第30回 抗悪性腫瘍薬開発フォーラム
世界・アジアをリードする日本からの臨床開発〜3密（産官学＆知行果）を目指して〜』
第二部 世界におけるアジアのリーズナブルなポジショニング

ゲノムを含むデータ連携について～研究者の目線から～
スクラム・ジャパンの歩みからの展望
From ‘MAY BE’ to ‘MUST BE’

吉野 孝之

国立がん研究センター東病院 消化管内科 科長

Feb 13th, 2021

Agenda•PAST to Present: Achievement of the SCRUM-Japan Tissue NGS* Screening (SCR) and stratified clinical trials
 Data Base (DB) Liquid Bx SCR and stratified clinical trials
 International Collaboration•

TODAY: Ongoing innovative projects
 Liquid Bx & Microbiome (MB) Artificial intelligence /Machine Learning (AI/ML)
 CIRCULATE-Japan (C-J) and Early Detection (COSMOS)•TOMORROW: Translating Multi-Omics into Clinical Utility*Next Generation Sequencing

SCRUM-Japan Project Since 2015

LC-SCRUM
Participating
Institutions

GI*-SCREEN

Genomic &clinical data

Participating
fees

LC*-SCRUM

AstellasAstra
ZenecaDaiichiSankyo

BristolMyers

Taiho

Annotated report

SCRUM-Japan Common Data Center & Administrative Office

Eisai

Takeda

Kyowa
Kirin

Ono

ChugaiPfizerMSD

Novartis

Merck
SeronoEli Lilly

Submission of Clinical Samples

Analysis
Data

Submission of Clinical Samples

Annotated reportComprehensive NGS (Next Generation Sequencing) test by central vendor

Janssen

Boehringer
Ingelheim

GI-SCREEN
Participating
Institutions

Yoshino T. ESMO 20193

Accomplishment of SCRUM-Japan so far
As of July 2020

On-time clinico-pathologic-genome data sharing
with academia & Pharmas
17,000+NGS* test

270+
hospitals

17
Pharmas600+
Patients
enrolledclinical trials

50+
clinicaltrials

9 Approved
Drugs &
9 CDXs*

Co-PIs, Takayuki Yoshino (GI-SCREEN) and Koichi Goto (LC-SCRUM)
4*Notes;
NGS, Next Generation
Sequencing: CDX, companion
diagnostic test

Update on Yoshino T, et all. Oral presentation at Google Cloud Next ’19 in San Francisco
https://www.youtube.com/watch?v=iO4x1aJw_Jc

Comparison of key elements
between NCI-MATCH and SCRUM-Japan
NCI-MATCH（US）

SCRUM-Japan

Public grant

Public grant/industry budget

Starting year

2015

2015

NGS panelcommercial（OCP/OCA）

commercial（OCP/OCA）

Study type

IIT

IIT+SIT

ClosedOpen platform

No. of studies

40

56 (incl. 20 IIT)

Target tumors

All solid tumors

All solid tumors

No. of enrollment(11/2018)

6,000

17,000+Rate for study enrollment

5%3%

No. of completed studies

1129

No. of agents approved

1

9

Clinical/genome data sharingnone

66 acad. ctrs. and 17
industries

Liquid biopsy screening

Not yet

Ongoing

Regulatory-grade registry

Not yet

Ongoing

Microbiome

Not yet

Ongoing

funding

Study enrollment from the screening
platform

5

SCRUM-Japan DB

On-time clinico-pathologic-genome data sharing;Number of accesses/number of data downloads
企業ユーザーPharma

14998

16000
14000

施設ユーザーAcademia

20000

17820

18000

14998

17820

16000

12000

14000
10000
8000
6000

12000

6345

4929

6345

4000

392

407

392

8000

3724

4929

2000

3724

407

6000

200

999
999

200

0
2017年度

2017

7681

10000

2018年度

20182019年度

2019

累計

Total

アクセス数

ダウンロード数

ユーザーアカウント内訳
企業ユーザー（DL権限あり）

70

施設ユーザー（DL権限あり）

39

施設ユーザー（DL権限なし）

101

4000

5986

4153

7681

5986

55

2000

281

55

281

4153

74

410

74

410

0

2017

2017年度

20182018年度

20192019年度

Total
累計

アクセス数

ダウンロード数

2017年度：2017年6月～2018年3月
2018年度：2018年4月～2019年3月
2019年度：2019年4月～2020年2月※ 2019年4月サーバーメンテナンスによるアクセス中断

No. Assesses
No. DLs

6

SCRUM-Japan DB

Utilization of Real World Data

Comparison of Endpoints in Each Sub-study with Data in SCRUM Japan Registry
SCRUM Japan Registry
Trastuzumab+ Pertuzumab(TRIUMPH)

HER2 positivemCRC

TMB-High
Cohort (GI cancers)

Eribulin(TMB-H Basket)

TMB-H
GI Cancers

BRAF non-V600E MT
Cohort (mCRC)

Encorafenib+ Binimetinib+ Cetuximab(BIG BANG)

BRAF non-V600E
mCRC

MET amplification
Cohort (mCRC)

Cabozantinib+ Panitumumab(MET-BEIGE)

MET-amplified
mCRC

FGFR alteration
Cohort (Solid tumor)

TAS-120(TiFFFANY)

FGFR-alteredsolid tumor

……

……

…

HER2 positive
Cohort (mCRC)

7

Nationwide Genome
Screening Project
SCRUM-Japan GI-SCREENGOZILA
RWD
DB

Collection of
RWD

Agenda•PAST to Present: Achievement of the SCRUM-Japan Tissue NGS* Screening (SCR) and stratified clinical trials
 Data Base (DB) Liquid Bx SCR and stratified clinical trials
 International Collaboration•

TODAY: Ongoing innovative projects
 Liquid Bx & Microbiome (MB) Artificial intelligence /Machine Learning (AI/ML)
 CIRCULATE-Japan (C-J) and Early Detection (COSMOS)•TOMORROW: Translating Multi-Omics into Clinical Utility*Next Generation Sequencing

Liquid Bx SCR

SCRUM-Japan ctDNA Screening Platform
As of Dec 2020

N= 3572
Stage IV GI Cancers mainly
Since Jan 2018

N= 1237
Stage IV solid tumors
Since July 20199

Liquid Bx SCR

Ongoing Clinical Trials
Nationwide Genome
Screening Project

Tumor-specificapproach

SCRUM-Japan GI-SCREEN

CRC

ctDNA解析(Guardant360)

Non-CRC GI
Tumor-agnostic
approach

Non-GI Solid

ctDNA解析(Guardant360)

N=5,000

HER2 amplification

Trastuzumab + Pertuzumab(TRIUMPH)

BRAF V600E MT

Eribulin (BRAVERY)

BRAF non-V600E MT

Encorafenib + Binimetinib+ Cetuximab(BIG BANG)

MET amplification

Cabozantinib + Panitumumab(METBEIGE)

RAS WT

Re-challenge with anti-EGFR
mAb (PURSUIT)HER2 amplification

DS-8201a (HERB)bTMB-High

Nivolumab (bTMB-H basket)

Any FGFR alteration

TAS-120 (TiFFFANY)HER2 amplification

DS-8201a (HERALD)ROS1 fusion

Brigatinib (Barossa)

BRCA mutation

Niraparib (NIR-B)

10

Liquid Bx SCR

TRIUMPH; PROGRESSION-FREE SURVIVAL
WITH TRASTUZUMAB AND PERTUZUMAB
Tissue positive

ctDNA positive

All (N = 17)
ctDNA RAS/BRAF/PIK3CA/ERBB2 WT (N = 11)
ctDNA RAS/BRAF/PIK3CA/ERBB2 MT (N = 5)

100%
80%

100%
80%

60%

60%

40%

40%

20%

20%

0%

0

No. at Risk
All*
17
Quadruple WT
11
Any MT
52

4

6

All (N = 15)
ctDNA RAS/BRAF/PIK3CA/ERBB2 WT (N = 11)
ctDNA RAS/BRAF/PIK3CA/ERBB2 MT (N = 4)

8Time (months)
11

7

2

0

10

7

2

0

0

0

0

0

0%

0

No. at Risk
All
15
Quadruple WT
11
Any MT
4

2

4

6

8

Time (months)
9

6

2

0

9

6

2

0

0

0

0

0

* Including one patient without a ctDNA result

All
ctDNA RAS/BRAF/PIK3CA/ERBB2 WT
ctDNA RAS/BRAF/PIK3CA/ERBB2 MTMedian PFS,
months (95% CI)
4.0 (1.4-5.6)
5.6 (2.8-7.7)
1.4 (0.5-1.8)

All
ctDNA RAS/BRAF/PIK3CA/ERBB2 WT
ctDNA RAS/BRAF/PIK3CA/ERBB2 MTMedian PFS,
months (95% CI)
4.0 (1.3-5.6)
5.6 (1.3-6.2)
1.4 (1.2-1.8)

Nakamura Y, Yoshino T.: ESMO 2019 526PD

Liquid Bx SCR

12

Liquid Bx SCR

GI-SCREENとGOZILA等のDBs
ToMMo

GOZILA 臨床DB

GI-SCREEN 臨床DB

GOZILA ID
GOZILA ゲノムDB

SCRUM ID

dbSNP

GI-SCREEN ゲノムDB

SCRUM IDCOSMIC
CIViC
OncoKB

GI-SCREEN MSI DB

DB間の横断検索を可能としている
13

Utility of Circulating Tumor DNA Sequencing in Advanced
Gastrointestinal Cancer: SCRUM-Japan GI-SCREEN & GOZILA Studies.

Liquid Bx SCR

Tissue and Plasma Genotyping
Demonstrate Similar Success Rates

14

Nakamura Y, et al.: ASCO GI 2020 #5. Nakamura Y, Taniguchi H, Ohtsu A, Yoshino T. Nat Med. 2020

Global Collaboration
International collaboration for HER2 positive colorectal cancer

International collaboration for BRAF non-V600E mutation

SWOG
TRIUMPH
Meta-analysis
projectionof(USA)(Japan)HER2-amplified
metastatic colorectal cancer

HERACLES(Italy)

MyPathway(USA)
Yaeger R, Kotani D,….., Yoshino T, et al. Clin Cancer Res. 2019 in press

Collaboration for ctDNA-guided umbrella/basket trials
SCRUM-Japan

ACCRU

15

Global Collaboration led by ours (representative example)
Unification of International Diagnostic Criteria

Fujii S, Yoshino T. JCO Precision Oncology, 2020
World's First Guideline for Tumor-agnostic Precision Oncology in Advanced Solid Cancer

Yoshino T, Ann Oncol. 2020

新たな臓器横断的ゲノム診療の選択肢の可能性
JSCOｰESMOｰASCOｰJSMOｰTOS: international expert consensus recommendations
for tumour-agnostic treatments in patients with solid tumours
with microsatellite instability or NTRK fusions

The percentage of patients with a high TMB was much higher than for either MSI or known/likely NTRK rearrangements in adulttumours (as high as 54.60% in skin tumours, 6.32% overall in 212 704 adult profiles) but was low in pediatric patients (maximum
2.25% in gliomas, 0.91% overall in 4382 pediatric profiles).
Note; high TMB (denied as >20 mutations/Mb) in solid tumours from adult (age >18 years) and paediatric (age<18 years) patients.

Yoshino T, et al.: Ann Oncol 31:861-872, 2020.

Agenda•PAST to Present: Achievement of the SCRUM-Japan Tissue NGS* Screening (SCR) and stratified clinical trials
 Data Base (DB) Liquid Bx SCR and stratified clinical trials
 International Collaboration•

TODAY: Ongoing innovative projects
 Liquid Bx & Microbiome (MB) Artificial intelligence /Machine Learning (AI/ML)
 CIRCULATE-Japan (C-J) and Early Detection (COSMOS)•TOMORROW: Translating Multi-Omics into Clinical Utility*Next Generation Sequencing

The 3rd-stage MONSTAR-SCREENSCRUM-Japan GI-SCREENSCRUM-Japan MONSTAR-SCREEN

Period～2019 Mar

July 2019 ～ Mar 2021

Number6,391 patients(CRC 3439、non-CRC 2952)

2,000 patientsField

GI cancers

GI, breast, skin, head and neck,
Gynecological, urological

Aims

Promoting new drugs/devices
Creating large-scale database
Screening for clinical trials

Promoting new drugs/devices
Promoting translational research
Screening for clinical trials targeting resistantmechanisms

GI cancers ⇒ All solid tumors
Screening system ⇒ Monitoring system
Platform of translational research to develop new drugs

SCRUM-Japan参加企業との共同研究
Nationwide Genome
Screening Project
SCRUM-JapanMONSTAR-SCREEN

全固形がん

Strictly Confidential

SCRUM-Japan MONSTAR-SCREEN
Main Target N=2000を平均2回測定

薬物療法未治療の患者（4）（大腸癌・胃癌・乳癌・黒色腫に限定）
N = 500（10%が治験対象と想定）（肺がん除く）コンパニオン診断薬
開発のサポート（8）特定の遺伝子異常が同定された患者（1）N = 500（30%が治験対象と想定）

N=350の臨床試験登録を目標

NGS解析*

F1L
免疫療法を含む治療受ける患者（5）N = 500（10%が治験対象と想定）

分子標的薬治療に伴うClonal Evolutionの可能性がある患者（3）例：mCRCの抗EGFR抗体治療後
N = 500（20%が治験対象と想定）*FoundationOneやNCCオンコパネルなどの組織NGS解析結果も収集(VCF File)

Microbiome
(6)

臨床情報+
+SCRUM-Japan内で収集する臨床情報・治療効果等をより詳細に収集（現在の前向き
レジストリの収集項目を踏
襲）

腫瘍組織のNGS解析結果を収集比較によるClonal Evolutionの全体像の把握とカタログ化（3）SCRUM-Japan参加企業との共同研究範囲

個別研究

TR・Reverse TR
によるバイオマー
カー探索（7）

Target E

Clinical Trial E

Target F

Clinical Trial F

TargetG**

Clinical Trial G
経時的な評価Targetはあるが、治験不適格・治験不参加患者

自然史の追跡

16S rRNA-seq

Registryの
促進および
経時的な評
価による科
学的な裏づ
け（2）

** Clonal
Evolutionに伴う
Target。Clonal
Evolutionに対する臨床開発を意味
する。

G360

F1L

3

Microbiome analysis
Sample quality
Quality check(by one analysis)

16S rRNA Seq
MiSeq

Quality control(by 3 months)

SCRUM-Japan DB

FASTQ
16S rRNA Seq

Shotgun Seq
NovaSeq

FASTQ

Shotgun Seq

Free accessLilmited companies with
supplementary contract

We adopt both 16S rRNA and shutgun Seq

All participating companies

Single cell genome analysis

This is a Novel technology for bacterial
genome analysis

従来のメタゲノム解析
Conventional
metagenome analysis

Singleシングルセルゲノム解析
cell metagenome analysis

 Featureof the whole image
 全体像の特徴
 Miscellaneous
information
 雑多でカオスな情報
 Abstract
understanding 抽象的な理解 Featureof individual bacteria
 細菌個々の特徴
 Organized
information
 整理された情報
 Essential
understanding 本質の理解This method can analyze individual
bacterial whole genome without
contamination.

Differences Among Each Microbiome Analyses
16S rRNA analysis

Shotgun metagenome
analysis

Single cell genome
analysis

purposebacterial species
component data

Whole genome
sequencing

Functional analysis inindividual bacteria

Target

16S rRNA geneCollection of wholegenome fragment

All genome ofindividual bacteria

Read length

400 bases

Several million ~ several
ten million bases

Several 100 million ~several billion bases
per cell

Bacterial component

〇

〇

〇

Functional genecomponent

×〇〇Linkage of bacteria
and function

×

△

〇

Discrimination ofstrain××〇
We plan these analyses in this study (about 200 cases)

AI/MLSCRUM-Japan GENESIS Project

Pharmaceuticalcompanies

Collaboration
hospital

Research
institute

Government
agencyPublic interest
group

SCRUM-Japan GENESIS

AI oncology integrated
analysis data store

・・・
Various AI algorithms will be created based on
SCRUM-Japan

Extract necessary information for development

Storage / backup of analysis results

Cloud environment
On premise environment

Study participation
facility24

SCRUM-Japan Genesis

Basic info.

Clinical info.

Medicalhistory

Medication
history

Study participation
facility

Specimen
info.
Treatment
info.

Chemotherapy
history

Inspection
result info.

Adverse event

Medical image
info.

Study participation
facility

Genetic info.
・・・

Study participation
facility

Study participation
facility

Yoshino T, et all. Oral presentation at Google Cloud Next ’19
https://www.youtube.com/watch?v=iO4x1aJw_Jc

AI/MLSCRUM-Japan GENESIS Virtual Sequencing Project

HE staining

dissection

NGS sequencing

Genetic alteration identification

Virtual Sequencing

Development of prompt & accurate genome alteration estimation method
using AI image processing
Gene X…
Gene Y…

Learning by
Training data

Validation by
Verification
data

Gene Z…
FFPE section image and genome
analysis results, collected by
SCRUM-Japan

25

SCRUM-Japan Genesis

Virtual Sequencing algorithm

Verify the accuracy of thealgorithm using verification
data

Yoshino T, et all. Oral presentation at Google Cloud Next ’19
https://www.youtube.com/watch?v=iO4x1aJw_Jc

AI/ML

Preliminary result for gene X
AUC 0.94 in ROC*
*Gene X positive prediction for wholeslide images under prespecified
conditions.

Only a previous study+ showed an AUC from 0.640 to 0.856 to predict for 10 genes in patients with lung cancer.
+Coudray N, et al. Nature Med. 201826

SCRUM-Japan Genesis

Yoshino T, et all. Oral presentation at Google Cloud Next ’19
https://www.youtube.com/watch?v=iO4x1aJw_Jc

Agenda•PAST to Present: Achievement of the SCRUM-Japan Tissue NGS* Screening (SCR) and stratified clinical trials
 Data Base (DB) Liquid Bx SCR and stratified clinical trials
 International Collaboration•

TODAY: Ongoing innovative projects
 Liquid Bx & Microbiome (MB) Artificial intelligence /Machine Learning (AI/ML)
 CIRCULATE-Japan (C-J) and Early Detection (COSMOS)•TOMORROW: Translating Multi-Omics into Clinical Utility*Next Generation Sequencing

Recurrence Risk Assessment:
ctDNA Detecting Minimal Residual Disease (MRD)

Curative
Intent Treatment(e.g. primary removal)

Tumor DNA
Detection

Minimal Residual
Disease=Recurrence ++Normal DNA

No Minimal
Residual Disease=
Low Recurrence

Tie J, et al.: ASCO-GI 2019 #GI19.
Yoshino T. ESMO 2019

Stratification by Post-operative ctDNA Status
Plasma Safe-seqS

Signatera

Percentage Recurrence-survival

Stage II colon cancer, n = 178
Patients not treated with chemotherapy

Stage I-IIII CRC, n = 13010080

ctDNA Negative (n=164)
ctDNA Positive (n=14)

60
40
20

HR 18 (95%CI 7.9-40)

0
0

12

24
36
Months since surgery

48

60

29

Tie J, et al. Sci Transl Med 2016.
Reinert T, et al. JAMA Oncol 2019.

Cross-comparison of Predictivity
7.2
16.5*
Hazard Ratio (odds ratio) of relapse in CRC

0.73
MMR Status(MSS vs. MSI-H)

1.27

1.33

1.38

Tumor Grade(high vs. low)

Stage II
(T4 vs. T3)

Oncotype DX(Score per 25U)

This Assay(Pos vs. Neg)

本アッセイによる根治手術後の再発予測能は、既知の再発予測マーカーよりはるかに優れている。
*Tarazona N, et al. Abst #4009 ASCO 202030

6

Ideal Precision Medicine for Resected Colon Cancer

ctDNA
negative
Locally advanced
colon cancer

ctDNA
analysis

Observation or lessintensive adjuvant
treatment

Surgery

ctDNA
positive

More intensive adjuvant
treatment

Yoshino T. ESMO 2019
31

C-J

アッセイ紹介Personalized and Tumor-Informed Methodology Means Each Patient Gets Custom-Built Assay

Can detect as few as 1 genomic tumor
equivalent in 10 ml of blood (VAF = 0.01%)

全エクソーム解析（正常部および癌部）

カスタムパネルによるモニタリング

患者毎にカスタムパネルを作製

ctDNA陽転化による再発の早期同定

32

CIRCULATE JAPAN Overall Architecture

Incl. Taiwan

Curatively resectedcolon cancer(N=2,500)
Stage II/III(N=1800)

ctDNA positive
ctDNA negative

Resected
tissueStock Pre-Op
Samples

Post-Ope
ctDNA
Negative

3-5 wks
Curatively resected
rectal cancer(N=400)WES

N=1240R

Experimentalarm
No chemoNo
Chemo

Control arm
CAPOX 4 cycles(3 months)

Enroll into+ve study

3moStandard
Cx
Stage IV (N=300)

N=240

CAPOX
4 cyclesEnroll into+ve study
In case of performed NAC

R

B

Enroll into+ve studyA

Up to 24 mo

3-5 wks
Post-Ope
ctDNA
Positive

F/U for
7 years

Control arm
Placebo
6 cyclesExperimental arm
FTD/TPI
6 cyclesF/U for5 yearsCEarly Detection

New Horizon for Early Detection by ctDNA Analysis
Colorectal cancer screening Fecal occult blood test
Gastric cancer screening
 Stomach X-ray
 Gastroendoscopy
Lung cancer screening Chest X-ray
 Sputum cytology

All cancer type
 ctDNA analysis

Cervical cancer screening Cervical cytology
Breast cancer screening Mammography34

我々が行っているリキッドバイオプシー研究

早期発見
35

術後リスク評価

進行がんのがんゲノム医療

Agenda•PAST to Present: Achievement of the SCRUM-Japan Tissue NGS* Screening (SCR) and stratified clinical trials
 Data Base (DB) Liquid Bx SCR and stratified clinical trials
 International Collaboration•

TODAY: Ongoing innovative projects
 Liquid Bx & Microbiome (MB) Artificial intelligence /Machine Learning (AI/ML)
 CIRCULATE-Japan (C-J)•

TOMORROW: Translating Multi-Omics into Clinical Utility*Next Generation Sequencing

Strictly Confidential

Hypothesis

 AI-driven multi-omics analysis reveals what is actually happening intissues & cells as well as tumor micro-environment.

Clarify More Molecular Blueprint AI-driven multi-omics analysis identify candidate multidimensional
biomarkers.
Beyond Single Biomarkers

From Genomics to Multi-Omics with AI

Development of Anti-cancer Drugs
2nd Gen:Expression Driven & TME-Targeted
Precision Oncology

1st Gen:DNA-alteration Driven
Precision Oncology

Cytotoxic

Antibody
TKI

ADC
IO

CART
BiTE

Genome(DNA)
BeyondGenome(RNA, Protein)

38

Virus

Beyond Genomics

MONSTAR SCREEN-2 (Main Part)
From Genomics to Multi-Omics with AI
Since April 2021 during 3 years
Pan-cancer excl. lung cancer (N = 2,750)
AI Predictor, Database accessPan-cancer excl. lung cancer (N = 2,000)

Pre- and post-treatment

Clinical Data
 Enrollment to trials
 1st line or Subsequent line with intent for trial
enrollment
Tumor
 PS 0 or 1

tissue Colorectal
 Gastric
 Pancreatic Biliary tract
 Breast
F1L

 Gynecological Urological
 H&N
 Melanoma
 Others

F1CDx

 Identification of rare fraction
WES(SNV, CNV, Fusion,
HRD, TMB, SNP,
Pathogen)

Shotgun Seq
Single cell genome Seq

W Clinical Data (f/u rate > 99%)

NRG1)
 Novel trials (HRD, TMB,

WTS

Signature, AI predictor,(Fusion,Signature [i.e. CMS])

novel biomarker)
 Deep learning with deep

Microbiome

16S rRNA Seq

 Novel biomarkers (i.e.

clinicogenomic data
IHC
(PD-L1, HER2, etc.)

Beyond Genomics

MONSTAR SCREEN-2 (Sub Part)AI Predictor, Database accessPan-cancer excl. lung cancer (N = 2,750)
Pre- and post-treatment

Clinical Data
 Enrollment to trials
 1st line or Subsequent line with intent for trial
enrollment
Tumor
 PS 0 or 1
tissue Colorectal
 Gastric
 Pancreatic Biliary tract
 Breast

 Gynecological Urological
 H&N
 Melanoma
 Others

 Identification of rare
WES

fraction(SNV, CNV, Fusion,
HRD, TMB, SNP,
Pathogen) Novel biomarkers (i.e.

WTS

 Novel trials (HRD, TMB,

(Fusion,Signature [i.e. CMS])

NRG1)

Signature, AI predictor,
novel biomarker)
 Deep learning with deep

IHC
Pre- & postTreatment(PD-L1, HER2, etc.)

clinicogenomic data

Pan-cancer excl. lung cancer (N = 2,500 X twice) Whole-exome/transcriptome circulating tumor NA (nucleic acid) analysis

MONSTAR-SCREEN-2
Free Access Rightfor Research &Commercial Use incl.
IF images

Funding Companies

Whole genome

SCRUM DB

Beyond Genomics

Can also obtain the IF
images with annotation

ctNA analysis
AI-based integrated study

Whole transcriptome
HE images andPathological Dx

IHC images and scores

All of the images willdefinitely be essential forthe subsequent studies
as training data

Academia

Permission of Assess for
Registered Investigators
for Research Use Only
Note; IF, Immunofluorescence

Must upload theannotation for the
IF imageSummary - From ‘MAY BE’ to ‘MUST BE’ Metastatic Disease Clinical utility for analyzing ctDNA in GI cancers & international collaborations are actively ongoing. We
established ctDNA analysis likely to enable more GI cancer patients to enroll clinical trials withoutcompromising efficacy.
MRD & Early detection

 Beyond cancer patient selection for targeted therapy, the potential of ctDNA analysis may expand the utility
to patient stratification for adjuvant chemotherapy and early detection.
Beyond Genomics

 We will investigate clinical utility for analyzing multi-omics incl. ctNA in all solid tumors in the near future, thatis, evoking the paradigm shift from genomics to multi-omics with AI toward the digital medicine.

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