker of CAA, thought to reflect leakage of blood products and hemosiderin
deposits from cerebral vessels damaged by A deposition in Alzheimer’s disease (AD) (5–7).
The clinical presentations of atypical AD consist not only of amnestic symptoms, but also of
language/speech disturbances and visuospatial cognitive deficits. Logopenic variant of primary
progressive aphasia (lvPPA) (8–10) has been subsequently confirmed by the pathologicalfindings of AD (11), and posterior cortical atrophy (PCA) (12, 13) has been reported to beMicrobleeds and Biomarkers in AD subtypes 5

mainly based upon AD pathology (14). The three current global criteria characterizing AD
include the two atypical presentations of lvPPA and PCA with memory loss and progressive
cognitive decline (15–17). With regards to lvPPA and PCA, several reports have revealed theimportant evidences from neuroimaging (18–20) and cerebrospinal fluid (CSF) AD biomarkers(21–24). However, investigations of the four subtypes of AD (early onset AD (EOAD), late
onset AD (LOAD), lvPPA, and PCA) have been scarcely reported in relation to CSF biomarkers

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and neuroimaging with lobar CMBs. While the current well-established CSF biomarkers for
AD diagnosis include A1-42, phosphorylated tau 181 (P-Tau) and total tau (T-Tau) (15–17),

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non-A or non-tau CSF biomarker, neurofilament light chain (NFL) and chitinase 3-like 1
protein (YKL-40) have also became a point of focus as alternative biomarkers for AD (25–27).
Quite recently, CSF levels of A1-38 was reported to be lower in the CAA patients than the
AD patients and the controls; while CSF NFL of CAA and AD patients was higher than control
cases (28), although they were not atypical AD phenotypes. From these findings, we postulated
that differences might exist in the CSF levels of NFL, YKL-40, and A1-38, the number anddistribution of cortical areas, the lateral predominance (left-right difference) of lobar CMBs
localization, as well as the regional CBF (cerebral blood flow) volume and 11C PiB retention
may be apparent between the four subtypes of AD. Accordingly, we formulated the following
hypotheses: (1) typical amnestic AD and atypical AD exhibit unique clinical characteristics,

Microbleeds and Biomarkers in AD subtypes 6

CSF biomarkers, and frequency of apolipoprotein E gene (APOE) 4 allele carriers; (2) the
smaller soluble A molecule, A1-38, and the alternative biomarkers, NFL and YKL-40, could
serve as CSF biomarkers of the AD subtypes; (3) the number of lobar CMBs at the cerebral
areas, as well as the laterality predominance differs among the four AD subtypes; (4) regional
CBF volume differs across the four AD subtypes and is correlated with the number of CMBs
at the cerebral areas. To test these hypotheses and explore the heterogeneity of clinical AD, we

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sought to investigate the relationships between CMBs, CSF markers, CBF volumes, and 11C
PiB retention among the four AD subtypes.

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2 MATERIALS AND METHODS
2.1 Participants

The spouse or family members of each AD patient provided written informed consent for thepatient to participate in this study. The subjects who underwent lumbar punctures were recruited
at Gunma University Graduate School of Medicine, the Geriatrics Research Institute andMaebashi Red Cross Hospital. Upon entering the study, subjects underwent a standardized
clinical assessment, including medical history, physical and neurological examinations,
neuropsychological examinations of Mini-Mental State Examination (MMSE) (29), Montreal
Cognitive Assessment (MoCA) (30), Frontal assessment battery (FAB) (31), and brain MRI

Microbleeds and Biomarkers in AD subtypes 7

scanning. A diagnosis of AD was reached in patients with a score equal to, or below 23 points
on the MMSE (32), combined with information from caregivers on the patients’ daily activities.
The diagnosis of AD was also based upon the diagnostic criteria of the National Institute of
Neurological and Communicative Diseases and Stroke-Alzheimer’s Disease and Related
Disorders Association (NINCDS-ADRDA) (33), in addition to adapted NIA/AA criteria andDSM-5 (15–17). Subjects were classified as non-demented (ND) if they scored more than 24

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points on the MMSE, and/or if, based upon information on activities of daily living (ADL)
provided by the family, they were considered to have a normal daily life that did not requireIn

any cognitive assistance. We classified AD patients into the following four clinical AD
subtypes: (1) early-onset amnestic dominant deficit Alzheimer’s disease (EOAD), in which theage at onset (AAO) of the memory disturbances is lower than 64 years old, (2) late-onset
amnestic dominant deficit Alzheimer’s disease (LOAD), in which AAO is 65 years or older, (3)logopenic variant type of primary progressive aphasia (lvPPA) (9), and (4) posterior cortical
atrophy (PCA) (12, 13), in which the subjects initially suffer from visual agnosia and/or
visuospatial cognitive deficits followed by memory loss, consistent with current diagnostic
criteria for AD (15–17). All AD patients who participated in this study had CSF biomarkers
and/or 11C PiB-PET findings consistent with an underlying AD pathology. Exclusion criteria
included patients with dementia who were clinically diagnosed with corticobasal syndrome

Microbleeds and Biomarkers in AD subtypes 8(CBS), progressive supranuclear palsy (PSP), dementia with Lewy bodies (DLB),
frontotemporal dementia (FTD), vascular dementia (VaD), cerebral amyloid angiopathy-related
inflammation (CAA-RI), or other neurodegenerative diseases characterized by dementia. No
patient who participated in this study had autopsy performed.

2.2 Assessments of language/speech dysfunctions and visual agnosia/visuospatial cognitive
deficits

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Speech function was assessed using the Standard Language Test of Aphasia (SLTA) (10, 34), aInbattery of tests originally developed to assess multi-domain language function, including‘‘Confrontation naming’’, ‘‘Word repetition’’, ‘‘Sentence repetition’’, ‘‘Auditory single-wordcomprehension’’, and ‘‘Auditory complex sentence comprehension commands’’. A proportionof patients were assessed using the WAB (Western Aphasia Battery: Kertesz A. J Speech Hear
Disord. 1986). Visual cognitive functions were assessed using the Benton Visual Retention Test
(BVRT) and/or VPTA (Visual Perception Test for Agnosia, edited by Japan Society for Higher
Dysfunction: Brain Function Test committee, Tokyo, 1997). These assessments were carriedout for the differential diagnosis of visual agnosia/visuospatial cognitive deficits. We examined
AD patients who primarily presented with language/speech deficits and were diagnosed withlogopenic variant type of primary progressive aphasia (lvPPA) according to the Consensus

Microbleeds and Biomarkers in AD subtypes 9

Classification of the three clinical variants of PPA (9). We also examined AD patients who
primarily presented with visual agnosia/visuospatial cognitive deficits dominant AD as
posterior cortical atrophy (PCA) (12, 13).

2.3 Analyses of CSF levels of A1-42, A1-40, A1-38, P-Tau, T-Tau, NFL, and YKL-40
Cerebrospinal fluid (CSF) was obtained by a lumbar puncture of the L3/L4 or L4/L5

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intervertebral space, and the CSF samples were centrifuged for 10 min at 1,800 × g at 4 ℃
within 3 h of collection. Samples were divided into aliquots of 0.5 mL in polypropylene tubes

Inand stored at -80 ℃ until analysis with ELISA kits for human CSF A1-42 and A1-40(FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan) (10, 35) and for CSF A1-38(IBL, Fujioka, Gunma, Japan) (10, 35). Inter-assay CVs (coefficients of variation) of the CSF
A1-42, A1-40, and A1-38 were less than 20 %, respectively (supplementary Table 1). All
samples were measured by a single operator using the same reagents. Measurement ofphosphorylated Tau (P-Tau) in CSF was performed using a sandwich ELISA INNOTEST Ⓡ
PHOSPHO-TAU (181P) (FUJIREBIO, Ghent, Belgium) as previously described elsewhere (10,35). Human total tau (T-tau) was measured using a sandwich ELISA INNOTESTⓇ T-Tau-Ag(FUJIREBIO, Ghent, Belgium) (36). Measurement of NFL (neurofilament light chain), a CSF
marker of neurodegeneration and large fiber axonal degeneration, was performed using theMicrobleeds and Biomarkers in AD subtypes 10

sandwich ELISA NF-lightⓇ (IBL International, Hamburg, Germany) (37, 38). CSF levels of
YKL-40 (chitinase 3-like 1 protein), a CSF marker of glial neuroinflammation, was measured
by the MicroVue™ YKL-40 EIA kits (Quidel, San Diego, CA, USA) (38–40). Inter-assay CVsof P-Tau, T-Tau, NFL, and YKL-40 were less than 20 %, respectively (supplementary Table
1). All samples were measured by a single operator using the same reagents.

2.4 Analyses of APOE allele

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After obtaining informed consent for genetic testing of the apolipoprotein E gene (APOE) allele,In

we purified genomic DNA from lymphocytes in the peripheral blood of affected subjects. For
the analysis of APOE allele polymorphism, purified genomic DNA samples were examined aspreviously described (10, 35).

2.5 Analyses of neuroimaging markers
2.5.1 MRIAll participants underwent a brain MRI (T2WI, T1WI, FLAIR, T2*WI (2-dimensional gradientrecalled echo)) carried out on three different MRI scanners (Siemens 3.0T, Siemens 1.5T, andGeneral Electric 1.5T) (supplementary Table 2, 3). CMBs were defined as homogenous,
round areas with a signal void (of a diameter smaller than 10 mm) detected by T2*WI (7).

Microbleeds and Biomarkers in AD subtypes 11

Lobar CMBs were defined as microbleeds restricted primarily to cortical areas of frontal,
temporal, parietal, and occipital lobes bilaterally. We quantified the number of CMBs in deep
white matter (DWM) and cerebellum, rated by MRI (axial T2*WI) according to the anatomicalrating scale (MARS) (41). The number of CMBs on MRI T2*WI was determined independently,
and a random order, by the first rater (M.I.) who was an experienced neurologist and the second
rater (H.K.) who was an excellent neuroradiologist, both of whom were blinded to the clinical

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diagnosis of the patients. In cases of disagreement, the number of CMBs were ascertained byconsensus. The number of CMBs were used to estimated inter-rater reliability by weighted

Inkappa coefficient (0.827) carried out by statistical analyses in SPSS 24.0 (SPSS Inc. Chicago,
IL, USA). All neuro-radiological analyses were conducted by PACS Imaging Workstation(Sectra AB, Stockholm, Sweden).

2.5.2 99mTc ECD-SPECT studies
AD patients underwent 99mTc-ethyl cysteinate dimer single photon emission computerized
99m

tomography (Tc ECD-SPECT) (FUJIFILM RI Toyama Chemical Co., Ltd., Chuo-ku, Tokyo,

Japan) imaging as previously described (42). The degree of uptake of99m

Tc ECD-SPECT and

its AD diagnostic abilities are in excellent concordance with those of 18F FDG-positronemission tomography (PET) (43). We assessed blood perfusion of CBF volumes in the brains

Microbleeds and Biomarkers in AD subtypes 12of patients with the four subtypes of AD by 99mTc ECD-SPECT bilaterally in five regions(frontal, temporal, parietal, occipital lobes, and cerebellum) according to previously publishedmethodology (42, 43), however, we did not perform partial volume correction in this study.
Each AD patient was placed in a supine position on the scanning bed with eyes closed during
injection and during the sequent scanning period with a quiet examination dose of 600 MBq.

2.5.3

11C PiB-PET studies

11

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C PiB (2-(4-aminophenyl)-6-hydroxybenzothiazole) was synthesized for 11C PiB-PET (44),In

and 18F-labeled fluorodeoxyglucose was also synthesized for PET (FDG-PET) in Gunma
University hospital cyclotron according to previous reported methods (10, 44–48). We used a
Discovery ST Elite scanner (General Electric Medical Systems, Milwaukee, WI, USA) for all
PET studies. After an intravenous injection of 11C-PiB (550 MBq), emission scans wereacquired three-dimensionally without arterial sampling. Images were loaded on Xeleris
workstation (General Electric Medical Systems, Milwaukee, WI), where 11C PiB-PET images
were co-registered with the respective 18F FDG-PET images (10, 48). The 11C PiB-PET images
were rated as “positive” by visual inspection when the uptake level in the cerebral cortex was
more prominent than those in the white matter (10, 47, 48). The standardized uptake value ratio(SUVR) represents a quantitative measure of tracer uptake, which is normalized to the mean

Microbleeds and Biomarkers in AD subtypes 13

uptake in a reference region as well as published protocols (44–47) and our previous methods(48). The cerebellar cortex was selected as a reference region to evaluate the mean cortical
SUVR (mcSUVR) of 11C PiB-PET as 11C PiB uptake in the cerebellar cortices does not differ
between AD patients and healthy controls (44–47). Thus, since the cerebellar cortices are
expected to have a lower fibrillary A plaque burden than the cerebral cortices, the cerebellar
cortex was used as a reference region to evaluate mcSUVR (46–47). Regions in the frontal

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cortical region (FRC), parietal cortical region (PAR), anterior cingulate region (ANC), posterior
cingulate region (PCG), lateral temporal lobe cortical region (LTC), medial temporal lobeIn

cortical region (MTC), and occipital cortical region (OCC) were selected to calculate themcSUVR of the respective areas (44–48). Circular standard regions of interest (ROI) of 1 cm
in diameter were placed on each cortical region of each 11C PiB-PET images onto each corticalregion of the 11C PiB-PET image using the co-registered FDG-PET image (48). However, we
did not perform partial volume correction. A standardized single ROI was placed over three
regions of the FRC, three of the PAR, one of the ANC, one of the PCG, three of the LTC, oneof the MTC, and one of the OCC in the ipsilateral side (48). The levels of regional 11C PiB
accumulation were summarized and the mcSUVR was calculated. Additionally, the mcSUVR
at a total of 26 areas was used to calculate a global SUVR in each subject (48). Mean cortical

Microbleeds and Biomarkers in AD subtypes 14

SUVR values were calculated in 31 participants (lvPPA: 4, PCA: 4, EOAD: 10, LOAD: 13)
who underwent 11C PiB-PET scans, in all 14 areas as described above.

2.6 Statistical analyses
Comparison analyses for demographic data (AAO, duration of illness, MMSE, MoCA, FAB,
education years, hypertension, diabetes, hypercholesterolemia, and APOE 4 allele carrier)

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were performed between the two clinical AD subgroups (the typical AD group and the atypicalAD group) using either Mann-Whitney tests for continuous variables, or a chi-squared orIn

Fisher’s test for categorial variables. The chi-squared test applied for categorical variables, wasalso used to evaluate the association between the number of microbleeds between the two AD
subtypes. Statistical comparisons of CSF A-38, A-, A-, P-Tau, T-Tau, NFL, andYKL-40 across the two AD subgroups and the ND group were performed using a one-way
analysis of variance (ANOVA) (p < 0.05), Tuckey’s test was used for post hoc comparison. If
the non-normal distribution was identified for non-parametrical comparison using MannWhitney tests (defined as p < 0.05), Dunn’s test was used for post hoc comparison.
A Kruskal-Wallis test, as well as one-way ANOVA and Dunn’s test, used for post hoccomparison and correction for multiple comparisons, were applied to the following analyses:
statistical comparisons of the CSF biomarkers across the four AD subgroups, comparison ofMicrobleeds and Biomarkers in AD subtypes 15

CBF volumes by 99mECD-SPECT among the frontal, temporal, parietal, and occipital lobe
cortices and cerebellum, and comparisons of 11C PiB retention among the frontal, temporal,
parietal, and occipital lobe cortices. The correlation analysis among levels of the CBF volumes
and the number of lobar CMBs was performed using Spearman’s rank correlation tests at thefour areas (frontal, temporal, parietal, and occipital) in the four AD subtypes. Correlation
analysis for the levels of CSF markers and number of lobar CMBs was performed using

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Spearman’s rank correlation tests in all AD patients.

Data were reported as mean ± SD (standard deviation). All statistical analyses were performed

Inusing SPSS software package (version 24: SPSS Inc., Chicago, IL, USA) applying asignificance level of p < 0.05, and graphs were drafted using GraphPad Prism 7 (GraphPad
Software, La Jolla, CA, USA)

2.7 Standard protocol approvals and patient consent
This study complied with the Declaration of Helsinki and was approved by the Gunma
University Ethical Review Board for Medical Research Involving Human Subjects of Gunma
University (Maebashi, Gunma, Japan), the Geriatrics Research Institute and Hospital (Maebashi,
Gunma, Japan), and Maebashi Red Cross Hospital (Maebashi, Gunma, Japan). The spouse orfamily members of each AD patient provided written informed consent for the patient toMicrobleeds and Biomarkers in AD subtypes 16

participate in the study.

3. RESULTS
3.2 Demographics and CSF biomarkers in typical AD and atypical AD
A total of 117 AD patients were enrolled, then divided into the 85 typical amnestic AD patients(39 EOAD and 46 LOAD) and the 32 atypical AD patients (20 lvPPA and 12 PCA). Clinical

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information of the AD patients and the 40 non-demented (ND) subjects, and CSF biomarkersof them (A1-42, A1-40, A1-38, P-Tau, T-tau, NFL, and YKL-40) were also investigatedIn(Table 1). CSF levels of A1-42 were significantly lower in the typical AD (n = 85) andatypical AD (n = 32) groups compared to the ND group (n = 40) (p < 0.0001, respectively;
Table 1). Meanwhile, CSF levels of P-Tau, T-Tau, NFL, and YKL-40 were significantly higher
in both AD group compared to the ND group (p < 0.0001, respectively; Table 1). Additionally,
the CSF levels of A1-38 were significantly lower in the atypical AD compared to the typicalAD group (p = 0.002), although the results of other CSF biomarkers and the prevalence of
APOE 4 allele did not differ significantly between the typical AD and atypical AD groups(Table 1).

3.1 Demographics of the four subtypes of AD patients

Microbleeds and Biomarkers in AD subtypes 17

We then classified the AD patients into EOAD (n = 39), LOAD (n = 46), lvPPA (n = 20), and
PCA (n = 12) according to the AD criteria (15, 16, 17). The AAO of PCA and EOAD weresignificantly lower than that of LOAD (p < 0.0001, p < 0.0001, respectively), and the AAO of
PCA was significantly lower than that of lvPPA (p = 0.046), however, higher than that of EOAD
(p = 0.0002). The total MoCA scores in lvPPA were significantly lower than those in LOAD (p= 0.015), while the total FAB scores in lvPPA were significantly lower than those in EOAD (p

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= 0.045; Table 2). In the neuropsychological examinations, the scores of auditory sentencecomprehension of lvPPA were significantly lower than those of PCA (p = 0.015), EOAD (p =In

0.042), and LOAD (p = 0.0005). The “intersecting pentagon” scores of PCA were significantly
lower than those of EOAD (p = 0.021) and LOAD (p = 0.0009). The “Clock drawing test”
scores of PCA were significantly lower than those of LOAD (p = 0.028). Additionally, the SLTA
was performed as previously described for speech/language function (10), which revealed thatthe lvPPA patients presented with the hesitant speech and word-finding pauses due to impaired
single word retrieval and difficulty with sentence repetition (data not shown), which agreed
with the criteria described for lvPPA (8, 9).

Microbleeds and Biomarkers in AD subtypes 18

3.3 Prevalence and localization of CMBs in the four clinical AD subtypes
The prevalence of lobar CMBs (1 ≦) in lvPPA (50.00%) was higher than those in the otherthree AD subtypes (EOAD: 23.08%, PCA: 25.00%, LOAD: 36.96%). Among the four AD
subtypes, the prevalence of lobar CMBs in lvPPA was significantly higher than those in both
PCA (p = 0.036), as well as higher, though not significantly, than EOAD (p = 0.082) and LOAD
(p = 0.322) (Table 3). Comparison of the mean number of lobar CMBs per an AD patient with

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lobar CMBs was not significantly different across the four AD subtypes in the bilateral sides (p= 0.312), the right side (p = 0.715) and the left side (p = 0.259) (Table 3). The number of lobar

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CMBs in lvPPA tended to be higher in the right side than the left side, although comparison of
the mean number of lobar CMBs between the right and left sides was not significant among thefour AD subtypes (p = 0.161) (Table 3). Moreover, while the number of lobar CMBs in thefrontal, temporal, and parietal lobes in lvPPA and LOAD tended to be higher than in PCA(Figure 1A, B, C), the number of lobar CMBs in the occipital area in LOAD tended to be higher
than in the other subtypes of AD (Figure 1D). Additionally, the total number of lobar CMBs in
the frontal, temporal, parietal, and occipital lobes in lvPPA and LOAD was significantly higher
than in PCA (p = 0.0332, p = 0.0136, respectively, Figure 1F). There was no significant
difference in the number of CMBs in DWM among the four AD subtypes, although the numberof CMBs on each side of the DWM was less than three in each AD patient (Figure 1E). Among

Microbleeds and Biomarkers in AD subtypes 19

the four AD subtypes with lobar CMBs (lvPPA: n = 10, PCA: n = 3, EOAD: n = 9, LOAD: n =17), lobar CMBs in lvPPA tended to be predominantly localized on the left side, rather thanthose on the right side of frontal, temporal, parietal, and occipital lobes (left side percentage offrontal (68.18%), temporal (65.91%), parietal (67.74%), occipital (70.00%) lobes and total
areas (67.52%) shown in Figure 1A, B, C, D, and F). The total number of CMBs in frontal,
temporal, parietal, and occipital lobe cortices in lvPPA tended to be higher in the left side

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compared to the right, although not significant (p = 0.1833) (Figure 1F), while there was nodifference in the laterality dominance in the other subtypes of AD.

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3.4 Diagnostic values of CSF biomarkers in the AD patients

We did not have data information of CSF from AD patients based upon with postmortem
verification of AD diagnosis. We calculated diagnostic values of the CSF biomarkers of A142 (cut off < 308.7), P-Tau (cut off > 47.43), T-Tau (cut off > 270.2), NFL (cut off > 858.8),
and YKL-40 (cut off > 78.59) by the data from the typical AD (EOAD and LOAD) patients (n= 72) and non-dementia (ND) subjects (n = 33). These data were shown in Table 4.

3.5 Results of CSF biomarkers, APOE 4 alleles, and global 11C PiB-PET retention in thefour AD subtypes

Microbleeds and Biomarkers in AD subtypes 20

CSF biomarkers were analyzed in 18 patients with lvPPA, 12 with PCA, 34 with EOAD, and38 with LOAD, in addition to the 33 ND subjects, by reference to the diagnostic values of CSF
biomarkers (3.4) (Table 5). The CSF levels of A1-42 (pg/ml) in the four AD subtypes were all
significantly lower than those in ND (one-way ANOVA post hoc Dunn’s test, p < 0.0001,
respectively for all four AD subtypes), however, no significant differences were observed acrossthe four AD subtypes (Kruskal-Willis test: p = 0.983). The A1-40 (pg/ml) CSF levels did not

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differ significantly from those in ND (one-way ANOVA post hoc Dunn’s test, 0.05 < p), and no
significant differences were found across the four AD subtypes (Kruskal-Willis test: p = 0.379).

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The A1-38 (pg/ml) CSF levels of the AD subtypes were not significantly different from thosein ND (Kruskal-Willis test: p = 0.035; post hoc difference, 0.05 < p), nor were the CSF A1-38
levels significantly different across the four AD subtypes (0.05 < p) (Table 5).
A total of 135 subjects were analyzed for the CSF levels of P-Tau, T-Tau, NFL, andYKL-40. The levels of CSF P-Tau were significantly higher in lvPPA, EOAD, LOAD, PCA
compared with those in ND (ANOVA post hoc Dunn’s test: p < 0.0001, p < 0.0001, p < 0.0001,
p = 0.0212, respectively). Meanwhile, there were no significant differences observed in P-Tau
across the four AD subtypes (Kruskal-Willis test: p = 0.389). The CSF levels of T-Tau (pg/ml)
were higher in lvPPA, LOAD, EOAD and PCA than ND (p < 0.0001, respectively). However,no significant differences were observed in T-Tau across the four AD subtypes (Kruskal-Willis

Microbleeds and Biomarkers in AD subtypes 21

test: p = 0.756). The CSF levels of NFL (pg/ml) were the highest in lvPPA, followed by LOAD,
EOAD and PCA, and all four AD subgroups had higher levels than ND (p < 0.0001, p < 0.0001,
p < 0.0001, p = 0.036, respectively). Across all four AD subtypes, the CSF levels of NFL in
lvPPA were significantly higher than those in PCA (p = 0.003), EOAD (p = 0.014) and LOAD
(p = 0.045). The CSF levels of YKL-40 (ng/ml) in lvPPA, LOAD, EOAD, and PCA weresignificantly higher than those in ND (p < 0.0001, p < 0.0001, p < 0.001, p = 0.044,

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respectively); meanwhile no significant difference was noted among the four AD subtypes(Kruskal-Willis test: p = 0.148) (Table 5). The prevalence of APOE 4 allele carrier was theInhighest in EOAD (64.71%, n = 22/34), followed by lvPPA (55.56%, n = 10/18), LOAD (52.63%,
n = 20/38), and PCA (41.67%, n = 5/12), with no significant differences among them (Table 5).
There is no significant difference in global mcSUVR across all 14 areas among the four AD
subtypes (Table 5).

3.6 Regional comparison of CBF volumes in the four clinical subtypes of AD
The number of AD patients examined by 99mTc ECD-SPECT were as follows: lvPPA: n = 17,
PCA: n = 12, EOAD: n = 32, and LOAD: n = 36. In the frontal lobe, CBF volumes in lvPPA
were significantly lower than those in LOAD (p = 0.033; Figure 2A). In the temporal lobe, theCBF volumes in lvPPA were significantly lower than those in EOAD (p = 0.0011) and LOAD

Microbleeds and Biomarkers in AD subtypes 22(p = 0.0002; Figure 2B). In the parietal areas, the CBF volumes in PCA and lvPPA were lower
than those in LOAD (p < 0.0001, respectively; Figure 2C). In the occipital lobe, the CBF
volumes in PCA were significantly lower than those in EOAD (p = 0.0098) and LOAD (p =0.002), while those in lvPPA were significantly lower than those in LOAD (p = 0.0311; Figure2D). In the cerebellar region, there was no significant difference in the CBF volumes across all
four AD subtypes (Figure 2E). Among the total CBF volumes, namely FTPO, including those

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of the frontal, temporal, parietal, and occipital lobes in the four AD subtypes, the mean CBF
volumes in lvPPA were significantly lower than those in EOAD (p = 0.047) and LOAD (p =In

0.002), while those in PCA were significantly lower than those in LOAD (p = 0.0132; Figure2F). CBF volumes in lvPPA were significantly lower in the left side of the frontal, temporal,
and parietal lobes than in the right side (*p < 0.05, **p < 0.0001, respectively, shown in Figure
2A-D and 2F).

3.7 Correlation between CBF volume and number of lobar CMBs in the four AD subtypes
Spearman rank correlation tests revealed that the levels of CBF volume were significantly
inversely correlated with the number of lobar CMBs in the left temporal region in lvPPA (r = 0.382, p = 0.046); bilateral (right; left) parietal region (r = -0.397, p = 0.020; r = -0.345, p =0.042, respectively), bilateral (right; left) occipital region (r= -0.353, p = 0.042; r= -0.408, p =Microbleeds and Biomarkers in AD subtypes 23

0.043, respectively), and the bilateral (right; left) temporal region (r= -0.345, p = 0.042; r = 0.368, p = 0.027) in LOAD (Table 6).

3.8 Correlation between the number of lobar CMBs and the levels of CSF markers in the
total AD and the levels of NFL in the AD subtypes
In the total AD subtypes, Spearman rank correlation tests revealed that the CSF levels of A1-

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38, A1-40, A1-42, P-Tau and T-Tau were significantly negatively correlated with the amountsof lobar CMBs (r= -0.274, p = 0.004; r = -0.216, p = 0.019; r = -0.193, p = 0.049; r = -0.298, p

In= 0.0016; r = -0.331, p = 0.0005, respectively, by Spearman’s rank correlation t tests), while
the CSF levels of NFL were significantly positively correlated with the number of lobar CMBs(r = +0.397, p < 0.0001: Spearman t test), although YKL-40 showed no significant difference(FIGURE 3 (A)-(G)). CSF levels of NFL in the lvPPA patients showed significantly positive
correlation with the number of lobar CMBs (r = +0.587, p = 0.005), while the EOAD and LOAD
also show significant differences (r = +0.326, p = 0.034; r = +0.298, p = 0.046) (FIGURE 3(H)-(K)).

4. DISCUSSION
The key findings of our study are as follows. First, the CSF levels of A1-42 were significantlyMicrobleeds and Biomarkers in AD subtypes 24

lower in combined the typical AD group and the atypical AD than in the ND, while the CSF
levels of P-Tau, T-Tau, NFL, and YKL-40 were significantly higher in the typical AD andatypical AD groups compared to the ND group. Second, the CSF levels of NFL in the four AD
subtypes were significantly higher than those of ND, and the CSF levels of NFL weresignificantly higher in lvPPA than in PCA, EOAD, and LOAD among the four AD subtypes.
While the CSF levels of YKL-40 of the four AD subtypes were higher than ND, however, YKL-

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40 did not show significant differences among the four AD subtypes. Third, among the four
clinical AD subtypes, lvPPA had the highest prevalence of lobar CMBs (50%), which wereIn

distributed across the frontal, temporal, parietal, and occipital lobe cortices with a left side
predominance. Meanwhile, LOAD had a tendency of a higher number of lobar CMBs in theoccipital lobe cortices bilaterally than the other AD subtypes, although did not show significant
difference. Among the four AD subtypes, the total number of lobar CMBs in lvPPA and LOAD
were significantly higher than those in PCA. Fourth, the CBF volumes in lvPPA were reducedin the frontal, temporal, and occipital lobe areas, compared to those in the other AD subtypes,
with a significant left side predominance, while the CBF volumes of PCA were significantly
lower in the bilateral occipital and parietal lobes areas than in those of LOAD. Fifth, we
identified a significant inverse correlation between the number of lobar CMBs and the CBF
volume in the left temporal area in lvPPA, and in the bilateral temporal, parietal, and occipital

Microbleeds and Biomarkers in AD subtypes 25

areas in LOAD; while those of PCA and EOAD showed similar inverse correlations, however,were not significant. The global 11C PiB retention had no significant difference among the four
AD subtypes. We found that higher number of lobar CMBs in the AD patients were significantly
associated with lower amount of the smaller peptides A1-38 and higher amount of NFL,although A1-40, A1-42, P-Tau and T-Tau have been reported (49, 50). Furthermore, CSF
levels of NFL in the lvPPA patients was higher in a significantly positive correlation with the

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number of lobar CMBs (p=0.005), while the EOAD and LOAD also show significant
differences (p=0.034, p=0.0046, respectively).

In

We describe our considerations about the results in this study as follows. (1) We
demonstrated that the CSF levels of NFL and YKL-40 were significantly higher in the four AD
subtypes than in ND. Moreover, among the four AD subtypes, the CSF levels of NFL in lvPPA
were significantly higher than those in the other AD subtypes. Additionally, CSF levels of NFL
in the lvPPA patients were higher in significantly positive correlation with the number of lobar
CMBs. (2) Lobar CMBs in the four AD subtypes were most frequently detected in lvPPA,
followed by LOAD, PCA, and EOAD. The APOE 4 allele had the highest frequency in EOAD,
which had the lowest prevalence of lobar CMBs, suggesting that the prevalence of lobar CMBs
may be related not only to APOE 4 allele carrier status, but also to other factors, such asadvanced aging and higher AAO of dementia (lvPPA and LOAD). (3) The lvPPA group showedMicrobleeds and Biomarkers in AD subtypes 26

a higher number of lobar CMBs in the frontal, temporal, and parietal lobe cortices, in contrast
to those of EOAD and PCA, which may involve speech/language disturbances, resulting in
logopenic speech, although the other AD subtypes did not show any laterality predominance of
CMBs in distribution of cerebral cortices. Lobar microbleeds in typical amnestic LOAD andelderly people have been reported to be located predominantly in the occipital lobe cortices,presumably since aging and longevity might induce more severe CAA (5). (4) In lvPPA, AD

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pathology characterized by lobar CMBs with a clear left side predominance is likely to occur
as a result of ischemic vascular pathology derived from lobar CMBs (51). However, althoughIn

we observed high prevalence of lobar CMBs in left frontal areas, we observed a significant
inverse correlation between CBF volume and the number of lobar CMBs in the left temporal
region in lvPPA. In PCA, bilateral occipital and parietal lobes showed a decrease in CBF
volumes, likely due to localized brain atrophy, possibly involving visuospatial cognitive
syndromes. Consistent with three previously published reports (21–23), while the CSF levelsof P-Tau, T-Tau, and NFL in all PCA patients were significantly higher than in the ND group,
the lowest among all four AD groups was observed in PCA patients who also showed positive
11

C PiB retention, suggesting that PCA, based upon A pathology, may not be as aggressive a

disease of tau pathology and axonal neurodegeneration as the other AD subtypes. For thisreason, PCA of the amnestic AD type was considered to have unique differential pathology

Microbleeds and Biomarkers in AD subtypes 27

from that of typical AD even if compatible with AD criteria (52). The elevated CSF NFL levels
were significantly related to longitudinal cognitive decline in AD and mild cognitive
impairment (MCI) (25). Moreover, CSF NFL had a stronger correlation than those of T-Tau and
P-Tau, leading to brain atrophy and progression of cognitive decline in AD (26). Pathologically,
tau accumulation is reported to cause a great burden to cortical areas of the predominanthemisphere in lvPPA (53). With regards to lvPPA, the coexistence of AD pathology and

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argyrophilic thorny astrocytes clusters (ATAC) have been focused on intensely in tau
immunoreactive pathology in fronto-temporo-parietal cortices as well as subcortical regions in

In

lvPPA, suggesting that they may be markers of a process responsible for the prominent focal
clinical manifestations of lvPPA based upon AD pathology (54, 55). Quite recently, Buciuc et
al. reported that CAA pathology was the dominant risk factor of CMBs/SS (cortical superficial
siderosis) in LPA (lvPPA) by neuroimaging-pathological analyses, they emphasized that
CMB/SS were frequent in LPA patients (46%: 6/13) pathologically with moderate/severe CAA(56). They also described that they did not observe a higher frequency/number of CMBs/SS in
the regions with the most severe CAA, nor observe a topolographic relationship between
CMBs/SS location and regional PiB uptake or regional hypometabolism, although most
CMBs/SS co-occurred with some degree of regional CAA. Their results were consistent withanother neuroimaging-neuropathological study of CMBs/SS and CAA, where CMBs/SS

Microbleeds and Biomarkers in AD subtypes 28

occurred at the sites with reduced CAA (57). We found a significant inverse correlation between
CBF volume and the number of lobar CMBs in the left temporal region in lvPPA, although mostfrequent prevalence of lobar CMBs was left frontal areas, which might imply that lobar CMBs
were not necessarily determinant for decreased CBF volume, but regional brain atrophy mightreflect the decrease of CBF volume.
We found that CSF levels of NFL were significantly higher in lvPPA than those of

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other AD subtypes after controlling for multiple comparison, additionally, CSF NFL elevatedby the increased number of lobar CMBs in lvPPA patients, presumably that lobar CMBs and/orIn

CAA pathology in lvPPA might involve secondary and/or indirectly neuronal and axonal
degeneration based upon AD pathology. Indeed, in analyses of functional neuronal connectivity
in lvPPA patients, not only the language network (posterior superior temporal gyrus and inferior
frontal lobe) but also the working memory network (frontal regions, inferior parietal lobule,superior, and middle temporal gyri) have been shown to be widely disturbed with a left side
predominance by a resting-state fMRI study (58). Our findings of lvPPA imply that left
predominant hypoperfusion may occur due to left dominant brain atrophy causing by neuronaland axonal degeneration, and possibly and/or partly due to microcirculation aberration causing
by lobar CMBs and cerebrovascular injuries.
The limitation of this study was the absence of post-mortem autopsy analysis for any

Microbleeds and Biomarkers in AD subtypes 29of the patients, which prevented us from performing pathological diagnosis to support ourclinical diagnosis. Nonetheless, to ensure that the atypical AD group (lvPPA and PCA) did notinclude patients with CBS, PSP, FTD, DLB, or other neurodegenerative diseases that present
with dementia, we performed a careful clinical diagnosis in strict accordance with the current
global criteria (8, 9, 1317).

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5. CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial orInfinancial relationships that could be constructed as a potential conflict of interest.

6. AUTHOR CONTRIBUTION STATEMENT
MI, HK, KN, FY, MK, KH, NF, SN, YH, NH, YT, HT, SY, TY, YT, YA, MA, HY, KO, MT and
YI collected the clinical data, interpreted the data, and MI wrote the manuscript. ET analyzed
genomic DNA from the patient’s blood samples and CSF biomarkers from the patient’s CSF.
ES and AK performed neuropsychological examinations. MI, SK, HK, TH, YT and KI
evaluated the neuroimaging information. KK, HS and TS organized the neuroimaging systems.
MI and YI performed the clinical data analysis and evaluated the specificities and neurologicalsignificances.

Microbleeds and Biomarkers in AD subtypes 30

7. FUNDING
This study was supported by a Grant-in-Aid for Scientific Research (C) (M.I.:18K07491, T.Y.:
17K09790, T.H.: 19K08220, Y.T.: 18K07627, K.I.: 17K09813, Y.I.: 19K07813) from the
Ministry of Education, Culture, Sports, Science and Technology of Japan, and a research
program entitled ‘‘Dominantly Inherited Alzheimer’s Network-Japan (DIAN-J)’’ of the

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Ministry of Health, Labour and Welfare, also received an “Ai-no-bokin” (Donation of love)from a grant for dementia research from the JOMO-SHINBUN newspaper company (Maebashi,In

Gunma, Japan).

8. ACKNOWLEDGEMENTS
We especially thank Dr Hirofumi Kawakami, an Advanced Application Specialist who belongs
to Headquarter of Academy, GE Healthcare Japan, for his excellent technical assistance. We
thank Dr Mikio Shoji with academic discussions about this manuscript. We would like to thank
Editage (www.editage.com) for English language editing.

9. DATA AVAILABILITY STATEMENT
All data generated or analyzed during this study are included in this published article.

Microbleeds and Biomarkers in AD subtypes 31

LEGENDSTABLE 1. Demographics and CSF biomarkers in typical/atypical AD patients
Differences between groups were analyzed using a chi-squared test for the categories of sex,
hypertension, diabetes, and hypercholesterolemia, and a Kruskal-Wallis test, and variance was

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analyzed using post hoc Dunn tests (for age at onset, duration of illness, years of education, andtotal scores of MMSE, MoCA, and FAB, respectively). Data are presented as mean ± standardIndeviation. NA: not applicable. ND: non-demented subject. *p < 0.0001 comparing the ND andthe typical and atypical AD groups combined.

TABLE 2. Demographics of the four subtypes of AD patients
Differences between groups were analyzed using a chi-squared test for the categories of sex,
hypertension, diabetes, and hypercholesterolemia, and a Kruskal-Wallis test, and variance wasanalyzed using post hoc Dunn tests (for age at onset, duration of illness, years of education,
total scores of MMSE, MoCA, FAB, respectively, and the sub-scores of Language, Calculation,
Visuospatial functioning, and the Clock drawing test).

Microbleeds and Biomarkers in AD subtypes 32

TABLE 3. Prevalence and mean number of lobar CMBs in the four subtypes of AD
patients patients
Data are presented as mean ± standard deviation. Differences between groups were analyzed
using a chi-squared test, and a Kruskal-Wallis test, and variance was analyzed using post hoc
Dunn tests for mean lobar CMBs numbers (No.), respectively.

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TABLE 4. Diagnostic values of CSF biomarkers in the typical AD patients vs. ND
AD: Alzheimer’s disease, ND: non-dementia subjects, A: Amyloid , P-Tau: phosphorylatedIntau, T-tau: total tau, NFL: neurofilament light chain, YKL-40: chitinase 3-like 1 protein.

Table 5. CSF biomarkers, APOE 4 alleles and global 11C PiB-PET retention in the four
subtypes of AD patients
A Kruskal-Wallis test, one-way ANOVA multiple comparison analysis and post hoc Dunncorrection yielded the following results: CSF levels of A1-42 were lower, and CSF levels of
P-Tau, T-Tau, NFL, and YKL-40 were higher than those of ND: ap < 0.0001, bp < 0.001, cp <0.01, dp < 0.05. Among the four subtypes of AD, CSF levels of NFL in lvPPA were significantly
higher than those in PCA (p = 0.003), EOAD (p = 0.014), and LOAD (p = 0.045).

Microbleeds and Biomarkers in AD subtypes 33

TABLE 6. Correlation between the levels of CBF volumes and the number of lobar CMBs
in the four subtypes of AD patients
The levels of CBF volume were significantly correlated with the number of lobar CMBs at theleft temporal lobe (r = -0.382, p = 0.046) in lvPPA, and at the right and the left temporal lobes(r = -0.345, p = 0.042; r = -0.368, p = 0.027, respectively), at the right and the left parietal lobes(r = -0.397, p = 0.020; r = -0.345, p = 0.042, respectively), and at the right and the left occipital

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areas (r = -0.353, p = 0.042; r = -0.408, p = 0.043, respectively) in LOAD.

In

FIGURE 1. Regional comparison of the number of CMBs in the four AD subtypes ofAD

Number of lobar CMBs in frontal (A), temporal (B), parietal (C), occipital (D) lobes, and deep
white matter (DWM) (E) of lvPPA, PCA, EOAD, and LOAD. Total number of lobar CMBs infrontal, temporal, parietal and occipital lobe cortices (F). *p < 0.05.

FIGURE 2. Regional comparison of CBF volumes in the four AD subtypes in 99mTc ECDSPECTCBF volumes in frontal (A), temporal (B), parietal (C), occipital (D) lobes and cerebellum (E)of lvPPA, PCA, EOAD and LOAD. Total CBF volumes, namely, Sum of (A)-(D), in frontal,

Microbleeds and Biomarkers in AD subtypes 34

temporal, parietal and occipital lobes (F). *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

FIGURE 3. Correlations between the number of lobar cerebral microbleeds, andcerebrospinal fluid levels of A1-38, A1-40 A1-42, P-Tau, T-Tau, NFL and YKL-40 in
the total AD subtypes, and CSF NFL in the lvPPA
Scatter plots presenting the correlations per patient between total number of lobar CMBs and

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CSF biomarkers, A1-38 (A), A1-40 (B), A1-42 (C), P-Tau (D), T-Tau (E), NFL (F), andYKL-40 (G). The number of CMBs and the levels of CSF NFL in the lvPPA (H), the PCA (I)In

the EOAD (J), and the LOAD (K).

SUPPLEMENTAL TABLE 1.
Assay quality data of Inter-assay CVs (Coefficients of variation) %.
Inter-assay CVs of A1-42, A1-40 and A1-38 were less than 15%, 15% and 20%,
respectively. Inter-assay CVs of P-Tau, T-Tau, NFL, and YKL-40 were less than 15%, 20%,
20 % and 15 %, respectively.

SUPPLEMENTAL TABLE 2.
MRI parameters.

Microbleeds and Biomarkers in AD subtypes 35

FA = Flip angle, ST = Slice thickness, TE = Time to echo, TI = Time for inversion, TR = Timeto repeat.

SUPPLEMENTAL TABLE 3.
Distribution of patients across field strengths and sequences.
1.5 T and 3.0 T columns included all the patients scanned on these field strengths in the different

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AD subgroups. There was no significant difference among the four subgroups of AD acrossfield strength and hemosiderin positive sequences.

In

REFERENCES

1. Vinters HV. Cerebral amyloid angiopathy. A critical review. Stroke. (1987) 18:311–24. doi:
10.116/01.str.18.2.311

2. Greenberg SM, Finklestein SP, Schaefer PW. Petechial hemorrhages accompanying lobar
hemorrhage: detection by gradient-echo MRI. Neurology. (1996) 46:1751–54. doi:
10.1212/wnl.46.6.1751

Microbleeds and Biomarkers in AD subtypes 36

3. Cordonnier C, van der Flier WM, Sluimer JD, Leys D, Barkhof F, Scheltens P. Prevalenceand severity of microbleeds in a memory clinic setting. Neurology. (2006) 66:1356–60. doi:
10.1212/01.wnl.0000210535.20297

4. Viswanathan A, Greenberg SM. Cerebral amyloid angiopathy in the elderly. Ann Neurol.
(2011) 70:871–80. doi: 10.1002/ana.22516

w
e
i
v
re

5. Pettersen JA, Sathiyamoorthy G, Gao FQ, Szilagyi G, Nadkarni NK, St George-Hyslop P,

In

et al. Microbleed topography, leukoaraiosis, and cognition in probable Alzheimer disease
from the Sunnybrook dementia study. Arch Neurol. (2008) 65:790–795. doi:
10.1001/archneur.65.6.790

6. Gregg NM, Kim AE, Gurol ME, Lopez OL, Aizenstein HJ, Price JC et al. Incidental
Cerebral Microbleeds and Cerebral Blood Flow in Elderly Individuals. JAMA Neurol.
(2015) 72:1021–8. doi: 10.1001/jamaneurol.2015.1359

7. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al.
Neuroimaging standards for research into small vessel disease and its contribution to ageingMicrobleeds and Biomarkers in AD subtypes 37

and neurodegeneration. Lancet Neurol. (2013) 12:822–38. doi: 10.1016/S1474–
4422(13)70124–8

8. Mesulam MM. Primary progressive aphasia. Ann Neurol. (2001) 49:425–32

9. Gorno-Tempini ML, Hillis AE, Weintraub S, Kertesz A, Mendez M, Cappa SF, et al.

w
e
i
v
re

Classification of primary progressive aphasia and its variants. Neurology. (2011) 76:1006–
14. doi: 10.1212/WNL.0b013e31821103e6

In

10. Ikeda M, Tashiro Y, Takai E, Kurose S, Fugami N, Tsuda K, et al. CSF levels of Aβ138/Aβ1-40/Aβ1-42 and 11C PiB-PET studies in three clinical variants of primary
progressive aphasia and Alzheimer's disease. Amyloid. (2014) 21:238–45. doi:
10.3109/13506129.2014.949231

11. Rohrer JD, Rossor MN, Warren JD. Alzheimer's pathology in primary progressive aphasia.
Neurobiol Aging. (2012) 33:744–52. doi: 10.1016/j.neurobiolaging.2010.05.020

Microbleeds and Biomarkers in AD subtypes 38

12. Benson DF, Davis RJ, Snyder BD. Posterior cortical atrophy. Arch Neurol. (1988) 45:789–
93. doi: 10.1001/archneur.1988.00520310107024

13. Crutch SJ, Lehmann M, Schott JM, Rabinovici GD, Rossor MN, Fox NC. Posterior cortical
atrophy. Lancet Neurol. (2012) 11:170–78. doi: 10.1016/S1474–4422(11)70289–7

w
e
i
v
re

14. Tang-Wai DF, Graff-Radford NR, Boeve BF, Dickson DW, Parisi JE, Crook R, et al. Clinical,
genetic, and neuropathologic characteristics of posterior cortical atrophy. Neurology. (2004)

In

63:1168–74. doi: 10.1212/01.wnl.0000140289.18472.15

15. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Jr., Kawas CH, et al. The
diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National
Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for
Alzheimer’s disease. Alzheimers Dement. (2011) 7:263–9. doi: 10.1016/j.jalz.2011.03.005

16. American Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental
Disorders, Fifth Edition: DSM-5. Arlington, VA: American Psychiatric Association

Microbleeds and Biomarkers in AD subtypes 39

17. Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing
research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria. Lancet Neurol(2014) 13:614–29. Erratum in: Lancet Neurol. (2014) 13:757. doi: 10.1016/S1474–
4422(14)70090–0

18. Rabinovici GD, Jagust WJ, Furst AJ, Ogar JM, Racine CA, Mormino EC, et al. Abeta

w
e
i
v
re

amyloid and glucose metabolism in three variants of primary progressive aphasia. Ann
Neurol. (2008) 64:388–401. doi: 10.1002/ana.21451

In

19. Lehmann M, Ghosh PM, Madison C, Laforce R Jr, Corbetta-Rastelli C, Weiner MW, et al.
Diverging patterns of amyloid deposition and hypometabolism in clinical variants ofprobable Alzheimer's disease. Brain. (2013) 136:844–58. doi: 10.1093/brain/aws327

20. Ossenkoppele R, Schonhaut DR, Schöll M, Lockhart SN, Ayakta N, Baker SL, et al. Tau
PET patterns mirror clinical and neuroanatomical variability in Alzheimer's disease. Brain.
(2016) 139:1551–67. doi: 10.1093/brain/aww027

Microbleeds and Biomarkers in AD subtypes 40

21. de Souza LC, Lamari F, Belliard S, Jardel C, Houillier C, De Paz R, et al. Cerebrospinal
fluid biomarkers in the differential diagnosis of Alzheimer's disease from other corticaldementias.

J

NeurolNeurosurg

Psychiatry.

(2011)

82:240–6.

doi:

10.1136/jnnp.2010.207183

22. Ossenkoppele R, Mattsson N, Teunissen CE, Barkhof F, Pijnenburg Y, Scheltens P, et al.

w
e
i
v
re

Cerebrospinal fluid biomarkers and cerebral atrophy in distinct clinical variants of probable
Alzheimer's

disease.

In

Neurobiol

Aging.

(2015)

36:2340–47.

doi:

10.1016/j.neurobiolaging.2015.04.011

23. Paterson RW, Toombs J, Slattery CF, Nicholas JM, Andreasson U, Magdalinou NK, et al.
Dissecting IWG-2 typical and atypical Alzheimer's disease: insights from cerebrospinal
fluid analysis. J Neurol. (2015) 262:2722–30. doi: 10.1007/s00415–015–7904–3

24. Whitwell JL, Kantarci K, Weigand SD, Lundt ES, Gunter JL, Duffy JR, et al. Microbleeds
in atypical presentations of Alzheimer's disease: a comparison to dementia of the
Alzheimer's type. J Alzheimers Dis. (2015) 45:1109–17. doi: 10.3233/JAD–142628

Microbleeds and Biomarkers in AD subtypes 41

25. Preische O, Schultz SA, Apel A, Kuhle J, Kaeser SA, Barro C, et al; Dominantly Inherited
Alzheimer Network. Serum neurofilament dynamics predicts neurodegeneration andclinical progression in presymptomatic Alzheimer's disease. Nat Med. (2019) 25:277–83.
doi: 10.1038/s41591-018-0304-3.

26. Dhiman K, Gupta VB, Villemagne VL, Eratne D, Graham PL, Fowler C, et al.
Cerebrospinal fluid neurofilament light concentration predicts brain atrophy and cognition

w
e
i
v
re

in Alzheimer's disease. Alzheimers Dement (Amst). (2020) 12(1):e12005. doi:
10.1002/dad2.12005.

In

27. Antonell A, Mansilla A, Rami L, Lladó A, Iranzo A, Olives J, et al. Cerebrospinal fluid level
of YKL-40 protein in preclinical and prodromal Alzheimer's disease. J Alzheimers Dis.
(2014) 42:901–8. doi: 10.3233/JAD-140624.

28. Banerjee G, Ambler G, Keshavan A, Paterson RW, Foiani MS, Toombs J, et al.
Cerebrospinal Fluid Biomarkers in Cerebral Amyloid Angiopathy. J Alzheimers Dis. (2020)
74:1189–1201. doi: 10.3233/JAD-191254.

29. Folstein MF, Folstein SE, McHugh PR. ‘‘Mini-mental state’’. A practical method forgrading the cognitive state of patients for the clinician. J Psychiatr Res. (1975) 12:189–98.

Microbleeds and Biomarkers in AD subtypes 42

doi: 10.1016/0022-3956(75)90026–6

30. Nasreddine ZS, Phillips NA, Bedirian V, Charbonneau S, Whitehead V, Collin I, et al. The
Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive
impairment. J Am Geriatr Soc. (2005) 53:695–9. doi: 10.1111/j.1532-5415.2005.53221.x

w
e
i
v
re

31. Dubois B, Slachevsky A, Litvan I, Pillon B. The FAB: A Frontal assessment battery at
bedside. Neurology. (2000) 55:1621–26. doi: 10.1212/wnl.55.11.1621

In

32. Holsinger T, Deveau J, Boustani M, Williams JW Jr. Does this patient have dementia?
JAMA. (2007) 297:2391–2404. doi: 10.1001/jama.297.21.2391

33. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis

of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices ofdepartment of health and human services task force on Alzheimer’s disease. Neurology.

(1984) 34:939–44. doi: 10.1212/wnl.34.7.939

Microbleeds and Biomarkers in AD subtypes 43

34. Mimura M, Kato M, Kato M, Sano Y, Kojima T, Naeser M, Kashima H. Prospective andretrospective studies of recovery in aphasia. Changes in cerebral blood flow and language

functions. Brain. (1998) 121:2083–94. doi: 10.1093/brain/121.11.2083

35. Ikeda M, Yonemura K, Kakuda S, Tashiro Y, Fujita Y, Takai E, et al. Cerebrospinal fluid

w
e
i
v
re

levels of phosphorylated tau and Aβ1-38/Aβ1-40/Aβ1-42 in Alzheimer's disease with PS1
mutations. Amyloid. (2013) 20:107–12. doi: 10.3109/13506129.2013.790810

In

36. Andreasen N, Minthon L, Davidsson P, Vanmechelen E, Vanderstichele H, Winblad B, et al.
Evaluation of CSF-tau and CSF- Aβ42 as diagnostic markers for Alzheimer’s disease inclinical practice. Arch Neurol. (2001) 58:373–9. doi: 10.1001/archneur.58.3.373

37. Norgren N, Rosengren L, Stigbrand T. Elevated neurofilament levels in neurologicaldiseases. Brain Res. (2003) 987:25–31. doi: 10.1016/s0006–8993(03)03219–0

38. Ikeda M, Kuwabara T, Takai E, Kasahara H, Furuta M, Sekine A, et al. Increased
neurofilament light chain and YKL-40 CSF levels in one Japanese IBMPFD patient with

Microbleeds and Biomarkers in AD subtypes 44

VCP R155C mutation: a clinical case report with CSF biomarker analyses. Front Neurol.
(2020) 11:757. doi: 10.3389/fneur.2020.00757.

39. Molinuevo JL, Ayton S, Batrla R, Bednar MM, Bittner T, Cummings J, et al. Current stateof Alzheimer's fluid biomarkers. Acta Neuropathol. (2018) 136:821–53. doi:
10.1007/s00401-018-1932-x.

w
e
i
v
re

40. Baldacci F, Lista S, Palermo G, Giorgi FS, Vergallo A, Hampel H. The neuroinflammatory
biomarker YKL-40 for neurodegenerative diseases: advances in development. Expert Rev

In

Proteomics. (2019) 16:593–600. doi: 10.1080/14789450.2019.1628643.

41. Gregoire SM, Chaudhary UJ, Brown MM, Yousry TA, Kallis C, Jäger HR, et al. The
microbleed anatomical rating scale (MARS): reliability of a tool to map brain microbleeds.
Neurology. (2009) 73:1759–66. doi: 10.1212/WNL.0b013e3181c34a7d

42. Matsuda H, Mizumura S, Nagao T, Ota T, Iizuka T, Nemoto K, et al. Automateddiscrimination between very early Alzheimer disease and controls using an easy Z-score
imaging system for multicenter brain perfusion single-photon emission tomography. Am J
Neuroradiol. (2007) 28:731–6

Microbleeds and Biomarkers in AD subtypes 45

43. Ito K, Shimano Y, Imabayashi E, Nakata Y, Omachi Y, Sato N, et al. Concordance
between 99mTc-ECD SPECT and 18F-FDG PET interpretations in patients with cognitive
disorders diagnosed according to NIA-AA criteria. Int J Geriatr Psychiatry. (2014)
29:1079–86. doi: 10.1002/gps.4102

44. Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, et. Imaging brain

w
e
i
v
re

amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol. (2004) 55:306–
19. doi: 10.1002/ana.20009

In

45. Lopresti BJ, Klunk WE, Mathis CA, Hoge JA, Ziolko SK, Lu X, et al. Simplified
quantification of Pittsburgh Compound B amyloid imaging PET studies: A comparative
analysis. J Nucl Med. (2005) 46:1959–72

46. McNamee RL, Yee SH, Price JC, Klunk WE, Rosario B, Weissfeld L, et al. Considerationof optimal time window for Pittsburgh compound B PET summed uptake measurements. J
Nucl Med. (2009) 50:348–55. doi: 10.2967/jnumed.108.057612

47. Yamane T, Ishii K, Sakata M, Ikari Y, Nishio T, Ishii K, et al. Inter-rater variability ofMicrobleeds and Biomarkers in AD subtypes 46

visual interpretation and comparison with quantitative evaluation of 11C-PiB PET amyloid
images of the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) multicenter
study. Eur J Nucl Med Mol Imaging. (2017) 44:850–7. doi: 10.1007/s00259–016–3591–2

48. Kasahara H, Ikeda M, Nagashima K, Fujita Y, Makioka K, Makioka K, et al. Deep whitematter lesions are associated with early recognition of dementia in Alzheimer's disease. J

w
e
i
v
re

Alzheimers Dis. (2019) 68:797–808. doi: 10.3233/JAD-180939

In

49. Shams S, Granberg T, Martola J, Li X, Shams M, Fereshtehnejad SM, et al. Cerebrospinal
fluid profiles with increasing number of cerebral microbleeds in a continuum of cognitive
impairment.

J

Cereb

Blood

Flow

Metab.

(2016)

36:621–8.

doi:

10.1177/0271678X15606141

50. : Noguchi-Shinohara M, Komatsu J, Samuraki M, Matsunari I, Ikeda T, Sakai K, et al.
Cerebral Amyloid Angiopathy-Related Microbleeds and Cerebrospinal Fluid Biomarkers
in Alzheimer's Disease. J Alzheimers Dis. (2017) 55:905–13. doi: 10.3233/JAD–160651

Microbleeds and Biomarkers in AD subtypes 47

51. Mendes A, Bertrand A, Lamari F, Colliot O, Routier A, Godefroy O, et al. Cerebral
microbleeds and CSF Alzheimer biomarkers in primary progressive aphasias. Neurology.
(2018) 90:e1057–e1065. doi: 10.1212/WNL.0000000000005165

52. Wellington H, Paterson RW, Suárez-González A, Poole T, Frost C, Sjöbom U, et al. CSF
neurogranin or tau distinguish typical and atypical Alzheimer disease. Ann Clin Transl

w
e
i
v
re

Neurol. (2018) 5:162–71. doi: 10.1002/acn3.518

In

53. Petersen C, Nolan AL, de Paula França Resende E, Miller Z, Ehrenberg AJ, Gorno-Tempini
ML, et al. Alzheimer's disease clinical variants show distinct regional patterns ofneurofibrillary tangle accumulation. Acta Neuropathol. (2019) 138:597–612. doi:
10.1007/s00401-019-02036-6.

54. Munoz DG, Woulfe J, Kertesz A. Argyrophilic thorny astrocyte clusters in association with
Alzheimer's disease pathology in possible primary progressive aphasia. Acta Neuropathol.
(2007) 114: 347–57. doi: 10.1007/s00401–007–0266–x

Microbleeds and Biomarkers in AD subtypes 48

55. Resende EPF, Nolan AL, Petersen C, Ehrenberg AJ, Spina S, Allen IE, et al. Language andspatial dysfunction in Alzheimer disease with white matter thorn-shaped astrocytes.
Neurology. (2020) 94:e1353-e1364. doi: 10.1212/WNL.0000000000008937.

56. Buciuc M, Duffy JR, Machulda MM, Spychalla AJ, Gunter JL, Jack CR Jr, et al.
Association of amyloid angiopathy with microbleeds in logopenic progressive aphasia: an

w
e
i
v
re

imaging-pathology study. Eur J Neurol. (2021) 28:670–5. doi: 10.1111/ene.14594.

In

57. van Veluw SJ, Biessels GJ, Klijn CJ, Rozemuller AJ. Heterogeneous histopathology ofcortical microbleeds in cerebral amyloid angiopathy. Neurology. (2016) 86:867–71. doi:
10.1212/WNL.0000000000002419

58. Whitwell JL, Jones DT, Duffy JR, Strand EA, Machulda MM, Przybelski SA, et al.
Working memory and language network dysfunctions in logopenic aphasia: a task-free
fMRI comparison with Alzheimer's dementia. Neurobiol Aging. (2015) 36:1245-52. doi:
10.1016/j.neurobiolaging.2014.12.013.

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CASE REPORT
published: xx June 2021
doi: 10.3389/fneur.2021.645625
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Edited by:Achim Gass,
University of Heidelberg, Germany
Reviewed by:
Katsutoshi Furukawa,Tohoku University, Japan
Lukas Sveikata,
Massachusetts General Hospital andHarvard Medical School,
United States
Kenjiro Ono,
Showa University, Japan*Correspondence:
Masaki Ikeda
ikedam8@gmail.com

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Specialty section:
This article was submitted to
Applied Neuroimaging,a section of the journal
Frontiers in Neurology
Received: 15 January 2021
Accepted: 05 May 2021
Published: xx June 2021
Citation:
Ikeda M, Okamoto K, Suzuki K,
Takai E, Kasahara H, Furuta N,
Furuta M, Tashiro Y, Shimizu C,
Takatama S, Naito I, Sato M, Sakai Y,
Takahashi M, Amari M, Takatama M,
Higuchi T, Tsushima Y, Yokoo H,
Kurabayashi M, Ishibashi S, Ishii K
and Ikeda Y (2021) Recurrent Lobar
Hemorrhages and Multiple Cortical
Superficial Siderosis in a Patient of
Alzheimer’s Disease With
Homozygous APOE ε2 Allele
Presenting Hypobetalipoproteinemia
and Pathological Findings of
18
F-THK5351 Positron Emission
Tomography: A Case Report.
Front. Neurol. 12:645625.
doi: 10.3389/fneur.2021.645625

Recurrent Lobar Hemorrhages
and Multiple Cortical Superficial
Siderosis in a Patient of Alzheimer’s
Disease With Homozygous APOE ε2Allele Presenting
Hypobetalipoproteinemia andPathological Findings of
18 F-THK5351 Positron Emission
Tomography: A Case Report

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Masaki Ikeda *, Koichi Okamoto , Keiji Suzuki , Eriko Takai , Hiroo Kasahara ,Natsumi Furuta 3 , Minori Furuta 3 , Yuichi Tashiro 5 , Chisato Shimizu 2 , Shin Takatama 6 ,Isao Naito 6 , Mie Sato 7 , Yasujiro Sakai 2 , Manabu Takahashi 8 , Masakuni Amari 2 ,Masamitsu Takatama 2 , Tetsuya Higuchi 9 , Yoshito Tsushima 9 , Hideaki Yokoo 10 ,Masahiko Kurabayashi 11 , Shun Ishibashi 10 , Kenji Ishii 12 and Yoshio Ikeda 3

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Division of General Education (Neurology), Faculty of Health & Medical Care, Saitama Medical University, Saitama, Japan,
Department of Neurology, Geriatrics Research Institute and Hospital, Maebashi, Japan, 3 Department of Neurology, Gunma
University Graduate School of Medicine, Maebashi, Japan, 4 Department of Pathology, Geriatrics Research Institute andHospital, Maebashi, Japan, 5 Department of Neurology, Mito Medical Center, Mito, Japan, 6 Department of Neurosurgery,
Geriatrics Research Institute and Hospital, Maebashi, Japan, 7 Department of Anesthesiology, Geriatrics Research Instituteand Hospital, Maebashi, Japan, 8 Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine,
Maebashi, Japan, 9 Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of
Medicine, Maebashi, Japan, 10 Division of Endocrinology and Metabolism, Department of Internal Medicine, Jichi Medical
University, Tochigi, Japan, 11 Department of Pathology, Gunma University Graduate School of Medicine, Maebashi, Japan,
12Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
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In Alzheimer’s disease, the apolipoprotein E gene (APOE) ε2 allele is a protective genetic
factor, whereas the APOE ε4 allele is a genetic risk factor. However, both the APOE ε2
and the APOE ε4 alleles are genetic risk factors for lobar intracerebral hemorrhage. The
reasons for the high prevalence of lobar intracerebral hemorrhage and the low prevalenceof Alzheimer’s disease with the APOE ε2 allele remains unknown. Here, we describe thecase of a 79-year-old Japanese female with Alzheimer’s disease, homozygous for the
APOE ε2 allele. This patient presented with recurrent lobar hemorrhages and multiple
cortical superficial siderosis. The findings on the 11 C-labeled Pittsburgh Compound
B-positron emission tomography (PET) were characteristic of Alzheimer’s disease.
18 F-THK5351 PET revealed that the accumulation of 18 F-THK 5351 in the right pyramidal
tract at the pontine level, the cerebral peduncle of the midbrain, and the internal capsule,

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reflecting the lesions of the previous lobar intracerebral hemorrhage in the right frontal
lobe. Moreover, 18 F-THK5351 accumulated in the bilateral globus pallidum, amygdala,caudate nuclei, and the substantia nigra of the midbrain, which were probably off-target
reaction, by binding to monoamine oxidase B (MAO-B). 18 F-THK5351 were also
detected in the periphery of prior lobar hemorrhages and a cortical subarachnoid
hemorrhage, as well as in some, but not all, areas affected by cortical siderosis. Besides,
18 F-THK5351 retentions were observed in the bilateral medial temporal cortices andseveral cortical areas without cerebral amyloid angiopathy or prior hemorrhages, possibly
where tau might accumulate. This is the first report of a patient with Alzheimer’s disease,carrying homozygous APOE ε2 allele and presenting with recurrent lobar hemorrhages,
multiple cortical superficial siderosis, and immunohistochemically vascular amyloid β.
The 18 F-THK5351 PET findings suggested MAO-B concentrated regions, astroglial
activation, Waller degeneration of the pyramidal tract, neuroinflammation due to CAA
related hemorrhages, and possible tau accumulation.

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Keywords: Alzheiemer’s disease, apolipoprotein ǫ allele, cerebral amyloid angiopathy, cortical superficial
siderosis, recurrent lobar brain hemorrhages, hypobetalipoproteinemia, 18 F-THK5351 PET, 11 C-PiB PET

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INTRODUCTIONMETHODS

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11 C-PiB PET and 18 F-THK5351 PET

The apolipoprotein E gene (APOE) ε2 allele has an exceptionally
low likelihood of causing Alzheimer’s dementia in APOE2
homozygotes (1) and, it protects against Alzheimer’s disease(AD) (2). In contrast, the APOE ε4 allele is a genetic risk
factor of AD (3). However, the APOE ε2 and the APOE ε4
alleles, responsible for causing severe alterations in the vascularwall structure causing cerebral amyloid angiopathy (CAA), arecommon genetic risk factors for lobar intracerebral hemorrhage(ICH) (4–7). The reason for the high prevalence of lobar
ICH despite the very low prevalence of AD in individuals
with the APOE ε2 allele remains unknown. 18 F-THK5351,
which was developed as a tau binding tracer (8, 9), has beenshown to strongly bind to monoamine oxidase B (MAO-B)(10). Recently, in a patient of cerebral infarction, the off-target
retentions of 18 F-THK5351 revealed Wallerian degeneration of
the pyramidal tract, basal ganglia, thalamus, and cerebral cortices(11). In this report, we describe a patient with AD who carrieda homozygous APOE ε2 allele. This patient presented withrecurrent lobar hemorrhages and multiple cortical superficial
siderosis (cSS) with hypobetalipoproteinemia. Additionally,
several 18 F-THK5351 PET findings reflected an increase in the
MAO-B, reactive astrogliosis, and Wallerian degeneration of
the pyramidal tract due to lobar ICH, as well as the possible
accumulation of tau, related to AD pathology, in cerebral
cortical areas.

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A Cerebrospinal fluid was obtained by a lumbar puncture of
the L3/L4 or L4/L5 intervertebral space, and the CSF samples
were centrifuged for 10 min at 1,800 × g at 4◦ C within 3 hof collection. Samples were divided into aliquots of 0.5 mL
and stored at −80◦ C in polypropylene tubes until they wereanalyzed using enzyme-linked immunosorbent assay (ELISA)
kits for human CSF amyloid β (Aβ) Aβ1-42 and Aβ1-40(FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan)(12). Phosphorylated tau 181 (P-Tau) and human total tau(T-Tau) levels in the CSF were measured using the sandwich
using ELISA INNOTESTr PHOSPHO-TAU (181P) (15) andthe sandwich ELISA INNOTESTr T-Tau-Ag (both FUJIREBIO,
Ghent, Belgium) (16), respectively, as described previously(12, 17).

Abbreviations: APOE, apolipoprotein E gene; AD, Alzheimer’s disease; ICH,
intracerebral hemorrhage; CAA, cerebral amyloid angiopathy; MAO-B,
monoamine oxidase B; cSS, cortical superficial siderosis; 11 C PiB-PET, 11 Clabeled Pittsburgh Compound B-positron emission tomography; Aβ, amyloid β;
P-Tau, phosphorylated tau 181; T-Tau, human total tau; CSF, cerebrospinal fluid;
SAH, subarachnoid hemorrhage; MRI, magnetic resonance imaging; WMH, whitematter hyperintensities; PVC, perivascular spaces; ADL, activities of daily living.

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11 C-labeled

Analyses of the Cerebrospinal Fluid (CSF)

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Neuroimages
Pittsburgh Compound B positron emission
tomography (11 C-PiB PET) was performed at the Gunma
University Hospital as described previously by the authors(12, 13). 18 F-THK5351 PET examinations were performed at
the Tokyo Metropolitan Institute of Gerontology using the
Discovery PET/CT 710 scanner (GE Healthcare, Milwaukee,
WI, USA). Emission data were acquired for 20 min, starting
40 min after intravenous injection of 18 F-THK5351 at a dose of
approximately 190 MBq (5.1 mCi). In total, 47 slice images, witha voxel size of 2 × 2 × 3.27 mm and a matrix size of 128 × 128,
were obtained. The data, collected in three-dimensional mode,
were reconstructed after correcting for decay, attenuation, andscatter. 18 F-THK5351 PET images were then normalized using
the cerebellum as a reference region; cerebellar uptake was set tothe value of one, as in the previously reported method (11, 14).

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Pathological Examinations

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Brain tissue samples were obtained when the patient underwent
surgery for hematoma. Immunohistochemical studies were
performed on using an anti-Aβ1-42 antibody and an anti-Aβ1-40
antibody (both IBL, Fujioka, Gunma, Japan).

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APOE Genotyping and Apolipoprotein E
Protein Analyses

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After obtaining informed consent for the genetic test of the
APOE gene, we purified genomic DNA from lymphocytes in the
peripheral blood of the patient. For the analysis of APOE allele
polymorphism, purified genomic DNA samples were examined
as previously described (12). The Apolipoprotein E protein was
confirmed by the LSI Medience Corporation (Tokyo, Japan).

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This report describes the case of a 79-year-old Japanese woman
diagnosed with AD, presenting with recurrent hemorrhages
despite the absence of vascular risk factors such as hypertension,
diabetes mellitus, hypercholesterolemia, coronary heart disease,and smoking. However, fatty liver and hypocholesterolemia wereobserved on medical examination. Although her mother had
died of a subarachnoid hemorrhage (SAH). At age 70 years,
the patient experienced a loss of recent memory, and sincethen, the amnesia has gradually progressed. At the age of 72
years, she was admitted to the Gunma University Hospital fora headache caused by a brain hemorrhage; she felt a little
headache and dullness without nausea, vomiting or loss of
consciousness, a state that were quite different from a typicalacute SAH. At the time of her first admission, cerebral magnetic
resonance imaging (MRI) revealed a hemorrhage in the leftparietal subcortical area (Figure 1A) and multiple regions ofcSS (Figures 1A,B). Although there were no white matter
hyperintensities, perivascular spaces, chronic ischemic infarcts,
or lacunae, there were multiple regions of cSS and a few lobar
microbleeds. On neuropsychological examinations, the scores
were 23/30 in the Mini-Mental State Examination (MMSE)(disorientation of time, delayed recall deficits, acalculia, andagraphia), 19/30 in the Montreal Cognitive Assessment (MoCA)(visual-spatial disorientation, clock drawing, delayed recall, anddisorientation of time), and 9/18 in the Frontal Assessment
Battery (FAB) (deficits of similarities, motor series, conflicting
instructions, and prehension behavior). The Instrumental
Activities of Daily Living scales (IADL) (18) was 4/8 (Abilities
required for shopping, food preparation, responsibility forown medications, and handling finances were disturbed). She
could do activities of daily living (ADL) to some extent but
needed some assistance from her family. According to gradualamnestic progression and executive dysfunction of ADL, she wasdiagnosed with mild dementia with AD at the first admission;
the findings were also suggestive of possible CAA according to
the modified Boston criteria (19). At age 73 years, the second
cerebral hemorrhage occurred. This time she was admitted to theGeriatric Research Institute and Hospital for a left hemiparalysis
with a lobar ICH at the right frontal lobe (Figure 1C). The

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FIGURE 1 | (A) Brain magnetic resonance imaging (MRI; 3T System, Skyra,
Siemens, Germany) at the first admission. Susceptibility weighted images(SWI) showing a cortical hemorrhage (arrowhead) at the left parietal lobe
cortex and cortical superficial siderosis (cSS) in the right parietal cortex (arrow).
(B) In the same MRI, cSS was additionally observed in the left and right frontal
cortices (arrows). (C) Brain computed tomography (CT) showing a second
lobar hemorrhage at the right frontal subcortical area concurrent with left
hemiparesis. (D) Brain CT showing a third lobar hemorrhage at the rightparieto-occipital subcortical region. (E) 11 C-PiB PET revealing widespread
11
C-PiB retentions in the cerebral cortices (arrowheads) and precuneus(arrows). (F) Electrophoresis revealing a 2/2 pattern in the apolipoprotein E
phenotype for this patient (PT). (G) Congophilic amyloid deposits shown inblood vessels of the resected brain tissue, as shown using polarizing
microscopy (Scale bar: 20 µm). (H,I) Aβ-reactive structures in blood vessel
walls identified by immunohistochemistry using anti-Aβ1-42 (H) andanti-Aβ1-40 (I) antibodies. Scale bars: 100 µm. (J) Laboratory serum resultsfrom the patient’s first admission. (K) Serum lipoprotein electrophoresis reveals
a broad-β pattern (bar), where intermediate-density lipoprotein (fraction 3) andvery-low-density lipoprotein (fraction 4) are aberrantly elevated.

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hematoma was removed by a neurosurgical operation. The scoreof MMSE at the time of the second admission had decreased to
18/30, implying a cognitive decline, when compared to the firstadmission. The score of the revised Hasegawa Dementia Scale(HDS-R) (20) was 17/30, suggesting moderate dementia. At age

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located in the pyramidal tract, were considered a result of aprevious right frontal lobar hemorrhage.
In addition, they were detected at the bilateral substantia nigraof the midbrain, medial areas of the thalamus, the amygdala, theglobus pallidum (black circles in Figure 2 T, C-I-O-U, D-J-P-V),
and the anterior caudate nuclei (black circles in Figure 2 D-J-P),
in which originally contained a high concentration of MAO-B.
Besides, 18 F-THK5351 retentions were visible along withprevious lobar hemorrhagic lesions in the left parietal cortex (atthe first hemorrhage) and the right frontal lobe (at the second
hemorrhage), and an asymptomatic old cortical SAH lesion in
the right parietal cortex (yellow circles in Figure 2 E-K-Q-W
and F-L-R-X).
Additional 18 F-THK5351 retentions were observed in
the bilateral medial temporal areas (blue circles in Figure 2
A-G-M-S and B-H-N-T), the bilateral areas of entorhinaland parahippocampal gyruses, and hippocampus (bluecircles in Figure 2 B-H-N-T, C-I-O), the bilateral areas ofparahippocampal, fusiform, and lingual gyruses (blue circles
in Figure 2 C-I-O), the left anterior frontal cortical areas (bluecircles in Figure 2 B-H-N and C-I-O), the bilateral posterior
areas in the temporal cortices (blue circles in Figure 2 C-I-O and
D-J-P), bilateral basis areas in the temporal lobes (blue circles in
Figure 2 S, T, U, V, and W), the left calcarine sulcus (blue circles
in Figure 2 D-J-P), the left precuneus area and the left frontal
cortex (blue circles in Figure 2 E-K-Q-W).

77 years, she was admitted for a headache for the third time; thistime she had left hemispatial neglect, caused by a lobar ICH in
the right parieto-occipital lobes (Figure 1D). Currently, she has
no additional hemorrhages but is still affected by dementia andleft hemiparalysis. The patient has not on any anti-platelet, anticoagulation, or antihyperlipidemic drugs for the clinical course.
At the time of the first hospitalization, 11 C-PiB PET revealed
widespread 11 C-PiB retentions in the cerebral cortices andprecuneus, which were compatible with those of AD (Figure 1E).
The APOE genotyping revealed a homozygous ε2 allele(not shown). The apolipoprotein E (ApoE) was confirmed as
a 2/2 protein by the western blot (Figure 1F). Congophilic
amyloid deposits in blood vessels were confirmed on polarizing
microscopy, undertaken using tissue resected during the second
hospitalization (Figure 1G). Aβ-immunoreactive structures inblood vessels walls were revealed by an anti-Aβ1-42 antibody(Figure 1H) and an anti-Aβ1-40 antibody (Figure 1I) on
immunohistochemical studies. Based on the pathological results,
this case was suggestive of “probable CAA” with supporting
pathological evidence according to the modified Boston criteria(19). Laboratory examination of the serum at the first admission
demonstrated hypolipidemia, defined by low serum levels of totalcholesterol (Total C), low-density lipoprotein cholesterol (LDL
C), apolipoprotein A-2 (ApoA-2), and apolipoprotein B (Apo B).
Chylomicrons were scarcely detectable (data not shown). The
serum levels of apolipoprotein C-2 (ApoC-2), apolipoprotein
E (ApoE), remnant-like lipoprotein (RLP), intermediate-density
lipoprotein (IDL), and very-low-density lipoprotein (VLDL)
were increased (Figure 1J). Furthermore, polyacrylamide gel(PAGE) electrophoresis of her serum revealed a broad-β band
which seemed to be mimicking type III dysliplidemia (Figure 1K)(BML Co. Tokyo, Japan). There was no detection of aberrantly
truncated apolipoprotein B on the western blot (data not shown).
In the case, the CSF level of Aβ1-42 was 144.49 pg/mL, which
was lower than that in non-dementia subjects (mean ± standard
deviation, 431.55 ± 103.46 pg/mL, n = 20) and comparable
to that in age-matched AD patients (183.32 ± 14.68 pg/mL,n = 25); the Aβ1-40 was 3037.22 pg/mL, which was lower
than that in non-dementia subjects (mean ± standard deviation,
5118.79 ± 1882.47 pg/mL, n = 20) and age-matched AD patients(5358.47 ± 2376.80 pg/mL, n = 25); the P-Tau level was 42.94
pg/mL, which was higher than that in non-dementia subjects(29.33 ± 11.36 pg/mL, n = 20) and comparable to those ofage-matched AD patients (75.40 ± 7.5 pg/mL, n = 25); andthe T-Tau level was 424.50 pg/mL, which was higher than thatin non-dementia subjects (143.55 ± 14.63 pg/mL, n = 20) andcomparable to that in age-matched AD patients (503.30 ± 53.03
pg/mL, n = 25). These findings of CSF biomarkers and 11 C-PiB
PET of the patient were compatible with those of AD. Postdischarge the second hemorrhage at the age of 73 years, the76-year-old patient was examined using 18 F-THK5351 PET in astable condition.
18 F-THK5351 retentions areas were observed at the right sideof the pyramidal tract at the pontine level, the cerebral peduncleof the midbrain, and the internal capsule (red circles in Figure 2
A-G-M-S, B-H-N-T, C-I-O-U, and D-J-P-V). The off-target 18 FTHK5351 retentions, binding to MAO-B derived from astrocytes,

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To our knowledge, this is the first report of an AD patient,homozygous for the APOE ε2 allele, presenting withrecurrent lobar hemorrhage, vascular Aβ amyloid deposits,
hypobetalipoproteinemia, and pathological 18 F-THK5351
findings. The vasculopathologic changes associated with the
APOE ε2 allele might have a crucial role in the severity andclinical course of lobar ICH, and the screening of patients with
ICH for the APOE ε2 may identify those at increased risk ofmortality and poor functional outcomes (21). The APOE ε2,
as well as the APOE ε4, allele was reported to be significantly
associated with lobar ICH and cSS (21, 22), and their presence
may predict the recurrence of anti-coagulation-associated ICH(23). Clinically, some patients with the APOE ε2 allele present
with hypocholesterolemia (24). In animal study, APOE ε2transgenic mice showed a decreased serum level of low-density
lipoprotein cholesterol (25). These biochemical aberrations
may lead to the fragility in the structure of cerebral bloodvessel walls, resulting in recurrent ICH in AD patients with the
APOE ε2 allele (26, 27). APOE ε2/ε3 carriers have larger total
volumes of white matter hyperintensities compared to APOE
ε4 carriers and a higher prevalence of microbleeds compared to
APOE ε3 homozygotes (28). Because the homozygous APOE ε2allele has an extremely low frequency (1), no prior publication
has confirmed a proven vascular amyloid pathology, in a
homozygous APOE ε2 allele carrying AD patient with recurrent
lobar ICH, using 18 F-THK5351 PET and 11 C-PiB PET. In vivo
study revealed that the levels of Aβ40 and Aβ42 in both soluble4June 2021 | Volume 12 | Article 645625

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FIGURE 2 | F-THK5351 imaging and MRI of the patient’s brain. (A–F) Susceptibility-weighted MRI. (G–L) F-THK5351. (M–R) F-THK 5351 on transverse MRI(T1-weighted images). (S–X) 18 F-THK 5351 on coronal MRI (T1-weighted images). 18 F-THK5351 retentions areas were observed in the pyramidal tract at the pontine
level, the cerebral peduncle of the midbrain, and the internal capsule, all at the right side (red circles: A-G-M-S, B-H-N-T, C-I-O-U, and D-J-P-V). The bilateralsubstantia nigra of the midbrain, the globus pallidum, the anterior caudate nuclei, amygdala, and the medial areas of thalamus (black circles: C-I-O-U and D-J-P-V)
were considered the off-target retentions of 18 F-THK5351 binding to MAO-B. They were also visible along the previous lobar hemorrhagic lesions in the right frontal
lobe and the left parietal cortex, as well as an asymptomatic old cortical SAH lesion in the right parietal cortex (yellow circles: E-K-Q-W and F-L-R-X). Additional
18
F-THK5351 retentions were observed in the bilateral medial temporal areas (blue circles: A-G-M-S and B-H-N-T), the left anterior frontal cortical areas (blue circles:
B-H-N-T and C-I-O-U), the posterior areas in bilateral temporal cortices, and the bilateral fusiform, lingual, and parahippocampal gyruses (blue circles: C-I-O and
D-J-P).

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in astrocytes, compared to other types of glial cells (41, 42). The
MAO-B concentration is also increased in reactive astrogliosis(43), which describes a spectrum of astrocytic changes that occur
in response to brain injury (44). On the other hand, Walleriandegeneration, the progressive anterograde demyelination anddisintegration of distal axons, develops following a brain injury
to neuronal soma or proximal axons (45). In the early stage ofbrain injury, axonal swelling, and breakdown of myelin sheathoccur, followed by loss of myelinated fibers and infiltrationof macrophages. In the later stage, the degenerated axons are
replaced by gliosis, which is primarily the proliferation ofastrocytes to form a scar. A previous case report demonstrated
ipsilateral myelin loss and gliosis in the pyramidal tract lesion
5 years after a left middle cerebral artery infarction (45). As forthe other tau PET tracers and CAA, two reports of Tau PET
have been published (46, 47). In the former report, paired helicalfilament (PHF) tau burden overlapped cerebral microbleeds
and/or cSS on 18 F-AV1451 PET, raising the possibility that thecharacteristic markers of vascular amyloid were associated withlocal production of PHF tau (46). In the latter report, the amyloid
deposition, evaluated by 18 F-florbetaben PET, was not different
in vicinity of cSS, but tau depositions were elevated in vicinity ofcSS-affected regions compared to non-cSS-affected brain regions.
Their case of probable CAA suggested that cSS may be associatedand insoluble fractions of the brain lysate were reduced in APOE
ε2, but not in APOE ε3 or APOE ε4 gene integrated in human
amyloid precursor protein/human presenilin 1 (APP/PS1)
double transgenic mice (29). The CAA patients had a distinctive
CSF biomarker profile, with significantly lower concentrationsof Aβ40 and Aβ42 than those of non-demented controls and AD
patients, which were found in APOE ε2, ε4, and ε3 genotypes(30–33). Lower CSF levels of Aβ1-40 and Aβ1-42 in an AD
patient with CAA carrying APOE ε2 homozygote had notbeen reported before this case. So far, pathological reports ofautopsy studies showed reduced neurofibrillary tangles (NFTs) inpostmortem AD brains of APOE ε2 carriers (2, 34, 35). Although
the mechanism underlying this reduction of NFTs is poorly
understood; as APOE ε2 was significantly correlated with tau
pathology only in Aβ positive AD patients but not with thosein Aβ negative individuals, the protective effect of APOE ε2against AD tau may be partially mediated through its effect on
Aβ deposition (36). It remains still controversial why APOE
ε2 may protect against tau pathology independently of Aβ in
AD (37–39). 18 F-THK5351 was initially developed to target
tau aggregates in NFTs (8, 9), however, 18 F-THK5351 has beenshown to strongly bind off-target to MAO-B in the basal ganglia
and thalamus (10), as well as the melanin-containing cells of thesubstantia nigra (40). 18 F-THK5351 is also highly concentrated

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CAA With APOE ε2 Homozygoteof parenchymal P-Tau to flow out into the lumen of vessels
or less amount of tau phosphorylation in CAA than in AD
patients may have led to lower P-Tau levels in CSF. On the other
hand, Aβ 40 is thought to deposit in the blood vessels affectedby CAA, rather than flowing into the lumen of vessels (59).
Although the fact that the pathophysiologic mechanisms are
not completely understood, recent studies show that APOE2
allele is strongly associated with cSS and increased frequency
and severity of lobar ICH (22). Furthermore, while APOE ε2
promotes so-called CAA-related vasculopathic changes (vessel
cracking, detachment, and delamination of the outermost layerof the tunica media, and fibrinoid necrosis), this can lead tovessel rupture (5, 22), another evidence of increased ICH riskin APOE ε2 carriers was recently reported, with or without AD(60). Hypobetalipoproteinemia may not be a direct cause for
the ICH in this case, but might be an additional contributing
factor, because hypocholesterolemia was reported to be a risk
factor of ICH (61–63). This AD case carried the homozygous
APOE ε2 allele and severe hypocholesterolemia, which may haveexacerbated vascular changes in the CAA, thereby leading tomore severe CAA, multiple cSS, and recurrent lobar ICH. The
mechanism underlying the risk for ICH because of an APOE ε2genotype is still unclear and perhaps complex (60), which shouldbe elucidated hereafter. In future, a large-scale investigationof cholesterol levels in AD patients with a homozygous APOE
ε2 allele will be required to define its clinical significance andetiologies. This AD case with multiple cSS regions and lobar
hemorrhages is also worth reporting; it demonstrates that
18 F-THK5351 retentions reflected MAO-B, reactive astrogliosis,
and Wallerian degeneration of the pyramidal tract due to CAA
related lobar ICH, and possibly tau accumulation.
Finally, the therapies targeting APOE ε2 and APOE2for AD have been extensively developed; a viral-mediated
APOE ε2 overexpression, converting APOE ε4 to APOE ε2,
and plasma APOE-based therapy were described detail in a
recent review (64). Although these trials have some effect,
unfortunately, they have not been effective, regardless of theirsuccessful experimental results. Besides severe CAA pathology,
this case presented with hypobetalipoproteinemia, however,
we could not elucidate the clear molecular and pathologicalrelationships between them. To validate the mechanisms ofeffective therapies targeting APOE2/APOE ε2, more evidence
from humans and animal models is required. Further, more
AD patients carrying the APOE ε2 allele are required toanalyze pathological and biochemical findings. Furthermore,
improved understanding of the roles of APOE2/APOE ε2
in other diseases, CAA/Stroke and different proteinopathies,
including tau, TDP-43, and α-synuclein pathologies, may allow
a comprehensive assessment of the safety of APOE2 targeted
therapeutics for AD.
A limitation of this study is that it includes only one ADpatient carrying a homozygous APOE ε2 allele with recurrent
hemorrhages and multiple cSS regions. This impedes statisticalanalyses for cholesterol levels in comparison to other APOE
alleles or healthy control groups. The other limitation is that
we could not confirm how histopathological association between
18 F-THK5351 retention and astrogliosis pathology since wewith an elevation in local tau accumulation but not with anincrease in fibrillary amyloid deposition (47). 18 F-THK5351
PET has been reported to strongly bind to MAO-B derived
from astrocytes and possibly tau, probably reflecting astrogliosis
related CAA hematoma and cSS, also possibly tau accumulationin AD pathology (48). In our case, 3 year have passed sincethe 18 F-THK5351 PET examination for the previous ICH in
the right frontal lobe presenting as contralateral hemiparalysis.
In this patient, Wallerian degeneration may have developedin the ipsilateral pyramidal tract followed by astrogliosis, andthe marker 18 F-THK5351 PET, targeting MAO-B containing
tissues, may have identified this Wallerian degeneration. The
18 F-THK5351 is now recognized as a dual-purpose compoundthat binds to both MAO-B and tau aggregates, thus tau isoforms
might have accumulated in the damaged fasciculus. MAO-B
is highly concentrated in astrocytes and serotonergic andhistaminergic neurons (43), astrocytes proliferate in response toinflammation caused by brain injury (44). Therefore, regionalchanges in MAO-B concentration can be an index of astrogliosis
and be detected on 18 F-THK5351 PET imaging, although
tau accumulation may co-exist in the AD brain (48). Thesefindings suggest that 18 F-THK5351 accumulates in lesions
where astrogliosis occurs and that 18 F-THK5351 PET can be
an imaging modality to visualize and quantify astrogliosis, as
recently demonstrated in patients with a cerebral infarction
or neurological disorders (49–51). In our case, 18 F-THK5351
retentions were observed in some, but not all, regions with cSS,
presumably because this effect might depend on severity of cSS
and the degree of astrogliosis that is correlated with the extent
of hemosiderin deposition (52). 18 F-THK5351 retentions wereobserved only in some areas rather than all peripheral regions at
the lobar hemorrhage in this patient. Several cSS lesions reflected
18 F-THK5351 PET retentions, however, the other cSS lesions
did not reflect, which might probably depend upon severity ofcSS or neuroinflammation of CAA. It seems that old cSS showed
less or no THK5351 retention [such as in bilateral prefrontal
lobes cortices/subcortices (Figure 2 D-J-P and E-K-Q)], while
the recent lesions of hematoma or siderosis showed THK5351
retentions [such as in left parietal cortical area (hematoma at
the first admission) and right frontal cortico-subcortical areas(hematoma at the second admission)]. This case signifies thatboth AD and CAA pathologies could co-occur and contributeto cognitive decline (53), indicating that CAA, especially, cSS
strongly increases the risk of hemorrhages, even when theclinical changes are gradual. We found that CSF Aβ1-40 of thispatient was lower than those of AD patients and non-demented
subjects, as, in the recent publication, the levels of CSF Aβ1-40
in AD with disseminated cSS were lower than that in AD
patients with focal cSS and those without cSS (54, 55). This result
is thought be in accordance with neuropathological reports
demonstrating deposition of Aβ40 within the vessel walls of
CAA patients (56). CSF P-Tau levels, in this case, were notsignificantly higher than those in AD patients, because CSF
P-Tau levels in AD patients with CAA pathology have beenreported to be either slightly elevated or within the normal levels(55, 57, 58). This could be because of the change in permeabilityof the cerebral blood vessels due to CAA; decreased ability

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CS, MS, YS, MA, and MTakat collected the clinical information.
SI, MK, and MTakah performed and evaluated the western blotof ApoB protein, also discussed the hypobetalipoproteinemia inthis patient. ST and IN performed the neurosurgical operation toremoval the hematoma at the second admission. MI performed
the clinical data analysis and evaluated the specificities andneurological significances. All authors contributed to the article
and approved the submitted version.

histologically observed only a part of the brain. To better
understand the pathological mechanism, a detailed investigation
using neuroradiological, biochemical, genetical, and pathologicalmethods is required in CAA and AD patients.

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DATA AVAILABILITY STATEMENT

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The raw data supporting the conclusions of this manuscript will
be made available by the authors, without undue reservation, to
any qualified researcher.

FUNDING

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This study was supported by a Grant-in-Aid for Scientific
Research (C) (MI: 18K07491, MTakah: 20K08870, TH: 19K08220,
YTs: 18K07627, HY: 19K07411, KI: 17K09813, YI: 19K07813),(B) (SI: 19H03712, MK: 20H03671) from the Ministry of
Education, Culture, Sports, Science and Technology of Japan,
MI also received a Donation of love from the JOMO-SHINBUN
newspaper company (Maebashi, Gunma, Japan).

The studies involving human participants were reviewed andapproved by the ethics committee of the Gunma University
Hospital. The patients/participants provided their written
informed consent to participate in this study. Written informed
consent was obtained from the individual(s) for the publicationof any potentially identifiable images or data included inthis article. Written consent to publish the clinical information
was obtained from the patient’s family.

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ACKNOWLEDGMENTS

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ETHICS STATEMENT

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The authors thank the patient and his family for providing
clinical data and allowing the publication of this case report.
We especially thank Dr. Hirofumi Kawakami, an Advanced
Application Specialist who belongs to Headquarter of Academy,
GE Healthcare Japan, and Yoshikatsu Tsunemi (Inspection
Specialist), Department of Clinical Laboratory Medicine,
Geriatrics Research Institute and Hospital, for their excellent
technical assistance. We thank Dr. Mikio Shoji for academic
discussion about this manuscript. We would like to thank Editage(www.editage.com) for American English language editing.

AUTHOR CONTRIBUTIONS
MI collected the clinical data, interpreted the data, and wrote
the manuscript. ET analyzed genomic DNA from the patient’s
blood samples and CSF biomarkers from the patient. KO, KS, andHY performed pathological examinations and evaluation of theresults. TH and YTs performed the neuroimaging examinationfor 11 C PiB-PET, and KI performed and evaluated theneuroimaging examination for 18 F THK5351-PET. MF, NF, YTa,

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REFERENCES

vasculopathic changes: collaborative meta-analysis. J Neurol Neurosurg
Psychiatry. (2014) 85:300–5. doi: 10.1136/jnnp-2013-306485
8. Harada R, Okamura N, Furumoto S, Furukawa K, Ishiki A, Tomita
N, et al. 18F-THK5351: a novel PET radiotracer for imaging
neurofibrillary pathology in Alzheimer disease. J Nucl Med. (2016)57:208–14. doi: 10.2967/jnumed.115.164848
9. Tago T, Furumoto S, Okamura N, Harada R, Adachi H, Ishikawa Y,
et al. Structure-activity relationship of 2-arylquinolines as PET imaging
tracers for tau pathology in Alzheimer disease. J Nucl Med. (2016) 57:608–
14. doi: 10.2967/jnumed.115.166652
10. Ng KP, Pascoal TA, Mathotaarachchi S, Therriault J, Kang MS, ShinM, et al. Monoamine oxidase B inhibitor, selegiline, reduces 18 FTHK5351 uptake in the human brain. Alzheimers Res Ther. (2017)
9:25. doi: 10.1186/s13195-017-0253-y
11. Ishibashi K, Kameyama M, Tago T, Toyohara J, Ishii K. Potential use of
18F-THK5351 PET to identify wallerian degeneration of the pyramidal
tract caused by cerebral infarction. Clin Nucl Med. (2017) 42:e523–
4. doi: 10.1097/RLU.0000000000001868
12. Ikeda M, Tashiro Y, Takai E, Kurose S, Fugami N, Tsuda K, et al. CSF levelsof Aβ1-38/Aβ1-40/Aβ1-42 and 11 C-PiB PET studies in three clinical variantsof primary progressive aphasia and Alzheimer’s disease. Amyloid. (2014)
21:238–45. doi: 10.3109/13506129.2014.949231
13. Kasahara H, Ikeda M, Nagashima K, Fujita Y, Makioka K, Tsukagoshi
S, et al. Deep white matter lesions are associated with early recognitionof dementia in Alzheimer’s disease. J Alzheimers Dis. (2019) 68:797–
808. doi: 10.3233/JAD-180939

720
721
722
723
724
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726
727
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730
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736
737
738
739
740

1. Reiman EM, Arboleda-Velasquez JF, Quiroz YT, Huentelman MJ,
Beach TG, Caselli RJ, et al. Exceptionally low likelihood of Alzheimer’s
dementia in APOE2 homozygotes from a 5,000-person neuropathologicalstudy. Nat Commun. (2020) 11:667. doi: 10.1038/s41467-019-1
4279-8
2. Serrano-Pozo A, Qian J, Monsell S, Betensky R, Hyman B. APOEε2 isassociated with milder clinical and pathological Alzheimer’s disease. Ann
Neurol. (2015) 77:917–29. doi: 10.1002/ana.24369
3. Corder EH, Saunders AM, Strittmatter WJ, Schmechel DE, Gaskell PC,
Small G, et al. Gene dose of apolipoprotein E type 4 allele and therisk of Alzheimer’s disease in late onset families. Science. (1993) 261:921–
3. doi: 10.1126/science.8346443
4. Nicoll JA, Burnett C, Love S, Graham DI, Dewar D, Ironside JW, et al. Highfrequency of apolipoprotein E ε2 allele in hemorrhage due to cerebral amyloid
angiopathy. Ann Neurol. (1997) 41:716–21. doi: 10.1002/ana.410410607
5. Greenberg SM, Vonsattel JP, Segal AZ, Chiu RI, Clatworthy AE, Liao A, etal. Association of apolipoprotein E ε2 and vasculopathy in cerebral amyloid
angiopathy. Neurology. (1998) 50:961–5. doi: 10.1212/wnl.50.4.961
6. O’Donnell HC, Rosand J, Knudsen KA, Furie KL, Segal AZ,
Chiu RI, et al. Apolipoprotein E genotype and the risk ofrecurrent lobar intracerebral hemorrhage. N Eng J Med. (2000)
342:240–5. doi: 10.1056/NEJM200001273420403
7. Rannikmäe K, Kalaria RN, Greenberg SM, Chui HC, Schmitt FA,
Samarasekera N, et al. APOE associations with severe CAA-associated

776
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854

CAA With APOE ε2 Homozygote

31. Banerjee G, Ambler G, Keshavan A, Paterson RW, Foiani MS, Toombs J, et al.
Cerebrospinal fluid biomarkers in cerebral amyloid angiopathy. J Alzheimers
Dis. (2020) 74:1189–1201. doi: 10.3233/JAD-191254
32. Charidimou A, Imaizumi T, Moulin S, Biffi A, Samarasekera N,
Yakushiji Y, et al. Brain hemorrhage recurrence, small vessel disease
type, and cerebral microbleeds: a meta-analysis. Neurology. (2017)89:820–29. doi: 10.1212/WNL.0000000000004259
33. Shams S, Martola J, Charidimou A, Cavallin L, Granberg T,
Shams M, et al. Cortical superficial siderosis: prevalence andbiomarker profile in a memory clinic population. Neurology. (2016)
87:1110–7. doi: 10.1212/WNL.0000000000003088
34. Bennett DA, De Jager PL, Leurgans SE, Schneider JA. Neuropathologic
intermediate phenotypes enhance association to Alzheimer susceptibility
alleles. Neurology. (2009) 72:1495–503. doi: 10.1212/WNL.0b013e3181a2e87d
35. Nagy Z, Esiri MM, Jobst KA, Johnston C, Litchfield S, Sim E, et al.
Influence of the apolipoprotein E genotype on amyloid deposition andneurofibrillary tangle formation in Alzheimer’s disease. Neuroscience. (1995)
69:757–61. doi: 10.1016/0306-4522(95)00331-c
36. Farfel JM, Yu L, De Jager PL, Schneider JA, Bennett DA. Association of APOE
with tau-tangle pathology with and without β-amyloid. Neurobiol Aging.
(2016) 37:19–25. doi: 10.1016/j.neurobiolaging.2015.09.011
37. Goldberg TE, Huey ED, Devanand DP. Association of APOE ε2 genotype
with Alzheimer’s and non-Alzheimer’s neurodegenerative pathologies. Nat
Commun. (2020) 11:4727. doi: 10.1038/s41467-020-18198-x
38. Zhao N, Liu CC, Van Ingelgom AJ, Linares C, Kurti A, Knight JA, et al.
APOE ε2 is associated with increased tau pathology in primary tauopathy.
Nat Commun. (2018) 9:4388. doi: 10.1038/s41467-018-06783-0
39. Ghebremedhin E, Schultz C, Botez G, Rüb U, Sassin I, Braak E, et al.
Argyrophilic grain disease is associated with apolipoprotein E epsilon 2 allele.
Acta Neuropathol. (1998) 96:222–4. doi: 10.1007/s004010050886
40. Tago T, Toyohara J, Harada R, Furumoto S, Okamura N, Kudo Y, et
al. Characterization of the binding of tau imaging ligands to melanincontaining cells: putative off-target-binding site. Ann Nucl Med. (2019)33:375–82. doi: 10.1007/s12149-019-01344-x
41. Gulyás B, Pavlova E, Kása P, Gulya K, Bakota L, Várszegi S, et al. Activated
MAO-B in the brain of Alzheimer patients, demonstrated by [11 C]-L-deprenyl
using whole hemisphere autoradiography. Neurochem Int. (2011) 58:60–
8. doi: 10.1016/j.neuint.2010.10.013
42. Levitt P, Pintar JE, Breakefield XO. Immunocytochemical demonstration of
monoamine oxidase B in brain astrocytes and serotonergic neurons. Proc Natl
Acad Sci USA. (1982) 79:6385–9. doi: 10.1073/pnas.79.20.6385
43. Saura J, Luque JM, Cesura AM, Da Prada M, Chan-Palay V, Huber G, etal. Increased monoamine oxidase B activity in plaque-associated astrocytesof Alzheimer brains revealed by quantitative enzyme radioautography.
Neuroscience. (1994) 62:15–30. doi: 10.1016/0306-4522(94)90311-5
44. Sofroniew MV. Molecular dissection of reactive astrogliosis and glial scarformation. Trends Neurosci. (2009) 32:638–47. doi: 10.1016/j.tins.2009.08.002
45. Jones KC, Hawkins C, Armstrong D, Deveber G, Macgregor D, MoharirM, et al. Association between radiographic Wallerian degeneration andneuropathological changes post childhood stroke. Dev Med Child Neurol.
(2013) 55:173–7. doi: 10.1111/dmcn.12010
46. Kim HJ, Cho H, Werring DJ, Jang YK, Kim YJ, Lee JS, et al. 18F-AV-1451
PET imaging in three patients with probable cerebral amyloid angiopathy. J
Alzheimers Dis. (2017) 57:711–6. doi: 10.3233/JAD-161139
47. Brendel M, Catak C, Beyer L, Linn J, Wahl H, Janowitz D, et al. Colocalizationof tau but not β-amyloid with cortical superficial siderosis in a case withprobable CAA. Case Rep Neurol. (2020) 12:232-7. doi: 10.1159/000506765
48. Harada R, Ishiki A, Kai H, Sato N, Furukawa K, Furumoto S, etal. Correlations of 18 F-THK5351 PET with postmortem burden of tau
and astrogliosis in Alzheimer disease. J Nucl Med. (2018) 59:671–
4. doi: 10.2967/jnumed.117.197426
49. Ishibashi K, Miura Y, Hirata K, Toyohara J, Ishii K. 18 F-THK5351 PET
can identify astrogliosis in multiple sclerosis plaques. Clin Nucl Med. (2020)
45:e98–100. doi: 10.1097/RLU.0000000000002751
50. Schönecker S, Brendel M, Palleis C, Beyer L, Höglinger GU,
Schuh E, et al. PET imaging of astrogliosis and tau facilitates
diagnosis of Parkinsonian syndromes. Front Aging Neurosci. (2019)11:249. doi: 10.3389/fnagi.2019.00249

14. Ishibashi K, Miura Y, Hirata K, Toyohara J, Ishii. Relationship between
the temporal course of astrogliosis and symptom improvementin cerebral infarction: report of a case monitored using 18 FTHK5351 positron emission tomography. BMC Med Imaging. (2020)
20:81. doi: 10.1186/s12880-020-00481-4
15. Vanmechelen E, Vanderstichele H, Davidsson P, Van Kerschaver E, Van Der
Perre B, Sjögren M, Andreasen N, et al. Quantification of tau phosphorylated
at threonine 181 in human cerebrospinal fluid: a sandwich ELISA with asynthetic phosphopeptide for standardization. Neurosci Lett. (2000) 285:49–
52. doi: 10.1016/s0304-3940(00)01036-3
16. Andreasen N, Minthon L, Davidsson P, Vanmechelen E, Vanderstichele H,
Winblad B, et al. Evaluation of CSF-tau and CSF-Aβ42 as diagnostic markers
for Alzheimer’s disease in clinical practice. Arch Neurol. (2001) 58:373–
9. doi: 10.1001/archneur.58.3.373
17. Ikeda M, Kuwabara T, Takai E, Kasahara H, Furuta M, Sekine A,
et al. Increased neurofilament light chain and YKL-40 CSF Levels
in one Japanese IBMPFD patient with VCP R155C mutation: aclinical case report with CSF biomarker analyses. Front Neurol. (2020)
11:757. doi: 10.3389/fneur.2020.00757
18. Lawton MP, Brody EM. Assessment of older people: self-maintaining andinstrumental activities of daily living. Gerontologist. (1969) 9:179–86.
19. Linn J, Halpin A, Demaerel P, Ruhland J, Giese AD, Dichgans
M, et al. Prevalence of superficial siderosis in patients withcerebralamyloidangiopathy.
Neurology.
(2010)
74:1346–
50. doi: 10.1212/WNL.0b013e3181dad605
20. Katoh S, Shimogaki H, Onodera A, Ueda H, Oikawa K, Ikeda K, et al.
Development of the revised version of Hasegawa’s Dementia-Scale (HDS-R).
Jpn J Geriatr Psychiatry. (1991) 2:1339–47. (Japanese)
21. Biffi A, Anderson CD, Jagiella JM, Schmidt H, Kissela B, Hansen BM, et al.
APOE genotype and extent of bleeding and outcome in lobar intracerebral
haemorrhage: a genetic association study. Lancet Neurol. (2011) 10:702–
9. doi: 10.1016/S1474-4422(11)70148-X
22. CharidimouA,
Zonneveld
HI,
Shams
S,
Kantarci
K,
Shoamanesh A, Hilal S, et al. APOE and cortical superficial
siderosis in CAA: meta-analysis and potential mechanisms.
Neurology.
(2019)
93:e358–71.
doi:
10.1212/WNL.000000000000
7818
23. Biffi A, Urday S, Kubiszewski P, Gilkerson L, Sekar P, Rodriguez-TorresA, et al. Combining imaging and genetics to predict recurrence ofanticoagulation-associated intracerebral hemorrhage. Stroke. (2020) 51:2153–
60. doi: 10.1161/STROKEAHA.120.028310
24. Mahley RW, Huang Y, Rall SC. Pathogenesis of type III hyperlipoproteinemia(dysbetalipoproteinemia): questions, quandaries, and paradoxes. J Lipid Res.
(1999) 40:1933–49.
25. Huang Y, Liu XQ, Rall SC, Mahley RW. Apolipoprotein E2 reduces the
low-density lipoprotein level in transgenic mice by impairing lipoprotein
lipase-mediated lipolysis of triglyceride-rich lipoproteins. J Biol Chem. (1998)
273:17483–90. doi: 10.1074/jbc.273.28.17483
26. McCarron MO, Nicoll JA, Stewart J, Ironside JW, Mann DM, Love S, et al.
The apolipoprotein E epsilon2 allele and the pathological features in cerebral
amyloid angiopathy-related hemorrhage. J Neuropathol Exp Neurol. (1999)
58:711–8. doi: 10.1097/00005072-199907000-00005
27. Nelson PT, Pious NM, Jicha GA, Wilcock DM, Fardo DW, Estus
S, et al. APOE-ε2 and APOE-ε4 correlate with increased amyloid
accumulation in cerebral vasculature. J Neuropathol Exp Neurol. (2013)
72:708–15. doi: 10.1097/NEN.0b013e31829a25b9
28. Groot C, Sudre CH, Barkhof F, Teunissen CE, van Berckel BN, Seo
SW, et al. Clinical phenotype, atrophy, and small vessel disease in
APOEε2 carriers with Alzheimer disease. Neurology. (2018) 91:e1851–
9. doi: 10.1212/WNL.0000000000006503
29. Hudry E, Dashkoff J, Roe AD, Takeda S, Koffie RM, Hashimoto T, et al.
Gene transfer of human ApoE isoforms results in differential modulation ofamyloid deposition and neurotoxicity in mouse brain. Sci Transl Med. (2013)
5: 212ra161. doi: 10.1126/scitranslmed.3007000
30. Costa AS, Pinho J, Kučikiene D, Reich A, Schulz JB, Reetz K. Cerebral
amyloid angiopathy in amyloid-positive patients from a memory clinic
cohort. J Alzheimers Dis. (2021) 79:1661–72. doi: 10.3233/JAD-20
1218

856
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877
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879
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885
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887
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920
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CAA With APOE ε2 Homozygote

51. Takami Y, Yamamoto Y, Norikane T, Mitamura K, Hatakeyama T, Nishiyama
Y. 18 F-THK5351 PET can identify lesions of acute traumatic brain
injury. Clin Nucl Med. (2020) 45:e491–2. doi: 10.1097/RLU.00000000000
03165
52. Charidimou A, Perosa V, Frosch MP, Scherlek AA, Greenberg SM, van Veluw
SJ. Neuropathological correlates of cortical superficial siderosis in cerebral
amyloid angiopathy. Brain. (2020) 143:3343–51. doi: 10.1093/brain/awaa266
53. Boyle PA, Yu L, Wilson RS, Leurgans SE, Schneider JA, Bennett DA. Personspecific contribution of neuropathologies to cognitive loss in old age. Ann
Neurol. (2018) 83:74–83. doi: 10.1002/ana.25123
54. Martínez-Lizana E, Carmona-Iragui M, Alcolea D, Gómez-Choco M,
Vilaplana E, Sánchez-Saudinós MB, et al. Cerebral amyloid angiopathyrelated atraumatic convexal subarachnoid hemorrhage: an ARIA before thetsunami. J Cereb Blood Flow Metab. (2015) 35:710–7. doi: 10.1038/jcbfm.2
015.2555. Catak C, Zedde M, Malik R, Janowitz D, Soric V, Seegerer A, et al. Decreased
CSF levels of ß-amyloid in patients with cortical superficial siderosis. Front
Neurol. (2019) 10:439. doi: 10.3389/fneur.2019.00439
56. Vinters HV. Emerging concepts in Alzheimer’s disease. Annu Rev Pathol.
(2015) 10:291–319. doi: 10.1146/annurev-pathol-020712-163927
57. van Etten ES, Verbeek MM, van der Grond J, Zielman R, vanRooden S, van Zwet EW, et al. β-Amyloid in CSF: biomarker
for preclinical cerebral amyloid angiopathy. Neurology. (2017)
88:169–76. doi: 10.1212/WNL.0000000000003486
58. Shams S, Granberg T, Martola J, Li X, Shams M, Fereshtehnejad SM, et al.
Cerebrospinal fluid profiles with increasing number of cerebral microbleeds
in a continuum of cognitive impairment. J Cereb Blood Flow Metab. (2016)36:621–8. doi: 10.1177/0271678X15606141
59. Viswanathan A, Greenberg SM. Cerebral amyloid angiopathy
in the elderly. Ann Neurol. (2011) 70:871–80. doi: 10.1002/ana.
22516

60. Goldberg TE, Huey ED, Devanand DP Associations of APOE e2 genotype
with cerebrovascular pathology: a postmortem study of 1275 brains. J Neurol
Neurosurg Psychiatry. (2021) 92:7–11. doi: 10.1136/jnnp-2020-323746
61. Yilmaz P, Ikram MA, Ikram MK, Niessen WJ, Viswanathan A, Charidimou
A, et al. Application of an imaging-based sum score for cerebral amyloid
angiopathy to the general population: risk of major neurological diseases andmortality. Front Neurol. (2019) 10:1276. doi: 10.3389/fneur.2019.01276
62. An SJ, Kim TJ, Yoon BW. Epidemiology, risk factors, and clinicalfeatures of intracerebral hemorrhage: an update. J Stroke. (2017) 19:3–
10. doi: 10.5853/jos.2016.00864
63. Mustanoja S, Strbian D, Putaala J, Meretoja A, Curtze S,
Haapaniemi E, et al. Association of prestroke statin use and lipid
levels with outcome of intracerebral hemorrhage. Stroke. (2013)
44:2330–2. doi: 10.1161/STROKEAHA.113.001829
64. Li Z, Shue F, Zhao N, Shinohara M, Bu G. APOE2: protective mechanism andtherapeutic implications for Alzheimer’s disease. Mol Neurodegener. (2020)
15:63. doi: 10.1186/s13024-020-00413-4

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Conflict of Interest: The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could be construed as apotential conflict of interest.

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Copyright © 2021 Ikeda, Okamoto, Suzuki, Takai, Kasahara, Furuta, Furuta,
Tashiro, Shimizu, Takatama, Naito, Sato, Sakai, Takahashi, Amari, Takatama,
Higuchi, Tsushima, Yokoo, Kurabayashi, Ishibashi, Ishii and Ikeda. This is an openaccess article distributed under the terms of the Creative Commons Attribution
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provided the original author(s) and the copyright owner(s) are credited and that theoriginal publication in this journal is cited, in accordance with accepted academic
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with these terms.

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