Abstract
Gastric cancer is among the most common malignancies worldwide, characterized by geographical, epidemiological and histological heterogeneity. Here, we report an extensive, multiancestral landscape of driver events in gastric cancer, involving 1,335 cases. Seventy-seven significantly mutated genes (SMGs) were identified, including ARHGAP5 and TRIM49C. We also identified subtype-specific drivers, including PIGR and SOX9, which were enriched in the diffuse subtype of the disease. SMGs also varied according to Epstein–Barr virus infection status and ancestry. Non-protein-truncating CDH1 mutations, which are characterized by in-frame splicing alterations, targeted localized extracellular domains and uniquely occurred in sporadic diffuse-type cases. In patients with gastric cancer with East Asian ancestry, our data suggested a link between alcohol consumption or metabolism and the development of RHOA mutations. Moreover, mutations with potential roles in immune evasion were identified. Overall, these data provide comprehensive insights into the molecular landscape of gastric cancer across various subtypes and ancestries.
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Data availability
All data needed to evaluate the conclusions in this paper are presented in this paper or the Supplementary Materials or are available at the following repositories.
For the Japanese cohort, WGS/WES/RNA-seq data (n = 697) from the International Cancer Genome Consortium (ICGC) Japanese gastric cancer cohort are available at the European Genome-phenome Archive (EGA) with the accession number EGAD00001004051 and EGAD00001008610 and the Japanese Genotype-phenotype Archive with the accession codes JGAS000228 (https://humandbs.biosciencedbc.jp/en/hum0227-v1) and JGAS000229 (https://humandbs.biosciencedbc.jp/en/hum0226-v1). These data (JGAS000228 and JGAS000229) are under controlled access because they are personally identifiable data defined by Japan’s Personal Information Protection Law. For use, approval is required at the review by the NBDC Human Data Review Board. Data users shall apply for data use in accordance with the data use application procedures (https://humandbs.biosciencedbc.jp/en/data-use).
For the Singaporean cohort, WES data (n = 49) are deposited at the EGA with the accession number EGAD00001006957. These data are under controlled access because they are personally identifiable data under Singapore’s Ministry of Health HIM (Health Instruction Manual) guidelines, where WES is classified as Restricted (Sensitive, High) information that requires access controls.
For the Chinese cohort, WGS/WES data (n = 169) are available at the EGA with accession numbers EGAS00001000597 (ref. 13) and EGAS00001001056 (ref. 14).
For the Vietnamese cohort, WES data (n = 48) are available at the EGA with the accession number EGAS00001000736 (ref. 15).
For the South Korean cohort, WES data (n = 52) are available at the European Nucleotide Archive database with accession number PRJEB10531 (ref. 16).
For the TCGA cohort (multiancestry), WES/RNA-seq data (n = 442) are available at dbGAP with the accession number phs000178 (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000178.v11.p8) (ref. 9) (https://portal.gdc.cancer.gov/projects/TCGA-STAD).
Code availability
We used publicly available software for the analyses. All software used in this study are described in Methods and the accompanying Reporting Summary.
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Acknowledgements
We thank all the patients who participated in this study. We also thank T. Taniguchi for sample collection and pathological advice; T. Kiyono for providing HGEC10-A2 cells; H. Chikuta, E. Arakawa, K. Shiina, and H. Meguro for expert technical assistance; and T. Umeda, S. Fukuda, T. Fujita, and E. Furukawa for data analysis. Computations were performed on the SHIROKANE supercomputer at the Institute of Medical Science, University of Tokyo. This work was supported by grants from the Practical Research Project for Innovative Cancer Control from the Japan Agency for Medical Research and Development (AMED) (JP20ck0106547h0001 to T.S. and H. Aburatani; JP20cm0106551 to S.I.), National Cancer Center Research and Development Fund (2020-A-7 to T.S. and Y. Shiraishi), and JST CREST (to M.K.).
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S.I., P.T., H. Aburatani and T.S. designed the study. H.R., C.S., S.S., H. Katai, H. Abe, S.N., T.M., E.S., T.O., Y.R., K.G.Y., J.S., K.S., R.S., Y. Seto, T.U. and A.N. contributed to sample acquisition and pathological evaluation. F.H., A.F., K.T. and Y.A. managed library preparation and sequencing. M.S.A., Y. Shiraishi, D.K., H.N., A.S., N.H., S. Yamamoto, H.O., H. Katoh, K.C., N.I., G.N., H.U. and Y.T. analyzed the data. Y.T., M.S.A., Y. Shiraishi, D.K., S. Yachida, M.K., S.I., H. Aburatani and T.S. interpreted the data. Y.T., M.S.A., Y. Shiraishi, D.K., P.T., S.I., H. Aburatani and T.S. wrote the manuscript. All authors critically reviewed the manuscript.
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Extended data
Extended Data Fig. 1 Experimental design.
(a) Table of the experimental design. (b) Principal-component analysis (PCA) of germline variations in the samples analyzed in this study was performed using the EthSEQ R package. The samples were grouped into five ancestries based on 96,442 single-nucleotide polymorphisms. EUR, European ancestry; EAS, East Asian ancestry; AMR, Admixed American ancestry; SAS, South Asian ancestry; AFR, African ancestry.
Extended Data Fig. 2 Landscape of somatic alterations in gastric cancer.
(a) One hundred and seventy-six microsatellite instability (MSI) and four non-MSI tumors in hypermutated cases. The number of coding indels differed between MSI and non-MSI tumors. Of the four non-MSI tumors, one had a POLE V411L mutation and a dominant POLE mutational signature (SBS10), while two had a dominant SBS17 mutational signature. Tumors with inactivating mutations in mismatch repair (MMR) genes (MLH1/3, MSH2/3/6, and PMS1/2) and MLH1 promoter hypermethylation are shown in green and yellow, respectively. The frequencies of tumors with MMR gene inactivation are shown in Supplementary Table 27. (b) Changes in copy number in gastric cancer. GISTIC analysis identified significant recurrent copy-number alterations. (c) Landscape of the SMGs in non-hypermutated tumors. The histogram of the left shows the number of alterations, and the panel on the right shows the altered cases for each SMG. Color coding indicates the type of alteration. SMGs with q value <0.1 are shown. Genes were sorted according to their q-values. (d) Validation of somatic mutations in TRIM49/49B/49 C. We performed deep genomic sequence analyses of S327R/G/C mutations in TRIM49/49B/49 C using the Illumina PCR amplicon workflow for ten paired DNA samples. See Supplementary information for details. (e) Number and proportion of patients with actionable genes. Evidence levels A and B represent evidence levels A, which has a validated association (that is, proven/consensus association in human medicine), and B, which has clinical evidence (that is, clinical trial or other primary patient data supports association), defined by CIViC21 and equivalent levels in OncoKB22 and CGI20 (see Methods), respectively. On- and off-labels indicate evidence supporting treatment in gastric cancer and different cancer types, respectively. Mutations with the highest level of evidence for each gene were counted. Of the total patients, 9.6% of patients had a mutation with the highest evidence level A for drugs applicable to gastric cancer. (f) Patients with actionable genes were classified into non-hypermutated and hypermutated cases and diffuse and intestinal types. Actionable mutations were detected more frequently in hypermutated cases (50.6%) than in non-hypermutated cases (20.5%). Among the diffuse-type cases, 14.8% harbored actionable mutations.
Extended Data Fig. 3 Clonality and positive selection of SMGs in gastric cancer.
Landscape of SMG clonality and positive selection in (a) non-hypermutated and (b) hypermutated cases. The median of the adjusted variant allele frequencies (aVAFs) of each SMG indicates clonality on the horizontal axis. The q value (-log10 q value) indicates the degree of positive selection on the vertical axis. Genes with activating and inactivating mutations are indicated in red and blue, respectively. (c) Distribution of aVAF of the B2M gene in non-hypermutated and hypermutated cases. High (>0.4) B2M aVAF was more frequent in hypermutated cases. (d) Degree of positive selection (denoted by q value of SMG tests) of 29 non-long-tail SMGs (altered in ≥3% of cases) and 39 long-tail SMGs (altered in <3% of cases) in non-hypermutated cases. The types of alterations and q-values are shown. The solid black line indicates the cutoff (altered in 3% of cases) between non-long and long-tail SMGs. (e) Molecular pathological features of RB1-mutated cases. A fraction of RB1-mutated cases showed higher expression of neuroendocrine markers (ASCL1, CHGA, NCAM, and SYP) (left). Boxplots represent the median and 25th and 75th percentiles of the data. Whiskers represent the highest and lowest values within 1.5 times the interquartile range of the boxplot. Histologically, RB1-mutated cases with high neuroendocrine marker expression showed densely packed cells with a high nuclear-cytoplasmic ratio as high-grade neuroendocrine features (right). The scale bar indicates 250 µm.
Extended Data Fig. 4 Actionable transcriptomic alterations in gastric cancer.
(a) Mutually exclusive distribution of RHO-GAP fusions, such as CLDN18-ARHGAP26, and CDH1/RHOA/ARHGAP5 mutations in non-hypermutated cases (top). Mutually exclusive distribution of WNT pathway (APC, CTNNB1, AXIN1/2, and RNF43) mutations and RSPO3 fusions in non-hypermutated cases (bottom). HD: Homozygous deletion. (b) RNA-seq read depth of typical cases with aberrant 3′ UTRs of PD-L1, PD-L2, and FGFR2. Normalized read depths of the 3′ UTR of aberrant cases (red dotted circles) were fully or partially lower than those of non-aberrant cases. Decreased 3′ UTR expression was also observed in cases with gene fusions. RNA-seq read depths were normalized by the maximum depth in coding exons for each case. (c) High expression of the coding exons of PD-L1/2 and FGFR2 and reduced expression of the 3′ UTR exon of the genes. The horizontal axis indicates the log10 of gene expression (FPKM) of the coding exons. The vertical axis indicates the log10 of the ratio of gene expression (FPKM) between coding and 3′ UTR exons. Each broken line indicates a threshold. Cases that were above the thresholds of both the horizontal and vertical axes were considered aberrant expressions. Cases with 3′ UTR aberrations without gene fusion are marked with red dots. Cases with gene fusion are marked with orange dots. (d) Comparison of coding expression between gene fusion-positive and fusion-negative cases. Boxplots represent the median and 25th and 75th percentiles of the data. Whiskers represent the highest and lowest values within 1.5 times the interquartile range of the boxplot. The P-values were calculated using two-sided Wilcoxon rank-sum tests. Gene fusion-positive cases showed significantly higher expression levels.
Extended Data Fig. 5 Splicing alterations in gastric cancer.
(a) Frequency of splicing alterations of SMGs in gastric cancer. (b) Examples of novel recurrent and in-frame splicing alterations in gastric cancer. (c) RHOA V33-Q52 deletion localized at the binding interface of RHOA effector proteins. The deleted segment (black) contains the Y42 position (pink-colored residue), which is a mutation hotspot in diffuse-type gastric cancer, and overlaps with a core effector domain that forms a binding interface with RHOA effector proteins, as exemplified by ROCK1 (sky blue). The three-dimensional structure was made from PDB:1S1C, and RHOA protein, GTP analog, and Mg2+ sphere are shown in green, beige, and light gray, respectively. Left: lateral view. Right: top view.
Extended Data Fig. 6 CDH1 mutations in diffuse-type gastric cancer.
(a) Interaction of amino acid residues targeted by non-protein truncating mutations in four functional regions (FR). FR_1, trans-dimerization interface; FR_2, hydrophobic core of the EC1 domain; FR_3, Ca2+ binding pocket at EC1-EC2 boundary; and FR_4, Ca2+ binding pocket at EC2-EC3 boundary. The model representation and color codes are the same as those shown in Fig. 5b. The ribbon model, colored pale green, represents the dimerization partner. The functional regions were defined by CDH1 crystallographic analysis, and the CDH1 model structures were constructed using the homology modeling method. Amino acids compositions in each region are as follows; trans-dimerization interface: {D155, W156, V157, I158, P159, P160, I161, N174, L175, V176, I178, ex4(A3), K179, N181, Y190, S232, A234, E243, D244, M246}; Ca2+ binding pocket at EC1-EC2 boundary: { E165, D221, E223, Y228, T229, ex5(A3), D254, Q255, N256, D257, N258, D288, D290, N297, D349}; Ca2+ binding pocket at EC2-EC3 boundary: {E273, T330, D334, E336, ex8(A5), D367, T368, N369, D370, N371, P400, E402, S408, T461}; and hydrophobic core of the EC1 domain: {I178, S180, V188, Y190, I192, G196, A197, V204, F205, I207, G212, L214, V216, L220, Y228, L230, S232, A234, I248, I250, V252, P260}. Splicing sites are indicated by the exon numbers and locations. (b) Survival analysis of gastric cancer patients with CDH1 mutations. Patients with non-protein truncating mutations had a significantly poorer prognosis than those with truncating mutations. (c) Comparison of the number of cases with two hits, both non-silent mutations and LOH, between CDH1 and other tumor suppressor genes. The P value indicates the significance of the infrequent number of cases with two hits in CDH1 in this study.
Extended Data Fig. 7 Frequency of genes with pathogenic germline variants in European and East Asian ancestry cases.
Circle size indicates -log10 of the q value, showing the significance of the frequency in this study cohort. The red part of the pie chart shows the frequency of cases with somatic LOH of the non-mutated allele.
Extended Data Fig. 8 Landscape of mutational signature in gastric cancer.
(a) Clustering of gastric cancer cases by the contribution of COSMIC mutational signatures using unsupervised hierarchical clustering with cosine distance and Ward linkage. Fourteen major mutational signatures are shown. Other minor signatures are grouped in the “Others” category (black). The top panel shows the contribution of each signature to the total number of mutations. The bottom panel shows the number of mutations in five representative signatures for each case. Nine clusters and one mixed group with characteristic combinations of mutational signatures were identified. See Supplementary information for details. (b) Correlations between the mutational signature cluster and clinical factors or presence of ADH1B/ALDH2 germline variants. P-values were calculated using two-sided Fisher’s exact tests. Signature Cluster 4 (SBS1/16) was significantly associated with ALDH2 rs671 AA/AG (P = 5.4e-15, OR = 27.6), ADH1B rs1229984 AA/AG (P = 1.6e-5, OR = 18.0), East Asian ancestry (P = 0.00012, OR = 14.2), diffuse-type (P = 0.00055, OR = 3.2), drinkers (P = 0.011, OR = 3.4), smokers (P = 0.019, OR = 3.0), and male patients (P = 0.0025, OR = 4.1). Signature Cluster 5 (SBS1/18) was significantly associated with the diffuse-type (P = 1.0e-11, OR = 2.5). Signature Cluster 7 (SBS17/28) was significantly associated with EBV infection (P = 1.7e-10, OR = Inf). (c) Comparison of 96 substitution patterns between SBS2 and PIK3CA hotspot mutations (in more than or equal to three cases). The top two panels show the mutation spectra classified by 96 substitution patterns. The top legend shows the bases immediately 5′ and 3′ for each substitution. The bottom panel shows the distribution of PIK3CA mutations in the current study. The TCA to TTA substitution in SBS2 corresponds to the E545K/E542K hotspot mutation. (d) Comparison of 96 substitution patterns between SBS1 and TP53 and APC hotspot mutations. (e) Comparison of 96 substitution patterns between SBS17 and mutations in CNBD1 and TRIM49C that showed high cosine similarities.
Extended Data Fig. 9 Immunogenomic features of gastric cancer.
(a) Effect of mutational signatures on neoantigen generation in hypermutated cases. Contribution of COSMIC mutational signatures to neoantigen and non-neoantigen mutations (left). The distribution of the SBS1 COSMIC mutational signature contribution to neoantigen and non-neoantigen mutations in each case (right). Boxplots represent the median and 25th and 75th percentiles of the data. Whiskers represent the highest and lowest values within 1.5 times the interquartile range of the boxplot. The P-values were calculated using two-sided Wilcoxon rank-sum tests. (b) Distribution of the number of LOH cases in each gene. Boxplots represent the median and 25th and 75th percentiles of the data. Whiskers represent the highest and lowest values within 1.5 times the interquartile range of the boxplot. HLA LOH is a common event in gastric cancer. LOH occurred more frequently in HLA-A/B/C than in other genes in hypermutated and non-hypermutated cases (P = 0.0018 and P = 0.0052, respectively, one-sided Wilcoxon test).
Supplementary information
Supplementary Information
Supplementary information for Fig. 5b, Extended Data Fig. 2d and Extended Data Fig. 8a legends and references for Supplementary Table 25.
Supplementary Table 1.
Supplementary Tables 1–28.
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Totoki, Y., Saito-Adachi, M., Shiraishi, Y. et al. Multiancestry genomic and transcriptomic analysis of gastric cancer. Nat Genet 55, 581–594 (2023). https://doi.org/10.1038/s41588-023-01333-x
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DOI: https://doi.org/10.1038/s41588-023-01333-x
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