Tumor mutation burden in Chinese cancer patients and the underlying driving pathways of high tumor mutation burden across different cancer types

Abstract

Background

Tumor mutation burden (TMB) has an important association with immunotherapy responses. TMB in the Chinese population has not been well established. Finding differences between the Chinese and Caucasian populations and elucidating the underlying biological mechanisms of high TMB might help develop more precise and effective means for TMB and immunotherapy response prediction.

Methods

Chinese cancer patients fresh tissue (n=2,177), formalin-fixed, paraffin-embed (FFPE) specimens (n=3,294), and pleural fluid (n=189) were profiled using a 295- or 520-gene next-generation sequencing (NGS) panel. The association of the TMB status with a series of molecular features and biological pathways was determined using bootstrapping.

Results

TMB, measured by 295- or 520-cancer-related gene panels, was correlated with whole-exome sequencing (WES) TMB based on the in silico simulation in The Cancer Genome Atlas cohort. The median TMB of our data was slightly higher than that from the Foundation Medicine Inc. (FMI) dataset. TMB was also slightly different within the same cancer type between the Chinese and Caucasian population. We discovered that the underlying pathways of TMB status varied greatly and sometimes had an opposite association with TMB across different cancer types. Moreover, we developed a 23-gene and a 16-gene signature to predict TMB prediction for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), respectively, indicating a histology-specific mechanism for driving high-TMB in lung cancer.

Conclusions

TMB varies among different ethnic populations. Our findings extend the knowledge of the underlying biological mechanisms for high TMB and might be helpful for developing more precise and accessible TMB assessment panels and algorithms in more cancer types.

Introduction

High tumor mutation burden (TMB) has been associated with improved response to immune checkpoint inhibitors (ICIs) because elevated TMB increases the odds of generating immunogenic neoantigens (1,2). TMB was revealed to be an independent predictor of responses to ICIs not only in non-small cell lung cancer (NSCLC) (3), but also in small-cell lung cancer (SCLC) (4), melenoma (5), and other varieties of cancer (6). Multiple clinical trials have demonstrated the positive correlation between TMB and response to ICIs. KEYNOTE-001 has shown that in NSCLC patients receiving pembrolizumab, those with higher TMB had an improved overall response rate (ORR) and longer progression-free survival (PFS). TMB has been previously calculated by whole-exome sequencing (WES) (1,7). Nevertheless, its assessment by WES could be substantially limited by its high cost, the lack of deep coverage, and the additional bioinformatics demands. Multiple studies have reported that tatgeted sequencing panels containing coding regions of several hundreds of cancer-related genes can accurately estimate TMB and predict response to immunotherapy (8-11).

Although TMB may be a pan-cancer predictor for immune check point inhibitor, different tumors have different immune features and TMBs (12,13), and their potential driving mechanisms are different (14,15). For instance, deficiency in DNA damage response (DDR) pathway can raise the overall mutation burden in bladder cancer (16). In colon cancer, the mismatch repair (MMR)-deficient tumors were found to have a higher TMB than the MMR-proficient tumor (17). However, in breast cancers, tumors with mutations in BRCA1, a central gene in the homologous recombination pathway, exhibited a greater mutational burden than BRCA1-wt tumors (18). Given these diverse findings, further exploring the distinction of underlying driving pathway between different cancer types may be clinically significant.

Besides the disparity between cancer types, TMB may also vary across different ethnic populations. In NSCLC, for which targeted therapy was first engineered, a huge gap of efficacy was detected between Western populations and East Asian population in 2000s. This can be explained by the fact that East Asian populations harbor a higher percentage of epidermal growth factor receptor (EGFR) mutation (19-22). A similar ethnic diversity in relation to the EGFR mutation and NSCLC may also exist for TMB. However, most of the studies concerning TMB have been conducted in Western populations, and thus the TMB features in Chinese patients have not been well established. This may have great clinical significance for oncologists in the era of immune therapy, especially in China. Furthermore, if we can find a way to predict TMB with fewer combinations of genes, it will reduce the cost of sequencing and provide more convenience for clinicians.

In this study, we examined the TMB landscape of a cohort of 5,660 Chinese cancer patients, spanning 11 cancer types, using either a 295- or a 520-gene NGS panel. We established cancer-specific and histology-specific biological pathways associated with TMB status. In addition, as a proof of concept, an unsupervised algorithm was conducted using stepwise logistic regression to generate TMB-predicting signatures from both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).

Methods

Cohort selection and study design

We reviewed the genomic profiling data of 5,660 cancer patients from the following 9 participating centers: Changzheng Hospital, The Affiliated Hospital of Qingdao University, Fudan University Shanghai Cancer Center, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, The First Affiliated Hospital of Suzhou University, The First Affiliated Hospital of Zhejiang University, Taizhou Central Hospital, Affiliated Hangzhou First People’s Hospital and Eastern Hepatobiliary Surgery Hospital. Samples were collected from April 2015-April 2018. There were 3 samples types: fresh tissue (n=2,177), formalin-fixed, paraffin-embedded (FFPE) (n=3,294) and pleural fluid (n=189), which were profiled in a Clinical Laboratory Improvement Amendments (CLIA)-certified sequencing laboratory (Burning Rock Biotech, Guangzhou, China) using the OncoScreen 295 (n=2,026) or OncoScreenPlus 520 (n=3,634) cancer-related gene panel. Of note, cases with maximal allelic frequency of less than 5% were not enrolled in this cohort. An external cohort consisting of 8,092 samples with WES sequencing data was downloaded from The Cancer Genome Atlas (TCGA) database to evaluate the in silico correlation of TMB using the 295- and 520-gene panels and WES. Eligible patients were histologically assessed according to the latest World Health Organization Criteria.

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by the Ethic Committee of Changzheng Hospital (2017SL016). Written informed consent was obtained from each patient for the use of their specimen.

NGS library preparation and sequencing

Capture-based targeted deep sequencing was performed using the 295- or 520-gene panel, spanning 1.44 and 1.64 Mb of the human genome, respectively. The gene list for each panel was listed in Tables S1 and S2. The detailed NGS library and sequencing protocol preparation was performed as previously described (23). In brief, DNA was fragmented by Covaris M220 focused ultrasonicator (Covaris, Inc., Woburn, MA, USA) followed by end repair, phosphorylation, dA addition, and adaptor ligation for library construction. Then, DNA library was purified by using Agencourt AMPure beads (Beckman Coulter, Fullerton, CA, USA). The quality and the size of the fragments were assessed using Qubit 2.0 fluorimeter with the dsDNA high-sensitivity assay kit (Life Technologies, Carlsbad, CA, USA). Indexed samples were sequenced on Nextseq500 (Illumina, Inc., USA) with paired-end reads.

Table S1. OncoScreen 295 gene list.

ABL1	MUTYH	CDK8	PIK3CA	FANCM	FGFR4	SMAD4	MCL1
AKT1	MYC	CDKN1B	PIK3CG	FAT3	FLT1	SMARCA4	MDM2
AKT2	MYCL	CDKN2A	PIK3R1	FBXW7	FLT3	SMARCB1	MDM4
AKT3	MYCN	CDKN2B	PIK3R2	FGF10	FLT4	SMARCD1	MED12
ALK	MYD88	CDKN2C	PMS2	FGF12	FOXL2	SMO	MEF2B
ALOX12B	NBN	CEBPA	PNRC1	FGF14	GATA1	SOCS1	MEN1
AMER1	NCOR1	CHEK1	PPP2R1A	FGF19	GATA2	SOX10	MET
APC	NF1	CHEK2	DDR2	PRDM1	GATA3	SOX2	MITF
APCDD1	NF2	CHUK	DIS3	PRKAR1A	GID4	SPEN	MLH1
AR	NFE2L2	CIC	DNMT3A	PRKDC	GNA11	SPOP	MPL
ARAF	NFKBIA	CRBN	DOT1L	PRSS8	GNA13	SRC	MRE11A
ARFRP1	NKX2-1	CREBBP	EGFR	PTCH1	GNAQ	STAG2	MSH2
ARID1A	NOTCH1	CRKL	EMSY	PTEN	GNAS	STAT4	MSH6
ARID2	NOTCH2	CRLF2	EP300	PTPN11	ADGRA2	STK11	MTOR
ASXL1	NOTCH3	CSF1R	EPHA3	RAD50	GRIN2A	IRS2	SUFU
ATM	NOTCH4	CTCF	EPHA5	RAD51	GSK3B	JAK1	SYK
ATR	NPM1	CTNNA1	EPHB1	RAD51B	HGF	JAK2	TBX3
ATRX	NRAS	CTNNB1	ERBB2	RAD51C	HLA-A	JAK3	TET2
AURKA	NSD1	CUL4A	ERBB3	RAD51D	HRAS	JUN	TGFBR2
AURKB	NTRK1	CUL4B	ERBB4	RAD52	IDH1	KAT6A	TIPARP
AXL	NTRK2	CYP17A1	ERG	RAD54L	IDH2	KDM5A	TMPRSS2
BACH1	NTRK3	DAXX	ESR1	RAF1	IGF1	KDM5C	TNFAIP3
BAP1	CARD11	NUP93	ETV1	RARA	IGF1R	KDM6A	TNFRSF14
BARD1	CASP8	PAK3	ETV4	RB1	IGF2	KDR	TOP1
BCL2	CBFB	PAK7	ETV5	REL	IKBKE	KEAP1	TP53
BCL2L2	CBL	PALB2	ETV6	RET	IKZF1	KIT	TRRAP
BCL6	CCND1	PARP1	EWSR1	RICTOR	IL7R	KLHL6	TSC1
BCOR	CCND2	PARP2	EZH2	RNF43	INHBA	KMT2A	TSC2
BCORL1	CCND3	PARP3	FAM46C	ROS1	IRF4	KMT2D	TSHR
BCR	CCNE1	PARP4	FANCA	FGF23	RPA1	KRAS	VHL
BLM	CD79A	PAX5	FANCC	FGF3	RPTOR	LMO1	WISP3
BRAF	CD79B	PBRM1	FANCD2	FGF4	RUNX1	LRP1B	WT1
BRCA1	CDC73	PDGFRA	FANCE	FGF6	RUNX1T1	MAP2K1	XPO1
BRCA2	CDH1	PDGFRB	FANCF	FGF7	SETD2	MAP2K2	XRCC3
BRIP1	CDK12	PDK1	FANCG	FGFR1	SF3B1	MAP2K4	ZNF217
BTG1	CDK4	PIK3C2G	FANCI	FGFR2	SH2B3	MAP3K1	ZNF703
BTK	CDK6	PIK3C3	FANCL	FGFR3	SMAD2	MAP3K13

Table S2. OncoScreenPlus 520 gene list.

ABL1	NRAS	CSF1R	FGF23	SMAD3	GREM1	CALR	MST1R
AKT1	NSD1	CTCF	FGF6	SMAD4	GRIN2A	CD276	CUL4B
AKT2	NTHL1	CTNNB1	FGF7	SMARCA4	GRM3	EPCAM	SNCAIP
AKT3	NTRK1	CUL3	H3F3B	SMARCB1	GSK3B	FAS	FYN
ALK	NTRK2	DAXX	HIST1H3F	SMO	GSTM1	TGFBR1	ABL2
AMER1	NTRK3	DDR2	HSD3B1	SOCS1	GSTT1	YAP1	ALOX12B
APC	NUP93	DICER1	MYOD1	SOX2	H3F3A	ZFHX3	STK40
AR	PALB2	DNMT3A	PHOX2B	SOX9	H3F3C	GPS2	TCF3
ARAF	PAK7	DOT1L	SOX10	SPEN	HGF	PIK3R3	MAGI2
ARID1A	PPP6C	EGFR	TMEM127	SPOP	HIST1H1C	ANKRD11	ERCC2
ARID1B	GABRA6	EMSY	BACH1	SPTA1	HIST1H2BD	CRBN	HLA-A
ARID2	ZNRF3	PARK2	BBC3	SRC	HIST1H3A	EIF4A2	PGR
ASXL1	ARID5B	PAX5	CENPA	SRSF2	HIST1H3B	ERCC4	ACVR1B
ATM	HSP90AA1	PBRM1	EP300	STAG2	HIST1H3C	REL	CASP8
ATR	CYLD	PDGFRA	EPHA3	STAT3	HIST1H3D	SHQ1	HDAC2
ATRX	KEL	PDGFRB	EPHA5	STAT5B	HIST1H3E	TET1	PARP2
AURKA	PARP4	PIK3CA	EPHA7	STK11	HIST1H3G	YES1	PIK3CD
AURKB	ZNF217	PIK3CB	EPHB1	SUFU	HIST1H3H	ZRSR2	NOTCH4
AXIN1	FRS2	PIK3CG	ERBB2	SYK	HIST1H3I	EED	LATS2
AXL	BIRC3	PIK3R1	ERBB3	TBX3	HIST1H3J	LYN	PARP3
BAP1	MAX	PIK3R2	ERBB4	TERC	HIST2H3D	PDK1	ASXL2
BARD1	TRAF2	PLCG2	ERCC1	CTLA4	HIST3H3	RAD21	MDC1
BCL2	KAT6A	PMS1	ERG	GNA13	HNF1A	PDPK1	MST1
BCL2L1	STAT5A	PMS2	ERRFI1	SDHAF2	HNF1B	PLK2	FGF12
BCL6	ADGRA2	POLD1	ESR1	RYBP	TERT	ERCC3	QKI
BCOR	RECQL4	POLE	EZH2	SH2D1A	TET2	ERCC5	BMPR1A
BLM	DNMT1	POM121L12	FAM175A	APCDD1	TGFBR2	HRAS	BCORL1
BRAF	ELOC	PPP2R1A	FAM46C	IL10	TNFAIP3	IDH1	PAK1
BRCA1	B2M	PPP2R2A	FANCA	KLF4	TNFRSF14	IDH2	RPS6KB2
BRCA2	RHOA	PRDM1	FANCC	PDCD1	TNFSF11	IGF1R	RPS6KA4
BRD4	IGF1	PRKAR1A	FANCD2	TIPARP	TOP1	IGF2	FGF14
BRIP1	IRF2	PRKDC	FANCE	VTCN1	TP53	IKBKE	PIM1
BTK	ACVR1	PTCH1	FANCF	WISP3	TP63	IKZF1	SH2B3
CARD11	EIF4E	PTEN	FANCG	GATA4	TSC1	IL7R	MAPK3
CBFB	AXIN2	PTPN11	FANCI	GATA6	TSC2	INHBA	TACC3
CBL	SMARCD1	PTPRD	FANCL	GID4	TSHR	INPP4B	MAP3K14
CCND1	CUL4A	RAC1	FAT1	PDCD1LG2	U2AF1	IRF4	SUZ12
MED12	TRAF7	RAD50	FAT3	PPM1D	VEGFA	IRS1	CTNNA1
MEF2B	CHUK	RAD51	FBXW7	PRSS8	VHL	IRS2	MALT1
MEN1	CCND2	RAD51B	FGF19	RAB35	WRN	JAK1	RPA1
MET	CCND3	RAD51C	FGF3	RIT1	WT1	JAK2	PRKCI
MITF	CCNE1	RAD51D	FGF4	XIAP	XPO1	JAK3	RFWD2
MLH1	CD274	RAD52	FGFR1	ARFRP1	XRCC2	JUN	LZTR1
MLH3	CD79A	RAD54L	FGFR2	DCUN1D1	NEB	KDM5A	NCOA3
MPL	CD79B	RAF1	FGFR3	IFNGR1	TRRAP	KDM5C	DIS3
MRE11A	CDC73	RARA	FGFR4	KLHL6	CHD4	KDM6A	FANCM
MSH2	CDH1	RB1	FH	NEGR1	PTPRT	KDR	MGA
MSH3	CDK12	SLIT2	FLCN	VEGFB	PREX2	KEAP1	PARP1
MSH6	CDK4	BCL2L2	FLT1	VEGFC	PIK3C2G	KIT	STAT4
MTOR	CDK6	BTG1	FLT3	XRCC3	PTPRS	KMT2A	PIK3C3
MUTYH	CDK8	CXCR4	RBM10	EIF1AX	TAF1	KMT2C	RANBP2
MYC	CDKN1A	FOXA1	RET	CYP17A1	CHD2	KMT2D	PIK3C2B
MYCL	CDKN1B	HIST2H3C	RICTOR	FLT4	NCOR1	KRAS	TOP2A
MYCN	CDKN1C	HOXB13	RNF43	FOXL2	INSR	LATS1	ATF1
MYD88	CDKN2A	ID3	ROS1	FOXO1	RASA1	LMO1	EPHA2
NBN	CDKN2B	INHA	RPTOR	FOXP1	INPP4A	LRP1B	FCGR2B
NF1	CDKN2C	NKX3-1	RUNX1	FUBP1	DNMT3B	MAP2K1	HDAC1
NF2	CEBPA	PMAIP1	SDHA	GALNT12	CSF3R	MAP2K2	HDAC4
NFE2L2	CHD1	PNRC1	SDHB	GATA1	TCF7L2	MAP2K4	NR4A3
NFKBIA	CHEK1	SOX17	SDHC	GATA2	RUNX1T1	MAP3K1	PTK2
NKX2-1	CHEK2	ZBTB2	SDHD	GATA3	E2F3	MCL1	TMPRSS2
NOTCH1	CIC	ZNF703	SETD2	GLI1	EGFL7	MDM2	BCR
NOTCH2	CREBBP	BCL10	SF3B1	GNA11	ICOSLG	MDM4	EWSR1
NOTCH3	CRKL	DNAJB1	SLX4	GNAQ	MAPK1	MAP3K13	NRG1
NPM1	CRLF2	FGF10	SMAD2	GNAS	RHEB	PAK3	BCL2L11

TMB calculation and microsatellite instability (MSI) assessment

For sequencing data from the 295- or 520-gene panel, the somatic alterations in exons of coding regions and the adjacent 20-bp length of both upstream and downstream sequences were included in the calculation of TMB. The copy number variation and fusion were not counted. Alterations in the mutations of EGFR (exon 18–21) and ALK (amino acid 1,116–1,382) kinase domains were also excluded from the TMB calculation. A maximum allelic fraction (max.AF) of 5% was defined as the detection limit for TMB assessment using in-house validation, and samples with max.AF <5% were excluded. MSI status was determined as previously described (24). Additionally, homologous recombination repair (HRR) and DDR were defined as any non-synonymous mutation in the coding region of 16 and 87 genes, respectively. Detailed gene lists are provided in Table S3.

Table S3. DDR and HRR gene lists.

DDR gene list			HRR gene list
MLH1	XPC	LIG4	BRCA1
MSH2	MSH3	POLM	BRCA2
MSH6	POLQ	XRCC3	ATM
PMS1	APEX1		BRIP1
PMS2	APEX2		PALB2
ERCC2	FEN1		RAD51C
ERCC3	TDG		BARD1
ERCC4	TDP1		CDK12
ERCC5	UNG		CHEK1
BRCA1	POLB		CHEK2
MRE11A	ATRIP		FANCL
NBN	RNMT		PPP2R2A
RAD50	TOPBP1		RAD51B
RAD51	ALKBH2		RAD51D
RAD51B	ERCC6		RAD54L
RAD51D	CUL5		FANCI
RAD52	POLN
RAD54L	EXO1
BRCA2	REV1
BRIP1	MLH3
FANCA	SLX1A
FANCC	XRCC5
PALB2	UBE2T
RAD51C	GEN1
BLM	TREX1
ATM	ALKBH3
ATR	MUS81
CHEK1	POLE3
CHEK2	REV3L
MDC1	TP53BP1
POLE	SHPRH
MUTYH	NHEJ1
PARP1	XRCC4
RECQL4	RBBP8
MGMT	PRKDC
BARD1	SHFM1
ERCC1	FANCB
FANCD2	EME1
FANCI	TOP3A
FANCL	XRCC2
FANCM	POLL
XPA	XRCC6

DDR, DNA damage response; HRR, homologous recombination repair.

Analysis of the correlation of underlying pathways and TMB

To compute the significance of the correlation of each pathway with TMB, the patients were divided into two sub-groups: one group included those with any mutation in the specific pathway, and the other group included those without any such mutation. The ratio of the mean TMB of the patients with and without mutations in this pathway was calculated as the main statistical indicator. Next, regions with the same size covered by all genes from each pathway were randomly selected from our panel with 1,000 repetitions to simulate the distribution of the statistic and compute the significance, while controlling for bias in which a high-TMB sample could elevate the number of mutations among any set of genes. In each simulation, patients were also divided into two sub-groups mutated or non-mutated, based on the mutation status of the randomly selected regions, and the mean TMB ratios of these two groups were also calculated.

Gene signature development for TMB prediction

A machine learning algorithm was used in the cohorts with LUAD and LUSC to construct TMB prediction models. Samples of 300 patients with LUAD and 100 patients with LUSC were selected randomly from the entire cohort as independent test sets. The remaining samples, utilized as training sets, were used to establish the TMB class prediction model. To select the most predictive genes, a t-test was employed firstly in the training set to find the genes related to TMB as candidate genes. Then, the CfsSubsetEval attribute evaluator and the BestFirst search method of WEKA software (version 3.8) were used for feature selection (25). The predictive capability of each attribute and the degree of redundancy between two different attributes were measured using the CfsSubsetEval attribute evaluator. Furthermore, a set of attributes with a high correlation and low-coupling was generated. The BestFirst search method searched the feature subset space through a greedy hill-climbing strategy augmented with a backtracking facility. Next, to avoid over-fitting, a ten-fold cross-validation was utilized in the feature selection procedure. Considering the convenience of clinical application, logistic regression was used to establish the TMB class prediction model by gene features. To evaluate the performance of the model, both ten-fold cross-validation of the training dataset and independent test datasets were utilized.

Statistical analysis

All data, except for the feature selection step of machine learning, were analyzed using Software R (Version 3.4.0). The correlation between TMB (as calculated by the 295- and 520-gene panels) and WES was evaluated by linear regression. Wilcoxon signed-rank test was used to compare the mutation loads between the age groups among the TMB-high, -medium, and -low patients. Comparisons between the mutation burden in male and female patients, MSI-H and microsatellite-stable (MSS) patients, DDR deficient and DDR proficient patients, and HRR deficient and HRR proficient patients were also performed using the Wilcoxon signed-rank test. For all statistical tests, a P value <0.05 was considered statistically significant.

Results

The landscape of TMB across different cancer types in the Chinese population

This cohort contained 1,996 (35.3%) females and 2,370 (41.9%) males, and gender information of 1,294 (22.9%) cases was unavailable. Median age of these patients was 58 years, ranging from 39 to 94 years (Table 1). For subsequent analyses, patients of this cohort with 11 distinct cancer and histology types, were classified into the following 3 main types on the basis of tumor origin and evolution: LUAD (1,847/5,660, 32.6%), colorectal cancer (548/5,660, 9.7%), and LUSCs (474/5,660, 8.4%). Other cancer types included breast cancer (466/5,660, 8.2%), gastrointestinal cancer (261/5,660, 4.6%), hepatobiliary cancer (154/5,660, 2.7%), etc. The last cancer type group, “others” (n=1,526/5,660, 27.0%), included cancer types containing less than 50 unique specimens (n=417), lung cancers except for LUAD and LUSC (n=899), and cases with unknown cancer types (n=210).

Table 1. Patient characteristics.

Patient characteristics	n	%
Total	5,660
Gender
Female	1,996	35.3
Male	2,370	41.9
Unknown	1,294	22.9
Age (y)
Median	58	–
Range	39–94	–
Tumor type
Lung adenocarcinoma	1,847	32.6
Colorectal cancer	548	9.7
Lung squamous cell carcinomas	474	8.4
Breast cancer	466	8.2
Gastrointestinal cancer	261	4.6
Hepatobiliary cancer	154	2.7
Sarcoma	123	2.2
Ovarian cancer	122	2.2
Pancreatic cancer	87	1.5
Kidney cancer	52	0.9
Others	1,526	27.0

Detailed panel information is presented in Figure S1A. The TMB, assessed by the 295- and 520-gene panels and WES closely correlated with each other (295-gene panel vs. WES, R2 =0.969; 520-gene panel vs. WES, R2 =0.975; 295- vs. 520-gene panel, R2 =0.993; Figure S1B,C,D). These results indicated that the comprehensive genomic profiling using 295- and 520-gene panels can accurately reveal the actual mutation burden.

We performed comparative mutation burden analysis between our Chinese study cohort and a larger cohort (over 100,000 samples) reported by Foundation Medicine Inc. (FMI) (8). In our cohort, the TMB distribution was highly variable between and within cancer classes, ranging from 0 to 723.8 mutations/Mb, with a median TMB of 5.6 mutations/Mb. The median TMB was slightly higher than that from the FMI dataset, which was 3.6 mutations/Mb. Overall, 5.4% (n=305) of the patients had a TMB higher than 20 mutations/Mb, 16.5% (n=936) cases had a TMB between 10 and 20 mutations/Mb, and 78.1% (n=4,439) cases had a TMB of less than 10 mutations/Mb in our cohort.

Among all the cancer groups, sarcomas had the lowest mutation burden (median TMB 2.4 mutations/Mb) in our cohort, which agreed well with the FMI results (median of each sarcoma subtypes ranged from 1.7 to 3.3 mutations/Mb). The median TMB of breast cancer ranked second in our cancer groups in terms of TMB from low to high and coincided with that of the FMI population (median of each breast cancer subtypes ranged from 2.7 to 3.8 mutations/Mb). As to ovarian cancer, the median TMB in our cohort was 4.1 mutation/Mb, and the range of median TMB for each ovarian cancer subtype in the FMI dataset was 1.8–3.6 mutation/Mb. We found that the median TMB of hepatobiliary cancer, kidney cancer, and pancreatic cancer was the same in our cohort (4.8 mutations/Mb), and higher than that in the FMI dataset (hepatobiliary cancer median =2.5–3.6 mutations/Mb; kidney cancer median = from 2.5–5.4 mutations/Mb; pancreatic cancer median =1.8–2.7 mutations/Mb). In gastrointestinal cancer and colorectal cancer, the median TMB was 5.6 and 7.1 mutations/Mb, respectively, higher than those of the FMI population (gastrointestinal cancer median =0.9–5.0 mutations/Mb; colorectal cancer median 3.6–5.9 mutations/Mb). In addition, cancers related to chronic mutagen exposures such as lung cancers exhibited greater hyper-mutation than other cancer groups in our cohort. Within lung cancers, LUSC was more highly mutated than LUAD (median 10.2 vs. 5.1 mutations/Mb), and consistent with conclusions from the FMI population (median 9.0 vs. 6.3 mutations/Mb) (Figure 1A,B).

Figure 1.

Landscape of the tumor mutation burden of 5,660 cancer patients across different cancer types. (A) Comparative tumor mutation burden (TMB) analysis between our study cohort and the FMI cohort. Orange bars indicate our study cohort. and green bars represent the FMI population. (B) Landscape of TMB in our cohort. The top table lists the patient number and median mutation burden of cancer patients grouped on the basis of different cancer types. The middle boxplots display the landscape of TMB in different cancer types. A single point indicates an individual patient. The specific proportion of TMB-high, TMB-medium, and TMB-low in different cancer types are indicated by different colors. LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; TMB, tumor mutation burden; FMI, Foundation Medicine Inc.

Association between TMB and demographic/molecular features

TMB-medium (63 years) and high groups (63 years) were significantly older than the TMB-low group (56 years; P<0.001, Wilcoxon signed-rank test; n=4,328; Figure 2A). This phenomenon was also observed in the lung cancer subpopulation (high TMB, median age =65 years; medium TMB, 63 years; low TMB, 59 years; P<0.001, n=1,801; Figure 2B), which was the major tumor type in this study. Furthermore, our analysis revealed that male patients more commonly correlated with higher TMB than the female patients, with statistical significance (median TMB 6.3 vs. 4.0 mutations/Mb, P<0.001, n=4,366; Figure 2C), in both the whole cohort and the lung cancers group (median TMB 7.1 vs. 4.0 mutations/Mb, P<0.001, n=1,810; Figure 2D).

Figure 2.

Association between TMB and patient demographics and molecular patterns. (A) The variations in the TMB status were correlated with the differences in age in the whole cohort, with statistically significant differences (Wilcoxon test); (B) high TMB was associated with old age in lung cancer patients (Wilcoxon test). Male patients were more prone to having high TMB than females in both the whole cohort (C) and lung cancer subgroups (D) (Wilcoxon test); (E) microsatellite instability-high commonly indicated high TMB (Wilcoxon test); (F) DDR deficiency and (G) HRR deficiency were both correlated with high TMB (Wilcoxon test). TMB, tumor mutation burden; DDR, DNA damage response.

We further established that the MSI-high patients usually had a higher TMB than the MSS patients (median TMB 71.4 vs. 5.1 mutations/Mb, P<0.001, n=4,513, Figure 2E). Alterations in DDR occurred in all 11 cancer type groups, with alteration frequencies ranging from 26.4% (23/87) in pancreatic cancer to 57.8% (274/474) in LUSC. We observed that DDR-deficient patients had a significantly higher TMB than the DDR-proficient patients (median TMB 7.9 vs. 4.1 mutations/Mb, P<0.001, n=5,660, Figure 2F).

Similar to DDR, HRR alterations were identified in the patients of all 11 cancer type groups, with a minimal alteration frequency of 11.5% (6/52) in kidney cancer and a maximal frequency of 34.5% (161/466) in breast cancer. HRR-deficient patients had a significantly higher TMB than HRR-proficient patients (median TMB 8.2 vs. 4.8 mutations/Mb, P<0.001, n=5,660, Figure 2G).

Underlying driving pathways of high TMB across different cancer types

We investigated the distribution of mutations across Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in different cancer groups (Figure 3A). The minimal percentage of mutated cases was observed in the MMR pathway of pancreatic cancer (0/87, 0%), whereas the maximal percentage occurred in the PI3K-Akt signaling pathway of colorectal cancer (537/548, 98.0%).

Figure 3.

Association of mutated pathways and TMB demonstrated in a heatmap. (A) Percentage of the mutated cases in each pathway across the different cancer type groups. The mutation percentages are colored as specified above; (B) correlation analysis of the underlying pathways and the TMB status across different cancer types. The different cancer types are located in the bottom category and the different pathways are located in the left category. The positive and negative correlations between the pathways and the TMB status are marked in red and green, respectively. *, indicates P<0.05; **, indicates P<0.01; and ***, indicates P<0.001. TMB, tumor mutation burden;

Some of the pathways displayed significant association with TMB status in different cancer groups, but no pathway had a universal association with TMB. Moreover, we observed that an alteration in an identical pathway but in different cancer groups may indicate an opposite direction of the TMB status (Figure 3B, Figure S2).

Figure S2.

Underlying driving pathways of high TMB across different cancer types. TMB, tumor mutation burden.

TMB predictive signature (TPS) development

Molecular signatures consisting of 23 and 16 gene features were derived for TMB status prediction in LUAD and LUSC, respectively (Figure 4A). In LUAD, 22 gene features were positively correlated with the TMB status, with a correlation coefficient value ranging from 0.34 for ATR to 1.63 for LRP1B. Only EGFR (oncogenic driver variants) was negatively correlated to TMB (correlation coefficient =−1.13). In LUSC, all 16 identified gene features were positively associated with TMB. Among them, KMT2A was the most highly correlated with the TMB status, with a correlation coefficient of 1.67, followed by TP53 (1.60) and RUNX1T1 (1.59).

Figure 4.

Gene signature for TMB prediction in non-small cell lung cancer. (A) Correlation coefficient of TPS for TMB prediction in LUAD and LUSC. The red column indicates a positive correlation, whereas the blue column indicates a negative correlation; (B,C) ten-fold cross-validation demonstrated TMB-prediction accuracy and robustness of TPS in ROC curves in both LUAD and LUSC. AUC, area under the curve; TMB, tumor mutation burden; TPS, TMB predictive signature; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma.

The TMB predicted by TPS was in remarkable agreement with the TMB directly calculated by the NGS panels, as measured by area under the curve (AUC) (LUAD, AUC =89.3%, Figure 4B; LUSC, AUC =86.5%, Figure 4C) and seven other parameters (Table S4) in both NSCLC subtypes.

Table S4. Performance of TMB prediction of TPS in LUAD and LUSC.

	LUAD			LUSC
	Training cross-validation	Independent test		Training cross-validation	Independent test
Sensitivity	62.7%	63.6%		77.7%	82.2%
Specificity	95.2%	95.3%		77.2%	76.6%
PPV	79.4%	79.4%		78.2%	76.9%
NPV	89.7%	90.3%		76.7%	81.9%
Accuracy	87.8%	88.4%		77.5%	79.3%
MCC	63.2%	64.1%		54.9%	58.8%
F1-score	70.1%	70.6%		77.9%	79.5%
AUC	90.7%	89.3%		85.1%	86.5%

TMB, tumor mutation burden; TPS, TMB predictive signature; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; PPV, positive predictive value; NPV, negative predictive value; MCC, Matthews correlation coefficient ; AUC, area under the curve.

Discussion

We characterized the landscape of TMB in a cohort of 5,660 Chinese cancer patients across 11 cancer groups. To our knowledge, our cohort is the largest reported Chinese cohort concerning TMB in a pan-cancer population. We observed a rich variation in mutational burden across and within cancer types, which was consistent with previous studies (1,7,8). Patients with high TMB can be identified in nearly all cancer types, implying that patients with any cancer types may have potentially benefit from immunotherapy. In our study cohort, the median TMB of several tumor types was higher than that of the FMI dataset. Several factors can account for these results including but not limited to the difference in ethnicity, age, stage, line of treatment, cohort size, and TMB calculation algorithm.

Numerous previous studies have explored the demographic and molecular features associated with TMB and yielded conflicting findings. A recent study in a Chinese population reported the absence of a correlation between TMB and age or gender, but only included 16 adolescent patients (9). However, an investigation in a Caucasian population established that a high TMB was related to older age, but no difference in the median TMB existed between female and male patients (8). In our cohort, the higher TMB was correlated with older age and male gender. We also revealed that MSI-high, DDR, and HRR deficiency commonly indicated a higher TMB than MSS, DDR proficiency, and HRR proficiency, which is consistent with the findings of previous studies (8).

Increasing evidence suggests that the underlying TMB-associated biological mechanisms vary across different cancer types. DDR deficiency leads to a high TMB in bladder cancer, whereas MMR deficiency leads to hypermutation in colon cancer (16,17). Here, we estimated the association of 26 crucial biological pathways and the TMB status in 11 tumor type groups. Besides the pathways related to genomic instability and DNA repair, such as MMR, HRR, and DDR, signaling pathways were also included in our analysis. The correlation between TMB and the biological pathways was found to be both cancer- and histology-specific. LUSC is characterized by a high mutation burden and marked genomic complexity (26). There are frequent alterations of CDKN2A, RB1, and AKT in LUSC, which are involved in the following pathways: cell cycle control, p53 signaling pathway, apoptosis, PI3K-Akt pathway, central carbon metabolism, and MAPK signaling pathway (27). The frequent alterations in these pathways in LUSC are the potential underlying biological basis for a high TMB, which is consistent with our results that all the above-mentioned pathways are correlated with a high TMB in LUSC.

Notch signaling pathway was correlated with a low-TMB status in pancreatic cancer. This finding is in agreement with those of previous studies reporting that aberrant Notch signaling was involved in tumor initiation and tumor maintenance in pancreatic cancer (28,29), and patients with pancreatic cancer commonly had a low TMB (7,26). Nevertheless, it is worth noting that we have not definitively demonstrated the causality between mutated pathways and the mutation burden.

Efforts have been previously made to identify gene alterations associated with an increased TMB (10). Herein, we generated 23- and 16-gene signatures in LUAD and LUSC, respectively, to establish the TMB, reaching an accuracy of 88.4% (LUAD) and 79.3% (LUSC), respectively. To date, these are the smallest gene sets reported for TMB prediction.

Conclusions

This study is the largest pan-cancer NGS sequencing cohort reported in a Chinese population to date. In this study, using the 295- and 520-gene NGS panels, we produced a TMB estimation which strongly correlated with those calculated by WES. Using our targeted sequencing panel, we revealed the diversity of TMB between the Chinese and Caucasian populations, identified drivers and predictors of TMB status, and found highly diverse patterns across different cancer types. Moreover, gene signatures consisting of 23 and 16 genes were derived for TMB status prediction in LUAD and LUSC, respectively, with only 12 genes shared by both subtypes, suggesting that the two NSCLC histological subtypes possess distinct underlying mechanisms for induction of the TMB status.

Our findings extend the knowledge of the diversity across different ethnicities and reveal the underlying biological mechanisms for high TMB. These results might be clinically significant, especially for physicians in China, and may be helpful for developing more precise and accessible TMB assessment panels and algorithms in more cancer types.

Figure S1.

Accuracy of the comprehensive genomic profiling panel (295- and 520-cancer-related-gene panels) for assessment of the tumor mutation burden.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by the Ethic Committee of Changzheng Hospital (2017SL016). Written informed consent was obtained from each patient for the use of their specimen.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.