Open access

Original research

Koichi Azuma ‍ ‍ ,1 Huihui Xiang,2 Tomoyuki Tagami,3 Rika Kasajima,2
Yumiko Kato,3 Sachise Karakawa,3 Shinya Kikuchi,3 Akira Imaizumi,3Norikazu Matsuo,1 Hidenobu Ishii,1 Takaaki Tokito,1 Akihiko Kawahara,4Kenta Murotani,5 Tetsuro Sasada ‍ ‍ ,6 Yohei Miyagi,2 Tomoaki Hoshino7

To cite: Azuma K, Xiang H,
Tagami T, et al. Clinicalsignificance of plasma-­freeamino acids and tryptophan
metabolites in patients withnon-­small cell lung cancer
receiving PD-­1 inhibitor: a pilot
cohort study for developing a
prognostic multivariate model.
Journal for ImmunoTherapyof Cancer 2022;10:e004420.
doi:10.1136/jitc-2021-004420► Additional supplemental
material is published online only.
To view, please visit the journal
online (http://​dx.d​ oi.​org/​10.​1136/j​ itc-​2021-0​ 04420).

KA and HX contributed equally.
Accepted 31 March 2022

© Author(s) (or theiremployer(s)) 2022. Re-­use
permitted under CC BY.
Published by BMJ.
For numbered affiliations see
end of article.
Correspondence to
Dr Koichi Azuma;​azuma@​med.k​ urume-​u.a​ c.​jp

ABSTRACT
Background Amino acid metabolism is essential fortumor cell proliferation and regulation of immune cellfunction. However, the clinical significance of free aminoacids (plasma-­free amino acids (PFAAs)) and tryptophan-­related metabolites in plasma has not been fully
understood in patients with non-­small cell lung cancer(NSCLC) who receive immune checkpoint inhibitors.
Methods We conducted a single cohort observational
study. Peripheral blood samples were collected from
53 patients with NSCLC before treatment with PD-­
1 (Programmed cell death-­1) inhibitors. The plasma
concentrations of 21 PFAAs, 14 metabolites, and neopterin
were measured by liquid chromatography–massspectrometry. Using Cox hazard analysis with thesevariables, a multivariate model was established to stratify
patient overall survival (OS). Gene expression in peripheral
blood mononuclear cells (PBMCs) was compared between
the high-­risk and low-­risk patients by this multivariate
model.
Results On Cox proportional hazard analysis, higher
concentrations of seven PFAAs (glycine, histidine,
threonine, alanine, citrulline, arginine, and tryptophan)as well as lower concentrations of three metabolites(3h-­kynurenine, anthranilic acid, and quinolinic acid) andneopterin in plasma were significantly correlated with
better OS (p<0.05). In particular, the multivariate model,
composed of a combination of serine, glycine, arginine,and quinolinic acid, could most efficiently stratify patient
OS (concordance index=0.775, HR=3.23, 95% CI 2.04
to 5.26). From the transcriptome analysis in PBMCs, thismultivariate model was significantly correlated with thegene signatures related to immune responses, such as
CD8 T-­cell activation/proliferation and proinflammatory
immune responses, and 12 amino acid-­related genes weredifferentially expressed between the high-­risk and low-­risk
groups.

Key messages
What is already known on this topic⇒ Imbalances of plasma-­free amino acids (PFAAs) andrelated metabolites have been reported in various
types of cancer, but their impact on antitumor immune responses has not been fully understood.

What this study adds
⇒ The multivariate model, composed of a combinationof serine, glycine, arginine, and quinolinic acid, could
efficiently stratify overall survival in patients withnon-­small cell lung cancer who receive PD-­1 inhibitors. This model was significantly correlated with the
immune-­related gene signatures in peripheral bloodmononuclear cells.

How this study might affect research, practiceor policy⇒ The profiling of PFAAs and metabolites in plasma

might be useful for stratifying patients with cancer
who will benefit from PD-­1 inhibitors.

Conclusions The multivariate model with PFAAs andmetabolites in plasma might be useful for stratifying
patients who will benefit from PD-­1 inhibitors.

INTRODUCTION
Immune checkpoint inhibitors (ICIs) haveprovided new therapeutic approaches fortreatment of otherwise incurable cancer
patients. Recently, ICI therapy has become
widely accepted as a standard cancer treatment for various cancers, including non-­small
cell lung cancer (NSCLC). However, only alimited number of patients show long-­term
clinical benefits. Thus, biomarkers for patient

Azuma K, et al. J Immunother Cancer 2022;10:e004420. doi:10.1136/jitc-2021-004420

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Clinical significance of plasma-­freeamino acids and tryptophan metabolites
in patients with non-­small cell lungcancer receiving PD-­1 inhibitor: a pilot
cohort study for developing a prognostic
multivariate model

Open access

MATERIALS AND METHODSPatients
This study was a single cohort observational study that
included patients with advanced or recurrent NSCLC who
were scheduled to receive anti-­PD-­1 antibody (nivolumab
or pembrolizumab) without concurrent chemotherapy at
Kurume University Hospital (Fukuoka, Japan) between
May 2017 and September 2019. As an exploratory study,
we set an eligible target number of cases during the study
period. All eligible patients received nivolumab (3 mg/
kg/body) every 2 weeks or pembrolizumab (200 mg/body) every 3 weeks. Kidney dysfunction, liver cirrhosis,
and congenital metabolic disorder were excluded.
Data collection
All clinical data, including blood test results, werecollected from the patients’ medical records. The best
overall response was defined as the best response designation recorded between the date of the first dose ofPD-­
1 inhibitor and the date of the first documented
tumor progression. Treatment response was determinedaccording to the Response Evaluation Criteria in Solid
Tumors (RECIST) v.1.1. Progression-­free survival (PFS)2

was defined as the period from the date of the first dose to
the date of disease progression or death due to any cause.
Overall survival (OS) was defined as the period from thedate of the first dose to the date of death from any cause.
The severity of AEs was graded using the National Cancer
Institute Common Terminology Criteria for Adverse
Events V.4.0. PD-­L1 expression was examined by immunohistochemistry with anti-­PD-­L1 antibody (clone E1L3N;
Cell Signaling Technology, Danvers, Massachusetts,USA) in paraffin-­embedded tissue samples, as reported
previously.7
Analysis of PFAAs
Peripheral blood samples were collected in the morning
before initiation of ICI therapy. After an overnight fast,
blood samples (5 mL) were collected from the antecubital
vein into tubes containing EDTA-­2Na as an anticoagulant
and were immediately (<1 min) placed on ice. Plasma wasseparated from the whole blood samples via centrifugation at 3000 rpm (at 1000G or more to reduce plateletcontamination) for 15 min at 4°C and stored at −80°C
until analysis. After thawing, plasma samples were deproteinized using acetonitrile at a final concentration of
50% before measuring amino acid concentrations using
high-­
performance liquid chromatography–electrosprayionization mass spectrometry via precolumn derivatization, as described previously.8 This deproteinizing stepwith acetonitrile allowed analysis of amino acids boundto protein carriers in plasma. Concentrations of thefollowing 21 amino acids were measured: alanine, alpha-­amino-­butyric acid, arginine, asparagine, citrulline, glutamine, glutamate, glycine, histidine, isoleucine, leucine,lysine, methionine, ornithine, phenylalanine, proline,
serine, threonine, tryptophan, tyrosine, and valine.
Analysis of tryptophan-related metabolites and neopterin inplasmaTryptophan-­related metabolites and neopterin wereanalyzed as reported previously.9 Briefly, 14 metabolites (3-­hydroxyanthranilic acid, 3-­hydroxy-­kynurenine,3-­indoleacetic acid, 5-­hydroxyindole-­3-­acetic acid,5-­hydroxy-­L-­tryptophan, anthranilic acid, indole-­3-­lactic
acid, kynurenine, kynurenic acid, picolinic acid, quinolinic acid, serotonin, xanthurenic acid, and N′-formyl-­kynurenine) in deproteinized plasma were analyzed using
liquid chromatography–electrospray ionization tandem
mass spectrometry. These metabolites were successfully
separated within 15 min without derivatization using areversed-­phase pentafluorophenyl column for liquid
chromatography separation. Neopterin, which is known
as a biomarker for inflammation and is often evaluatedwith tryptophan metabolites, was also simultaneously
analyzed.
Statistical analysis for developing multivariate models
To develop the multivariate model according to previously
reported methods,10 the PFAAs and metabolites associated with OS (p<0.1) without adjusting for multiplicity
Azuma K, et al. J Immunother Cancer 2022;10:e004420. doi:10.1136/jitc-2021-004420

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selection need to be developed to identify those who will
optimally benefit from ICI and to avoid unnecessary
adverse events (AEs) and treatment costs.
To date, several biomarkers, such as PD-­
L1
(Programmed death-­Ligand 1) expression in tumor or
immune cells, the existence of driver gene mutations, andtumor mutational burden, have been available to predictpatients’ responses to ICI but with limited accuracy.1
Further, various next-­
generation ‘omics’ technologies
that evaluate the genome, transcriptome, and epigenome
have been applied to development of novel biomarkers,2
but reliable discrimination technology is yet to be determined. In addition to good discrimination performance,
simple and less invasive techniques that do not require
tumor tissues remain to be developed because tumor
samples often cannot be easily obtained especially inpatients with cancer without indication of surgical treatment. Therefore, blood testing might be one of the desirable approaches for biomarker development.3
The regulation of amino acid metabolism has beenreported to be essential for tumor cell proliferation andregulation of immune cell functions.4 Thus, the aminoacid profiles could be a critical marker to evaluate metabolic reprogramming in the tumor immune microenvironment and the metabolic condition of systemic immune
cells. The amino acid profiles that show characteristicchanges in different cancers can be used for diagnosis
and evaluation of prognosis after tumor resection.5 6 The
present study thus aimed to evaluate the clinical significance of plasma-­free amino acid (PFAA) and tryptophan
metabolites to ultimately develop a reliable method forstratifying patients with cancer who might benefit from
ICI therapy.

Open accessPreparation of peripheral blood mononuclear cells (PBMCs)
and total RNA extraction
PBMCs were isolated by density gradient centrifugation using Ficoll-­Paque Plus (GE Healthcare, Uppsala,Sweden) before initiation of ICI therapy and were frozen
until analysis. Total RNA was isolated from PBMCs
using RNeasy Plus Universal Mini Kit (Qiagen, Hilden,
Germany), according to the manufacturer’s instructions. RNA amounts and purities were measured using
NanoDrop1000 (Thermo Scientific, Wilmington, Delaware, USA). RNA integrity was assessed using the RNA
Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100
system (Agilent Technologies, Santa Clara, California,
USA). Construction of cDNA libraries followed by RNA
sequencing was performed by Takara Bio (Shiga, Japan)
as a contract analysis with SMART-­Seq V.4 Ultra Low Input
RNA Kit (Clontech, Palo Alto, California, USA) and a
NovaSeq sequencing system (Illumina, San Diego, California, USA) according to the manufacturer’s instruction.

patient based on an LM22 signature gene file.11 The‘DESeq2’ Bioconductor R package V.1.28.1 (http://www.​bioconductor.org/packages/release/bioc/vignettes/​
DESeq2/inst/doc/DESeq2.html) was carried out tonormalize the process and identify the differentiallyexpressed genes (DEGs) between two groups from theread count data of RNA sequencing. The threshold value
to list DEGs was greater than 1.5-­fold change with p<0.05.
A list of amino acid metabolism-­related genes (AAMGs),
including those involved in amino acid metabolic pathways and amino acid transport, was generated from the
Kyoto Encyclopedia of Genes and Genomes (KEGG, www.​genome.jp/kegg) and the AmiGO V.2 browser (http://​amigo.geneontology.org/amigo). Gene annotation analysis was performed for DEGs as a whole or DEGs listed
as AAMGs (amino acid metabolism-­related DEGs (DEG–
AAMGs)) based on the Gene Ontology (GO, http://geneontology.org), KEGG, Reactome Pathway (REACTOME,
https://reactome.org), and gene set enrichment analysis(GSEA, https://gsea-msigdb.org/gsea/index.jsp) databases. The ‘clusterProfiler’ V.3.16.1 and ‘ReactomePA’
V.1.32.0 R packages were used for enrichment data analysis and data visualization.12 P values were corrected for
multiple comparisons by performing the R function p.adjust with the Benjamin and Hochberg method.
Statistical analysis
The Cox proportional hazard model was used to analyzeeach patient’s characteristics, blood test results, PFAA
and metabolite concentrations as an explanatory variable(univariate analysis) correlated with OS. Continuous variables were presented as mean and SD; they were compared
between categorical groups using a t-­test. Correlations
between continuous variables were evaluated using Spearman’s rank correlation coefficient analysis. Univariate
logistic regression was performed with the multivariate
model score as the dependent variable and patient characteristics as independent variables to estimate OR and
95% CI. All statistical analyses were conducted using theR-­language for statistical computing V.2.9.0 and V.4.0.2,
and JMP V.15 (SAS Institute, Cary, North Carolina, USA).

Analysis of RNA sequencing data
After confirming the read quality with FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/),
sequence data were aligned to the human genome
GRCh37 using STAR V.2.5.2a (https://github.com/​
alexdobin/STAR/releases/tag/2.5.2a), and the mapped
read count of each sample was calculated using Python
based HTseq. The obtained read count data for
V.2.7-­
each PBMC sample were subjected to the Genomon 2
DNA analysis pipeline (https://github.com/Genomon-​Project) to check sequencing qualities. The CIBERSORT
algorithm (‘CIBERSORT’ R package, http://cibersort.​
stanford.edu) was applied to deconvolute the total transcriptome data for predicting the relative proportion ofindividual immune cell subtypes in the PBMCs of each

RESULTS
Patient characteristics and their association with OS
This study enrolled 53 patients treated with anti-­PD-­
1
therapy. The overall response rate, median PFS, andmedian OS were 30.2%, 2.8 months (95% CI 1.4 to 4.9
months), and 7.6 months (95% CI 5.5 to 13.1 months),
respectively. Overall, 19/53 patients (35.8%) developed
treatment-­
related AEs, and 35 patients died duringfollow-­
up. The patients’ characteristics and blood test
results are shown in table 1. Forty (75%) patients weremale and 13 (25%) were never-­smokers, and the mean
age of all patients was 69.7 years. Seven (13%) patients
had recurrence after concurrent chemoradiotherapy; 14(26%) had recurrence after surgery; and 32 (60%) hadstage IV. Twenty (38%) patients received PD-­1 inhibitor

Azuma K, et al. J Immunother Cancer 2022;10:e004420. doi:10.1136/jitc-2021-004420

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were chosen as variables for multivariate regression analysis with the Cox proportional hazards model. Stepwise
variable selection was performed to minimize the Akaike
information criterion (AIC) and the concordance index(C-­index), and its 95% CI was also estimated. To verify the
combination of variables, leave-­one-­out cross-­validation(LOOCV) was applied to the multivariate model using thesame variables set. In brief, one matched set composedof one responder patient, and a corresponding non-­responder patient was omitted from the training data set.
The logistic regression model was then calculated using
the remaining samples to re-­estimate coefficients for eachvariable. The function values for the left-­out matched set
were calculated based on this model. This process was
repeated until every sample in the study data set had beenleft out once. Cut-­off values of the multivariate modelfor Kaplan-­Meier analysis and log-­rank test were established for the first quartile, median, and third quartile
values. The HR and 95% CI between the stratified groups
with a multivariate model were determined using Cox
proportional hazard analysis. Complete-­case analysis was
performed in the development of the multivariate model.

Open access

Cox hazard model
Patient characteristics

Total (N=53)

HR

95% CI

P valueAge (years), mean (SD)69.7 (8.5)

1.23

0.88 to 1.74

0.232

 Female

13 (25)1 Male40 (75)0.92

0.44 to 1.92

0.826

 Former40 (75)1 Never

13 (25)1.46

0.71 to 3.01

0.318

 0–1

38 (71)1 2–3

15 (29)2.17

1.06 to 4.44

0.043

7 (13)

1

Sex, n (%)

Smoking, n (%)

Performance Status, n (%)

Stage, n (%) Stage III (recurrent after chemoradiotherapy) Recurrent after surgery

14 (26)2.25

0.47 to 10.72

0.308 Stage IV

32 (60)4.85

1.14 to 20.67

0.033 Non-­squamous

38 (72)

1 Squamous

15 (28)1.38

0.65 to 2.92

0.410

 Wild Type41 (77)1 EGFR (Epidermal growth factor receptor)

11 (21)0.64

0.26 to 1.56

0.306

 ALK (Anaplastic lymphoma kinase)

1 (2)(EGFR or ALK/WT)

0.60 to 2.51

0.232

Histology, n (%)

Driver mutation, n (%)

Tumor PD-­L1 expression, n (%) 0%–49%

21 (45) 50%–100%

26 (55)1.23 1st line

20 (38)1 2nd line

22 (42)0.76

0.36 to 1.60

0.471

0.30 to 1.88

0.540

Treatment line, n (%) 3rd line

7 (13)

0.75 4th line

2 (4)(3rd line or later/1st line) 6th and 7th lines

2 (4)PD-­1 blocker, n (%) Nivolumab

25 (47)1 Pembrolizumab

28 (53)1.37

0.70 to 2.66

0.358

3.42 (0.61)

0.57

0.38 to 0.85

0.005

Blood test, mean (SD) Albumin (g/dL) LDH (lactate dehydrogenase, U/L)

2634 (118)1.13

0.81 to 1.49

0.448

 White blood cell (/μL)

7513 (3357)

1.35

0.98 to 1.75

0.062

 Lymphocyte (/μL)

1354 (615)0.58

0.38 to 0.86

0.006

 Neutrophil (/μL)

5476 (2969)

1.50

1.10 to 1.97

0.011

 Eosinophil (/μL)

176 (178)0.80

0.52 to 1.15

0.248

 Monocyte (/μL)

477 (223) Neutrophil:lymphocyte ratio

4.94 (3.56)

1.49

1.05 to 2.02

0.027

1.72

1.27 to 2.25

<0.001Categorical variables are shown as the distribution of corresponding patient numbers. Continuous variables are shown as mean and SD values.
Univariate analysis was conducted using the Cox proportional hazard model for OS. HR, 95% CI.
ALK, anaplastic lymphoma kinase; LDH, lactate dehydrogenase; OS, overall survival; PS, performance status; WT, wild type.

4

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Table 1 Patient characteristics and their association with OS

Open access

Association between PFAA and metabolite concentrations andOS
Higher concentrations of seven PFAAs, including
glycine, histidine, threonine, alanine, citrulline, arginine,
and tryptophan, before ICI therapy were significantly
correlated with better OS on Cox proportional hazard
analysis (p=0.049, 0.009, 0.015, 0.006, 0.018,<0.001,
and 0.005, respectively) (table 2). In contrast, lower
concentrations of three tryptophan-­related metabolites,
including 3h-­kynurenine, anthranilic acid, and quinolinic acid, and neopterin before ICI therapy were significantly correlated with better OS (p=0.002, 0.007, 0.049,
and 0.014, respectively). The concentrations of serine,
histidine, threonine, alanine, arginine, tryptophan,anthranilic acid, xanthurenic acid, and neopterin stratified by the median value also showed significant differences in OS when analyzed by log-­rank testing (online
supplemental figure 2).
Development of a multivariate model for stratifying prognosis
in ICI-treated patients
The PFAA and metabolite variables that showed a trendof correlation with OS in the univariate analysis (p<0.1)
were selected as variables in the multivariate model. These
included the following 15 PFAAs and metabolites: serine,
glycine, histidine, threonine, alanine, citrulline, arginine,lysine, tryptophan, 3h-­kynurenine, 5h-­tryptophan, anthranilic acid, kynurenine, quinolinic acid, and neopterin.
A multivariate model was optimized through a stepwise
method to minimize AIC. We created the best multivariate model comprising three PFAAs and one metabolite,
namely, serine, glycine, arginine, and quinolinic acid asvariables, expressed by the combination of each variable
and the corresponding coefficient, F=a+b*serine+c*glycine+d*arginine+e*quinolinic acid. The discrimination
performance of this model was as follows: C-­index=0.775
Azuma K, et al. J Immunother Cancer 2022;10:e004420. doi:10.1136/jitc-2021-004420(no validation), C-­index=0.742 (the combination of variables was validated with LOOCV), and Cox HR=3.23(95% CI 2.04 to 5.26).
Figure 1A shows the results of discrimination with thismultivariate model at three cut-­off values: (1) first quartile, (2) median, and (3) third quartile. In this multivariate model, the higher the score, the worse the prognosis;
thus, we defined high-­risk groups when the score of themultivariate model was higher than the cut-­off value.
When the cut-­off value of the third quartile was selected,
the model could discriminate patients to the high-­risk
group (MST=2.1, 95% CI 0.97 to 2.8) and low-­risk group(MST=13.1, 95% CI 7.6 to 24.5) with an HR of 15.79(95% CI 6.15 to 40.52). We performed univariate regression analysis to investigate which factors were associatedwith the multivariate model and found that this model
was significantly correlated with monocyte count and
NLR (figure 1B). Furthermore, we evaluated the independency of prognostic factors, such as tumor stage,
performance status, tumor PD-­L1 expression, albumin
level, monocyte count, NLR, and the multivariate model(table 3). Since neutrophil and lymphocyte counts had
a direct inter-­relation with NLR, they were not includedin this analysis. As a result, performance status and themultivariate model were selected as significantly independent factors (p=0.008 and p=0.005, respectively).
We further evaluated the multivariate prediction modelin the subgroups stratified by PD-­L1 expression levels intumors (figure 1C). When the cut-­off value of the multivariate model was set in the median score, the high-­risk
group showed worse OS in higher (≥50%, n=26) PD-­L1
expression subgroups (log-­rank test, p=0.014).
Total transcriptome analyses in PBMCs obtained from highrisk and low-risk patients
We investigated the relationships between PFAAs/metabolites and transcriptome in PBMCs to elucidate potential molecular mechanisms of amino acid metabolism.
Among the 53 patients analyzed previously, PBMCs from36 patients before ICI treatment were available for totaltranscriptome analyses by RNA sequencing. We separated
the patients into the low-­risk (n=30) and high-­risk (n=6)
groups according to the cut-­off at the third quartile on
the established multivariate model. Online supplemental
tables 1 and 2 show the information of the 36 patientsanalyzed, including patient characteristics, blood test
results, and concentrations of PFAAs and tryptophan-­related metabolites, and neither of them in these 36
patients had significant differences with those in the original 53 patients. The patients in the high-­risk group hada significantly worse OS after ICI therapy (online supplemental figure 3A), and MST of both high-­risk (2.5 months)
and low-­risk (13.6 months) groups showed no significant
differences with those in the original 53 patients (2.1 and13.1 months, respectively). The risk scores by the multivariate model with PFAAs/metabolites were compared
Whitney-­
Wilcoxon test between theusing a Mann-­subgroups classified by the antitumor responses (RECIST
5J Immunother Cancer: first published as 10.1136/jitc-2021-004420 on 15 May 2022. Downloaded from http://jitc.bmj.com/ on May 15, 2022 by guest. Protected by copyright.

as the first line, 22 (42%) as the second line, 7 (13%) as
the third line, 2 (4%) as the fourth line, and 2 (4%) as thesixth or seventh line.
On the univariate analysis, performance status andtumor stage showed significant correlations with OS(p=0.043 and 0.033, respectively). In the blood test results,
albumin levels, lymphocyte counts, neutrophil counts,
monocyte counts, and neutrophil:lymphocyte ratios(NLRs) before ICI therapy were significantly correlatedwith OS (p=0.005, 0.006, 0.011, 0.027, and <0.001, respectively). Regarding the treatment lines, there were no
significant differences in OS between the first line and thesecond line or the third line or later in patients with bothlow (0%–49%) and high (50%–100%) PD-­L1 expression
levels (online supplemental figure 1A). Of the 12 patients
positive for driver gene mutations, most (92%) patients
receive PD-­
1 inhibitors as the third-­
line or later after
molecular targeted therapy and/or chemotherapeutic
agents had failed, but half of the enrolled patients showed
good antitumor responses (Partial Response, n=6) in thisstudy (online supplemental figure 1).

Open access

Cox hazard model
PFAA/metabolite (μM)

Mean (SD)HR

95% CI

P value

Glutamic acid
Serine50.5 (22.2)
101.6 (27.4)

1.03
0.72

0.71 to 1.45
0.50 to 1.03

0.863
0.075

Asparagine

41.9 (10.8)

0.73

0.47 to 1.06

0.107

Glycine207.4 (64.5)

0.69

0.45 to 1.00

0.049

Glutamine

543.4 (101.6)

0.77

0.51 to 1.16

0.213

Histidine

65.0 (23.0)

0.52

0.30 to 0.86

0.009

Threonine

107.4 (41.8)

0.58

0.34 to 0.91

0.015

Alanine305.2 (110.6)

0.60

0.41 to 0.87

0.006

Citrulline

30.4 (13.1)

0.61

0.39 to 0.92

0.018

Arginine

74.1 (20.3)

0.42

0.27 to 0.64

<0.001

Proline151.3 (63.3)

0.80

0.53 to 1.11

0.199

α-Amino butyric acid17.5 (6.0)

0.99

0.70 to 1.39

0.977

Tyrosine

63.5 (17.4)

0.79

0.52 to 1.14

0.234

Valine210.5 (63.7)

0.78

0.52 to 1.12

0.193

Methionine

22.1 (7.5)

0.75

0.46 to 1.12

0.183

Ornithine

64.9 (13.4)

0.72

0.46 to 1.10

0.126

Lysine175.7 (49.1)

0.70

0.47 to 1.03

0.069

Isoleucine

69.2 (29.7)

0.90

0.56 to 1.22

0.566

Leucine120.8 (47.7)

0.87

0.55 to 1.22

0.471

Phenylalanine

62.1 (13.3)

1.23

0.83 to 1.82

0.294

Tryptophan

46.8 (17.8)

0.53

0.34 to 0.83

0.005

3h-­kynurenine

0.069 (0.050)

1.85

1.27 to 2.60

0.002

3h-­anthranilic acid

0.047 (0.036)

1.37

0.86 to 1.96

0.166

5h-­indol-­3-­acetic acid

0.070 (0.062)

1.02

0.68 to 1.33

0.923

5h-­tryptophan

0.005 (0.009)

1.38

0.99 to 1.74

0.054

Anthranilic acid0.019 (0.016)

1.47

1.13 to 1.82

0.007

Indol-­3-­acetic acid

1.996 (1.771)

0.81

0.51 to 1.12

0.236

Indol-­3-­lactic acid

0.862 (0.551)

0.96

0.67 to 1.28

0.810

Kynurenic acid

0.049 (0.027)

1.22

0.83 to 1.73

0.293

Kynurenine

2.448 (0.716)

1.37

0.96 to 1.95

0.083

Picolinic acid

0.054 (0.033)

1.18

0.82 to 1.63

0.358

Quinolinic acid

0.730 (0.515)

1.34

1.00 to 1.69

0.049

Serotonin

0.159 (0.152)

1.09

0.70 to 1.55

0.680

Xanthurenic acid

0.011 (0.012)

0.91

0.60 to 1.29

0.572

Neopterin

0.010 (0.009)
0.018 (0.014)

1.50
0.82

1.10 to 1.92
0.46 to 1.14

0.014
0.295

N′-formyl-­kynurenine

The concentrations of PFAAs and metabolites are shown as mean and SD values.
Univariate analysis of PFAAs and metabolites before ICI therapy was conducted using the Cox proportional hazard model for OS. HR, 95% CI
and P values are shown.
ICI, immune checkpoint inhibitor; OS, overall survival; PFAA, plasma-­free amino acid.

criteria). The distribution of risk scores in the multivariate model showed significant differences between thetumor response PD and PR groups, similar to the original 53 patients (online supplemental figure 3B). These
data suggested that the 36 patients, whose PBMCs were
6

evaluated by RNA sequencing, retained the same clinicalcharacteristics as the original 53 patients.
The immune cell constitution of PBMCs obtained fromthe high-­risk and low-­risk patients was deduced by deconvoluting the bulk RNA sequencing data with the CIBERSORT
Azuma K, et al. J Immunother Cancer 2022;10:e004420. doi:10.1136/jitc-2021-004420

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Table 2 Association between PFAA and metabolite concentrations before ICI therapy and OS

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Figure 1 Multivariate model for prognosis of patients treated with immune checkpoint inhibitor. (A) Kaplan-­Meier analysis
for OS in the subgroups stratified by the multivariate model. The cut-­off values between the high-­risk and the low-­risk groups
at the following three points: first quartile value, median value and third quartile value. Cox HR (95% CI) and p value (log-­ranktest) are shown. (B) Univariate regression analysis for the association of patient characteristics with the multivariate model.
The cut-­off value of the multivariate model was set at the third quartile score. Continuous prognostic factors were divided at
the median value. ORs about tumor stage and mutation were not determined because all patients with stage III (recurrent after
chemoradiotherapy) or Epidermal growth factor receptor mutations were stratified into the high-­risk group. (C) Kaplan-­Meieranalysis for OS in the subgroups stratified by the multivariate model in the high (≥50%, n=26) or low (<50%, n=21) PD-­L1
expression group. The cut-­off values between the high-­risk and low-­risk groups were set at the median value. Cox HR (95% CI)
and p value (log-­rank test) are shown. NLR, neutrophil:lymphocyte ratio; OS, overall survival.
Azuma K, et al. J Immunother Cancer 2022;10:e004420. doi:10.1136/jitc-2021-004420

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Cox hazard model
Prognostic factors

HR

95% CI

P value

Tumor stage (recurrent
after surgery and stage
VI/recurrent-­after
chemoradiotherapy)
Performance status(2–3/0–1)

5.00

0.64 to 39.06

0.125

3.18

1.35 to 7.51

0.008

1.24

0.56 to 2.78

0.596

PD-­L1 expression (50%–100%/0%–49%)
Monocyte (median)

0.64

0.29 to 1.40

0.264

Neutrophil:lymphocyte
ratio (median)

2.17

0.75 to 6.25

0.151

Albumin (median)Multivariate model(median)

1.59
3.55

0.68 to 3.69
1.47 to 8.59

0.283
0.005

Each prognostic factor for overall survival was evaluated bymultivariate Cox proportional hazard analysis.

algorithm. Among the deduced 22 immune cell subtypes,
the proportions of CD8+ T cells, follicular helper T cells,and M1 macrophages were significantly abundant in thelow-­risk group (p=0.016, 0.003, and 0.024, respectively), as
shown in figure 2A. The relative abundance of CD8+ T cells
in the PBMCs was significantly positively correlated with
CD4+ memory-­activated T cells and significantly negatively
correlated with memory B cells, monocytes, resting mast
cells, and activated dendritic cells, with a p value of <0.05(figure 2B).
We also examined the correlations between the concentrations of PFAAs/metabolite selected in the multivariate model(arginine, serine, glycine, and quinolinic acid) and theexpressions of immune-­related genes, especially those of T
cell-­related genes. As shown in figure 2C, the plasma concentrations of arginine and serine were positively correlatedwith the expressions of genes of TCR-­mediated signaling
pathways, such as CD3D, CD3E, CD3G, LCK, ZAP70, and LAT,
and T-­cell activation, such as CD6, ICOS, and CD40 Ligand,whereas the quinolinic acid concentration tended to be
negatively correlated with them. Of note, the arginine andserine concentrations also tended to be positively correlatedwith the expressions of genes of CD8 T cells and their activation, such as CD8A, CD8B, IFN-γ, Perforin, and Granzymes,
but not with CD4-­related genes. Furthermore, arginine andserine tended to be negatively correlated with genes related
to M2 macrophages, such as CD209, FCER1G, VSIG4, andCD163, but quinolinic acid tended to be positively correlatedwith them.
Identification and characterization of DEGs between the highrisk and low-risk patients
Through analysis of RNA sequencing data of PBMCs,
492 DEGs were identified between the low-­risk and high-­risk groups. Top 10 GO terms obtained by GO term
8enrichment analysis for the identified DEGs included
the terms ‘T cell activation’, ‘Lymphocyte differentiation’, ‘Nuclear division’, ‘Regulation of T cell activation’, ‘Mitotic nuclear division’, ‘Regulation of leukocyte
cell-­
cell adhesion’, ‘T cell differentiation’, ‘Regulationof nuclear division’, ‘Regulation of mitotic nuclear division’, and ‘T cell proliferation’ (online supplemental
figure 4A). In addition, immune-­related GO terms, suchas ‘Regulation of lymphocyte activation’, ‘Positive regulation of cell cycle’, ‘Macrophage activation’, ‘Regulation ofinterferon-­gamma production’, ‘Regulation of inflammatory response to antigenic stimulus’, and ‘Regulation ofdendritic cell differentiation’ were also obtained by thisanalysis (p<0.05) (online supplemental table 3).
Analysis of enrichment pathways in DEGs by KEGG
identified ‘Staphylococcus aureus infection’, ‘complement
and coagulation cascades’, ‘pertussis’, and ‘cell cycle’ as
upregulated pathways in the high-­risk group, whereas‘NF-­kappa B signaling pathway’, ‘T cell receptor signaling
pathway’, ‘hematopoietic cell lineage’, and ‘primary
immunodeficiency’ were downregulated (p<0.05)(online supplemental figure 4B and online supplemental
table 4). We further evaluated the RNA sequencing data
across different pathways by GSEA between the high-­risk
and low-­risk groups. ‘Complement cascade’, ‘regulationof TLR by endogenous ligand’, ‘interferon alpha beta
signaling’, ‘cell cycle checkpoints’, ‘biological oxidations’like receptor TLR1:TLR2 cascade’ showed aand ‘Toll-­
significantly positive enrichment in the low-­risk group(p<0.05). In contrast, ‘cell–cell junction organization’and ‘antigen activated B-­cell receptor leading to generation of second messengers’ were positively enriched in
the high-­risk group (p<0.05) (online supplemental figure
4C).
Analysis of DEG–AAMGs
Among 492 DEGs identified between the low-­risk andhigh-­risk groups, 12 were amino acid metabolism-­related
genes (DEG–AAMGs), as shown in figure 3A. The high risk
group showed upregulation of 9 DEG–AAMGs, including
SLC1A3, 3-­Hydroxy Anthranilic Acid Dioxygenase (HAAO),
PHGDH, AANAT, ALAS2, FAH, BCAT1, SLC11A1, andGlutamate-­Ammonia Ligase (GLUL), and dowregulationof 3 DEG–AAMGs, including DCT, SLC6A13, and TPH1
(figure 3B). Annotation of these DEG–AMGs by KEGG
enrichment pathways identified ‘biosynthesis of aminoacids’, ‘glysine, serine, and threonine metabolism’,‘biosynthesis of cofactors’, ‘cysteine and methionine
metabolism’, ‘glutamatergic synapse’, ‘arginine biosynthesis’, ‘pantothenate and CoA biosynthsis’, ‘tryptophan
metabolism’, ‘2-­oxocarboxylic acid metabolism’, and‘nitrogen metabolism’ as upregulated pathways in thehigh-­risk group. In contrast, ‘folate biosynthesis’, ‘tyrosine metabolism’, ‘tryptophan metabolism’, ‘synaptic
vesicle cycle’, ‘GABAergic synapse’, ‘melanogenesis’,‘serotonergic synapse’, and ‘tryptophan metabolism’pathways were downregulated in the high-­risk group (p<0.05)(online supplemental figure 5).
Azuma K, et al. J Immunother Cancer 2022;10:e004420. doi:10.1136/jitc-2021-004420

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Table 3 Multivariate analysis of prognostic factors foroverall survival

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Figure 2 Immune cell constitution of PBMCs in the high-­risk and low-­risk groups. (A) Violin plots depicting immune cell
subtypes in PBMCs from the high-­risk and low-­risk groups by the multivariate model. Fractions of immune cells were
deconvoluted by the CIBERSORT algorithm from RNA-­seq data in PBMCs and compared between the high-­risk and low-­risk
groups using Student’s t-­test. Blue, low-­risk group; orange, high-­risk group. (B) Heatmap of Spearman’s correlations among
immune cell subtypes. Red, positive correlation; blue, negative correlation; white, no correlation. (C) Correlations between
immune-­related gene expression in PBMCs and PFAAs/tryptophan metabolites. Heatmap of Spearman’s correlations between
gene expression levels of immune-­related genes and concentrations of 4 PFAAs/metabolite selected in the multivariate
model (arginine, serine, glycine, and quinolinic acid) are shown. The genes were grouped by immune pathways. Red, positive
correlation; blue, negative correlation; white, no correlation. NK, natural killer; PBMC, peripheral blood mononuclear cell.
Azuma K, et al. J Immunother Cancer 2022;10:e004420. doi:10.1136/jitc-2021-004420

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Figure 3 Identification of AAMGs differentially expressed between the high-­risk and low-­risk groups. (A) Venn plot of AAMGs
among the DEGs. (B) Volcano plot of DEGs. AAMGs were labeled in the plot with gene names. Red dots, upregulated genes;
blue dots: downregulated genes; gray dots, stable genes. (C) Kaplan-­Meier estimates of OS in the subgroups stratified by RNA
expression level of three AAMGs, SLC11A1, HAAO, and PHGDH, in PBMCs. Cut-­off values between the high and low groups
were set at the median of gene expression levels. P values (log-­rank test) are shown. (D) Correlations between 12 DEG–AAMGs
in PBMCs and PFAAs/tryptophan metabolites. Heatmap of Spearman’s correlations between expression levels of 12 DEG–
AAMGs and concentrations of 4 PFAAs/metabolites selected in the multivariate model (arginine, serine, glycine, and quinolinicacid) are shown. Red, positive correlation; blue, negative correlation; white, no correlation. (E) Scatter plots of the HAAO
gene expression in PBMCs versus concentrations of quinolinic acid or arginine (left half). Scatter plots of the SLC11A1 gene
expression in PBMCs verus concentrations of serine or arginine (right half). The correlations were evaluated by Spearman’srank correlation coefficient analysis. R indicates correlation coefficient. amino acid metabolism-­related gene; DEG, differentiallyexpressed gene; HAAO, 3-­Hydroxy Anthranilic Acid Dioxygenase; OS, overall survival; PFAA, plasma-­free amino acid.

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DISCUSSION
In the present study, we showed that the optimized multivariate model with PFAAs and metabolites in plasma canstratify the prognosis in patients with NSCLC treated with
ICI. Notably, this multivariate model was a significantly
independent factor for OS. In addition, it was useful for
stratifying the prognosis in the subgroup with higher
L1 expression. Therefore, our finding might help
PD-­develop a novel biomarker for ICI treatment, which could
have different values from current biomarkers, such as
PD-­L1 expression.
Amino acid metabolism in the tumor microenvironment (TME) plays key roles in tumor development andprogression. Tumor cells often exclusively consume
regional nutrients, such as amino acids, for their survival
and compete for them with other surrounding cells, suchas antitumor immune cells. It may thus be noteworthy
that the plasma PFAAs and metabolites before ICI therapy
were significantly correlated with OS. The best multivariate model for stratifying the prognosis of patientstreated with ICI was composed of four PFAAs/metabolite, namely, serine, glycine, arginine, and quinolinic acid.
Among these amino acids, serine and glycine are known
Azuma K, et al. J Immunother Cancer 2022;10:e004420. doi:10.1136/jitc-2021-004420

to form one-­carbon pathway involved in the anabolism
of multiple macromolecular substances, such as proteins,
nucleic acids, lipids and other biological molecules, tosupport tumor growth.13 Arginine is known as substrate ofnitric oxide and might regulate proliferation, cell death
and angiogenesis in cancer cells.14 15 Of note, these aminoacids are also important for metabolism of immune cells
and might substantially contribute to antitumor immunity. For example, extracellular serine was reported to be
one of the key immunometabolites that modulate adaptive immunity by controlling T-­cell proliferative capacity.16
Arginine is also widely recognized as a major contributor
to metabolic reprogramming through control of polyamine synthesis for immune cell proliferation and nitricoxidase synthesis for cytotoxicity. In addition, enhanced
arginine uptake from extracellular sources induces metabolic changes in T cells, including a shift from glycolysis
to oxidative phosphorylation in activated T cells, andpromotes generation of central memory-­like cells withstronger antitumor activity and longer survival capacity.17
Furthermore, infiltrating myeloid-­
derived suppressor
cells expressing arginase were reported to suppress theefficacy of ICI therapy.18 19 Interestingly, in the present
study, the concentrations of plasma arginine and serine
were significantly correlated with the expressions of T
cell-­related genes in PBMCs. Considering their criticalroles in T-­cell survival and activation,16 17 it might be likelythat they regulate T-­cell functions directly.
The metabolism of the tryptophan–kynurenine pathway
might not directly affect tumor cells but may promotetumor progression through modulation of the immunosuppressive microenvironment via multiple mechanisms.
For example, tryptophan-­metabolizing enzymes IDO(indoleamine 2,3-­
dioxygenase) /TDO (tryptophan 2,
3-­dioxygenase) were reported to affect antitumor immunity in various tumors.20 21 In the present study, our Cox
proportional hazard analysis demonstrated that decreasein tryptophan and increase in kynurenine-­derived metabolites, including 3h-­kynurenine, anthranilic acid, andquinolinic acid, were significantly correlated with poorOS, possibly due to impaired antitumor immunity. In
addition, the concentration of quinolinic acid tended tobe negatively correlated with the expressions of T cell-­related genes in PBMCs. Since quinolinic acid is a downstream catabolite of tryptophan, which is known to play a
critical role in T-­cell activation, it might be possible thattryptophan catabolism, which increases quinolinic acid, isassociated with T-­cell suppression and dysfunction. Moreover, since consumption of tryptophan and accumulationof its metabolites are known to activate specific genes,
such as AhR and GCN2, which drive a tolerogenic macrophage phenotype, indicating that they might directly
regulate myeloid cell functions.22 Consistent with our
results, similar metabolic features on tryptophan metabolites were reported to correlate with the prognosis ofpatients treated with ICI.23 24
Antitumor immune cells are suggested to continuously
circulate between the TME and the peripheral blood,
11

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Association between AAMG expression in PBMCs and patient
prognosis
We further examined the relationships between AAMG
expression in PBMCs and OS in the 36 patients treatedwith ICI. Expression levels of 20 AAMG mRNA weresignificantly correlated with the OS; upregulation of
3 AAMGs, VAMP2, PER2, and ACY1, was positively
correlated with OS, whereas upregulation of 17 AAMGs,
UNC13B, SH3BP4, SFXN3, SFXN2, ASL, ASH1L, ACAA2,
PLOD3, ACAT1, PSAT1, ACADS, ALDH3B1, ALDH2 and
HMGCL, SLC11A1, HAAO, and PHGDH, was negatively
correlated with OS (log-­rank test, p<0.05) (online supplemental figure 6 and figure 3C). Notably, SLC11A1, HAAO,
and PHGDH were also selected as DEG–AAMGs in thepreceding analysis.
We also performed Spearman’s correlation coefficient
analysis to investigate the relationships between the
PFAAs/metabolite selected as the components of themultivariate model and the expressions of DEG–AAMGs.
Heatmap of Spearman’s correlations between the expression levels of 12 DEG–AAMGs and concentrations of 4
PFAAs/metabolite, including arginine, serine, glycine,and quinolinic acid, are shown in figure 3D. Of note,among the three DEG–AAMGs with survival significance,
the HAAO expression was positively correlated with theplasma quinolinic acid (R=0.375, p=0.024) and negatively
correlated with the plasma arginine (R=−0.378, p=0.023),
whereas the SLC11A1 expression was negatively correlatedwith the plasma arginine (R=−0.353, p=0.035) and serine(R=−0.353, p=0.035) (figure 3E). However, the expressionof PHGDH gene was not significantly correlated with theplasma glycine (R=−0.183, p=0.285) or serine (R=−0.123
p=0.474) (data not shown).

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12

including PHGDH,35 such negative feedback mechanisms
might prevent direct correlation between the PHGDH
expression and serine/glycine concentrations.
In this study, the following nine genes were further
identified as DEG–AAMGs (figure 3B). BCAT1 is an
enzyme that converts branched-­chain amino acids into
the corresponding branched-­
chain α-keto acids andgenerates glutamate, and was reported to be associatedwith tumor growth and progression. BCAT1 was also
shown to control metabolic reprogramming in activatedhuman macrophages.36 GLUL is an enzyme for de novo
synthesis of glutamine by catalyzing the ATP-­dependent
condensation of glutamate with ammonia. It was reported
to bias macrophages towards an M1-­like phenotype andinhibit tumor metastasis.37 SLC1A3 is a glutamate/aspartate transporter that uses aspartate to support cells in theabsence of extracellular glutamine. Since SLC1A3 expression promotes the synthesis of glutamate and glutamine,tumor cells with high levels of SLC1A3 expression wereresistant to glutamine starvation and SLC1A3 deprivation
retarded the tumor growth.38 ALAS2 is a mitochondrial
enzyme which uses glycine and succinyl-­
CoA to form
5-­aminolevulinic acid (ALA), a crucial precursor in heme
synthesis.39 SLC6A13 belongs to the neurotransmittertransporter family and promotes ALA-­induced accumulation of protoporphyrin IX and photodamage throughALA uptake by cancer cells.40 AANAT and TPH1 are
involved in tryptophan metabolism for melatonin andserotonin synthesis. AANAT modulates the phagocytic
activity,41 whereas TPH1 regulates immunological tolerance.42 FAH43 and DCT44 are mainly involved in tyrosine
metabolism; FAH catalyzes the final step of tyrosine degradation, whereas DCT is a zinc enzyme associated with themelanogenic process of flavonoids. Since the roles ofmost of these genes, including SLC1A3, ALAS2, SLC6A13,
AANAT, TPH1, FAH, and DCT, in immune cells, have notbeen fully elucidated, functional mechanisms of thesegenes remain to be further investigated.
In conclusion, our findings suggested that the profilingof PFAAs and metabolites in plasma might be useful for
stratifying patients who will benefit from ICI treatment.
Nevertheless, the present study had several limitations.
The investigation was conducted in a single-­
centercohort with a relatively small number of patients who
receive anti-­PD-­
1 antibody without concurrent chemotherapy. However, the next phase of clinical trial has beenongoing as a larger multicenter cohort study to further
optimize the multivariate model with PFAAs and metabolites in patients with NSCSC who receive combined
treatment with anti-­PD-­
1 antibody and chemotherapy(jRCT1031190196).
Author affiliations
1
Division of Respirology, Neurology, and Rheumatology, Department of Internal
Medicine, Kurume University School of Medicine, Kurume, Japan2
Molecular Pathology and Genetics Division, Kanagawa Cancer Center ResearchInstitute, Yokohama, Japan
3
Research Institute for Bioscience Products and Fine Chemicals, Ajinomoto Co Inc,
Kawasaki, Japan
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as illustrated in online supplemental figure 7. Thus, theimmune cells in the peripheral blood may reflect thecomposition, activity, and metabolic characteristics of
those within the TME.25 26 Indeed, in the present study,
when the composition of immune cells was examinedby deconvoluting whole transcriptomic data of PBMCs
with the CIBERSORT algorithm, CD8+ T cells that haveprognostic relevance for tumor immunotherapy27 28 weresignificantly increased in the low-­risk group according to
the multivariate model. In addition, the GO term enrichment analysis of the PBMC transcriptome demonstrated
that DEGs identified between the low-­risk and high-­risk
groups were significantly associated with T-­cell proliferation, differentiation, and activation. Furthermore, the
KEGG pathway annotation and GSEA analysis of reactome pathways showed that the pathways associated withproinflammatory immune responses were significantly
enriched in the low-­risk group. Taken together, theseresults suggested that the PBMCs’ transcriptome in thelow-­risk group might reflect the so-­called ‘hot tumor’characteristics in TME.29
Interestingly, the expressions of 2 DEG–AAMGs withprognostic significance, HAAO and SLC11A1, showed weak
but significant correlations with some of PFAA/metabolite concentrations in the present study. The expressionof HAAO was positively correlated with the plasma quinolinic acid concentration. HAAO is an enzyme involvedin the metabolism of tryptophan–kynurenine pathway
and reported to catalyze the synthesis of quinolinicacid.30 Therefore, higher HAAO expression in PBMCs
might directly contribute to higher plasma concentration of quinolinic acid through catabolism of tryptophan. In contrast, the expression of SLC11A1 (NRAMP1)
encoding a proton-­coupled metal ion transporter was
negatively correlated with the plasma arginine concentration. SLC11A1 is expressed in the lysosomal compartmentof macrophages and translocates to the phagolysosome
membrane when activated. Previous reports showedthat SLC11A1 increases the expression of iNOS, an arginine metabolizing enzyme, and eliminates intracellularpathogens.31 Thus, it might be reasonable that higher
SLC11A1 expression in PBMCs directly contributes toreduction of plasma arginine concentration. Based onthese results, high expression of HAAO and SLC11A1 maylead to decrease in tryptophan and arginine, respectively,
resulting in immune cell suppression in the high-­risk
group in our multivariate model. In contrast, another
DEG–AAMG with prognostic significance, Phosphoglycerate Dehydrogenase (PHGDH), is a key enzyme for de novo
serine/glycine synthesis, but the PHGDH expression level
in PBMCs was not significantly correlated with the plasma
glycine or serine in this study. Elevated expression of
PHGDH is reported to be associated with tumor development in various tumors32 and poor prognosis in patientswith cancers, including NSCLC.33 It was also reported to
be involved in polarization and proliferation of M2 macrophage.34 Since deficiency of serine/glycine was reported
to increase the enzmes for serine/glycine synthesis,

Open access4

Acknowledgements The authors express their gratitude to the patients and theirfamilies for participation in this study. The authors thank Kyoko Yamaguchi and
Satomi Kobayashi for their technical support. The supercomputing resource wasprovided by the Human Genome Center, Institute of Medical Science, The Universityof Tokyo (http://sc.hgc.jp/shirokane.html).
Contributors KA, TH, and TTa conceived the study. KA, NM, HI, and TTo contributed
to the sample collection and patient data management. HX, TTa, RK, YK, SKa,
SKi, AK, AI, KM, and YM analyzed the samples. KA, HX, TS, and TTa wrote themanuscript. TH is responsible for the overall content as guarantor.
Funding This study was supported by Ajinomoto Co. and the AMED (grant number
21ae0101076h0003).
Competing interests KA reports receiving personal fees from AstraZeneka, MSD,
Bristol Myers Squibb, Ono Pharmaceutical and Chugai Pharmaceutical. HI reports
receiving personal fees from AstraZeneka, KyowaKirin, AMCO, Ono Pharmaceutical,
and Chugai Pharmaceutical. TTa reports receiving personal fees from AstraZeneka,
Ono Pharmaceutical, and Chugai Pharmaceutical. KM reports receiving personal
fees from AstraZeneka, MSD, Taiho, Boehringer Ingelheim, Ono Pharmaceutical,
and Chugai Pharmaceutical. TS reports receiving personal fees from Bristol Myers
Squibb and Chugai Pharmaceutical. TTo, YK, SKa, SKi, and AI are employees of
Ajinomoto Co. The remaining authors have no conflicts of interest to disclose.
Patient consent for publication Consent obtained directly from patient(s).
Ethics approval This study involves human subjects and was approved by theinstitutional review boards of Kurume University Hospital (study numbers 16249
and 19235 (ancillary study)) in accordance with the provisions of the Declarationof Helsinki. The subjects gave informed consent to participate in the study before
taking part.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data are available upon reasonable request. Data
are available from the corresponding authors upon reasonable demand, with the
exception of absolute values of PFAA and metabolite analysis.
Supplemental material This content has been supplied by the author(s). It has
not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been
peer-­reviewed. Any opinions or recommendations discussed are solely thoseof the author(s) and are not endorsed by BMJ. BMJ disclaims all liability andresponsibility arising from any reliance placed on the content. Where the content
includes any translated material, BMJ does not warrant the accuracy and reliabilityof the translations (including but not limited to local regulations, clinical guidelines,
terminology, drug names and drug dosages), and is not responsible for any error
and/or omissions arising from translation and adaptation or otherwise.
Open access This is an open access article distributed in accordance with theCreative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits
others to copy, redistribute, remix, transform and build upon this work for any
purpose, provided the original work is properly cited, a link to the licence is given,and indication of whether changes were made. See https://creativecommons.org/​
licenses/by/4.0/.
ORCID iDs
Koichi Azuma http://orcid.org/0000-0001-5040-5856
Tetsuro Sasada http://orcid.org/0000-0002-2313-8833

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13J Immunother Cancer: first published as 10.1136/jitc-2021-004420 on 15 May 2022. Downloaded from http://jitc.bmj.com/ on May 15, 2022 by guest. Protected by copyright.

Department of Diagnostic Pathology, Kurume University Hospital, Kurume, Japan
Biostatistics Center, Kurume University School of Medicine, Kurume, Japan
6
Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute,
Yokohama, Japan
7
Division of Respirology, Neurology, and Rheumatology Department of Internal
Medicine, Kurume University School of Medicine, Kurume, Japan
5

Open access

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