Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Multiancestry genomic and transcriptomic analysis of gastric cancer

Nature Genetics volume 55, pages 581–594 (2023)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Genomic landscape of gastric cancer.
Fig. 2: Landscape of SMGs in non-hypermutated gastric cancer.
Fig. 3: Distribution of PIGR, SOX9, TRIM49 and ARHGAP5 mutations in non-hypermutated gastric cancer.
Fig. 4: Aberrant expressions of PD-L1, PD-L2 and FGFR2.
Fig. 5: CDH1 mutations in gastric cancer.
Fig. 6: Landscape of mutational signatures in gastric cancer.
Fig. 7: Correlation between SBS16 and RHOA Y42C hotspot mutations.
Fig. 8: Immune driver landscape of gastric cancer.

Similar content being viewed by others

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.

References

  1. Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394–424 (2018).

    Article  PubMed  Google Scholar 

  2. Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

    Article  PubMed  Google Scholar 

  3. Parsonnet, J. et al. Helicobacter pylori infection and the risk of gastric carcinoma. N. Engl. J. Med. 325, 1127–1131 (1991).

    Article  CAS  PubMed  Google Scholar 

  4. Kumar, S., Metz, D. C., Ellenberg, S., Kaplan, D. E. & Goldberg, D. S. Risk factors and incidence of gastric cancer after detection of Helicobacter pylori infection: a large cohort study. Gastroenterology 158, 527–536 (2020).

  5. Smyth, E. C., Nilsson, M., Grabsch, H. I., van Grieken, N. C. & Lordick, F. Gastric cancer. Lancet 396, 635–648 (2020).

    Article  CAS  PubMed  Google Scholar 

  6. Laurén, P. The two histological main types of gastric carcinoma: diffuse and so-called intestinal-type carcinoma. An attempt at a histo-clinical classification. Acta Pathol. Microbiol. Scand. 64, 31–49 (1965).

    Article  PubMed  Google Scholar 

  7. Laurén, P. A. & Nevalainen, T. J. Epidemiology of intestinal and diffuse types of gastric carcinoma. A time-trend study in Finland with comparison between studies from high- and low-risk areas. Cancer 71, 2926–2933 (1993).

    Article  PubMed  Google Scholar 

  8. Parsonnet, J. et al. Helicobacter pylori infection in intestinal- and diffuse-type gastric adenocarcinomas. J. Natl Cancer Inst. 83, 640–643 (1991).

  9. The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202–209 (2014).

    Article  Google Scholar 

  10. Petrovchich, I. & Ford, J. M. Genetic predisposition to gastric cancer. Semin. Oncol. 43, 554–559 (2016).

    Article  CAS  PubMed  Google Scholar 

  11. Kang, B. W. & Chau, I. Current status and future potential of predictive biomarkers for immune checkpoint inhibitors in gastric cancer. ESMO Open 5, e000791 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  12. Kamariza, M., Crawford, L., Jones, D. & Finucane, H. Misuse of the term ‘trans-ethnic’ in genomics research. Nat. Genet. 53, 1520–1521 (2021).

    Article  CAS  PubMed  Google Scholar 

  13. Wang, K. et al. Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer. Nat. Genet. 46, 573–582 (2014).

    Article  CAS  PubMed  Google Scholar 

  14. Chen, K. et al. Mutational landscape of gastric adenocarcinoma in Chinese: Implications for prognosis and therapy. Proc. Natl Acad. Sci. USA 112, 1107–1112 (2015).

  15. Liu, J. et al. Integrated exome and transcriptome sequencing reveals ZAK isoform usage in gastric cancer. Nat. Commun. 5, 3830 (2014).

    Article  CAS  PubMed  Google Scholar 

  16. Park, H. et al. Genomic alterations in BCL2L1 and DLC1 contribute to drug sensitivity in gastric cancer. Proc. Natl Acad. Sci. USA 112, 12492–12497 (2015).

  17. Johansen, F. E. et al. Absence of epithelial immunoglobulin A transport, with increased mucosal leakiness, in polymeric immunoglobulin receptor/secretory component-deficient mice. J. Exp. Med. 190, 915–992 (1999).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Kakiuchi, N. et al. Frequent mutations that converge on the NFKBIZ pathway in ulcerative colitis. Nature 577, 260–265 (2020).

    Article  CAS  PubMed  Google Scholar 

  19. Guimarães, D. S. & Gomes, M. D. Expression, purification, and characterization of the TRIM49 protein. Protein Expr. Purif. 143, 57–61 (2018).

    Article  PubMed  Google Scholar 

  20. Tamborero, D. et al. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 10, 25 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  21. Griffith, M. et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat. Genet. 49, 170–174 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 2017, PO.17.00011 (2017).

    PubMed  Google Scholar 

  23. Kataoka, K. et al. Aberrant PD-L1 expression through 3′-UTR disruption in multiple cancers. Nature 534, 402–406 (2016).

    Article  CAS  PubMed  Google Scholar 

  24. Jusakul, A. et al. Whole-genome and epigenomic landscapes of etiologically distinct subtypes of cholangiocarcinoma. Cancer Discov. 7, 1116–1135 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  25. Shiraishi, Y. et al. A comprehensive characterization of cis-acting splicing-associated variants in human cancer. Genome Res. 28, 1111–1125 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  26. Kakiuchi, M. et al. Recurrent gain-of-function mutations of RHOA in diffuse-type gastric carcinoma. Nat. Genet. 46, 583–587 (2014).

    Article  CAS  PubMed  Google Scholar 

  27. Zhang, H. et al. Gain-of-function RHOA mutations promote focal adhesion kinase activation and dependency in diffuse gastric cancer. Cancer Discov. 10, 288–305 (2020).

    Article  CAS  PubMed  Google Scholar 

  28. Jaiswal, B. S. et al. Oncogenic ERBB3 mutations in human cancers. Cancer Cell 23, 603–617 (2013).

    Article  CAS  PubMed  Google Scholar 

  29. Boggon, T. J. et al. C-cadherin ectodomain structure and implications for cell adhesion mechanisms. Science 296, 1308–1313 (2002).

    Article  CAS  PubMed  Google Scholar 

  30. Guilford, P. et al. E-cadherin germline mutations in familial gastric cancer. Nature 392, 402–405 (1998).

    Article  CAS  PubMed  Google Scholar 

  31. Ciriello, G. et al. Comprehensive molecular portraits of invasive lobular breast cancer. Cell 163, 506–519 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Kievit, A. et al. Variants in members of the cadherin–catenin complex, CDH1 and CTNND1, cause blepharocheilodontic syndrome. Eur. J. Hum. Genet. 26, 210–219 (2018).

  33. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  34. Totoki, Y. et al. Trans-ancestry mutational landscape of hepatocellular carcinoma genomes. Nat. Genet. 46, 1267–1273 (2014).

    Article  CAS  PubMed  Google Scholar 

  35. Chang, J. et al. Genomic analysis of oesophageal squamous-cell carcinoma identifies alcohol drinking-related mutation signature and genomic alterations. Nat. Commun. 8, 15290 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  36. Li, H. et al. Geographically separate increases in the frequency of the derived ADH1B*47His allele in eastern and western Asia. Am. J. Hum. Genet. 81, 842–846 (2007).

  37. Eng, M. Y., Luczak, S. E. & Wall, T. L. ALDH2, ADH1B, and ADH1C genotypes in Asians: a literature review. Alcohol Res. Health 30, 22–27 (2007).

    PubMed Central  PubMed  Google Scholar 

  38. Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  39. Blees, A. et al. Structure of the human MHC-I peptide-loading complex. Nature 551, 525–528 (2017).

    Article  CAS  PubMed  Google Scholar 

  40. Schoenfeld, A. J. & Hellmann, M. D. Acquired resistance to immune checkpoint inhibitors. Cancer Cell 37, 443–455 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  41. Cristescu, R. et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science 362, eaar3593 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  42. Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  43. Ayers, M. et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930–2940 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  44. Santos, J. C. et al. SOX9 elevation acts with canonical WNT signaling to drive gastric cancer progression. Cancer Res. 76, 6735–6746 (2016).

    Article  CAS  PubMed  Google Scholar 

  45. Togasaki, K. et al. Wnt signaling shapes the histologic variation in diffuse gastric cancer. Gastroenterology 160, 823–830 (2021).

    Article  CAS  PubMed  Google Scholar 

  46. Selvanathan, A. et al. CDH1 mutation distribution and type suggests genetic differences between the etiology of orofacial clefting and gastric cancer. Genes 11, 391 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  47. GBD 2017 Stomach Cancer Collaborators. The global, regional, and national burden of stomach cancer in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease study 2017. Lancet Gastroenterol. Hepatol. 5, 42–54 (2020).

  48. WHO Classification of Tumours Editorial Board. (ed.) World Health Organization Classification of Tumours, 5th Edition: Digestive System Tumours (IARC, 2010).

  49. Luke, J. J., Bao, R., Sweis, R. F., Spranger, S. & Gajewski, T. F. WNT/β-catenin pathway activation correlates with immune exclusion across human cancers. Clin. Cancer Res. 25, 3074–3083 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  50. Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  51. Harrison, O. J. et al. The extracellular architecture of adherens junctions revealed by crystal structures of type I cadherins. Structure 19, 244–256 (2011).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  52. Nardone, V. et al. Crystal structure of human E-cadherin-EC1EC2 in complex with a peptidomimetic competitive inhibitor of cadherin homophilic interaction. J. Med. Chem. 59, 5089–5094 (2016).

    Article  CAS  PubMed  Google Scholar 

  53. Parisini, E., Higgins, J. M., Liu, J. H., Brenner, M. B. & Wang, J. H. The crystal structure of human E-cadherin domains 1 and 2, and comparison with other cadherins in the context of adhesion mechanism. J. Mol. Biol. 373, 401–411 (2007).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  54. Romanel, A., Zhang, T., Elemento, O. & Demichelis, F. EthSEQ: ethnicity annotation from whole exome sequencing data. Bioinformatics 33, 2402–2404 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  55. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2019).

    Article  Google Scholar 

  56. Yachida, S. et al. Comprehensive genomic profiling of neuroendocrine carcinomas of the gastrointestinal system. Cancer Discov. 12, 692–711 (2022).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  57. Olshen, A. B., Venkatraman, E. S., Lucito, R. & Wigler, M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5, 557–572 (2004).

    Article  PubMed  Google Scholar 

  58. Shen, R. & Seshan, V. E. FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Res. 44, e131–e131 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  59. Nakamura, H. et al. Genomic spectra of biliary tract cancer. Nat. Genet. 47, 1003–1010 (2015).

    Article  CAS  PubMed  Google Scholar 

  60. Dobin, A. et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  61. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  62. Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  63. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B 57, 289–300 (1995).

  64. Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  65. Mularoni, L., Sabarinathan, R., Deu-Pons, J., Gonzalez-Perez, A. & López-Bigas, N. OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations. Genome Biol. 17, 128 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  66. Priestley, P. et al. Pan-cancer whole-genome analyses of metastatic solid tumours. Nature 575, 210–216 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  67. Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296–W303 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  68. Willard, L. et al. VADAR: a web server for quantitative evaluation of protein structure quality. Nucleic Acids Res. 31, 3316–3319 (2003).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  69. Pettersen, E. F. et al. UCSF Chimera: a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    Article  CAS  PubMed  Google Scholar 

  70. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  71. Van der Auwera, G. A. et al. From FastQ data to high-confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–11.10.33 (2013).

  72. Sim, N. L. et al. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 40, W452–W457 (2012).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  73. Adzhubei, I., Jordan, D. M. & Sunyaev, S. R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. 7, 7.20.1–7.20.41 (2013).

    Google Scholar 

  74. Schwarz, J. M., Cooper, D. N., Schuelke, M. & Seelow, D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat. Methods 11, 361–362 (2014).

    Article  CAS  PubMed  Google Scholar 

  75. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  76. Harrison, S. M. et al. Using ClinVar as a resource to support variant interpretation. Curr. Protoc. Hum. Genet. 89, 8.16.1–8.16.23 (2016).

    PubMed  Google Scholar 

  77. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164–e164 (2010).

    Article  PubMed Central  PubMed  Google Scholar 

  78. 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  79. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  80. Petrovski, S., Wang, Q., Heinzen, E. L., Allen, A. S. & Goldstein, D. B. Genic intolerance to functional variation and the interpretation of personal genomes. PLoS Genet. 9, e1003709 (2013).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  81. Nagasaki, M. et al. Rare variant discovery by deep whole-genome sequencing of 1,070 Japanese individuals. Nat. Commun. 6, 8018 (2015).

    Article  CAS  PubMed  Google Scholar 

  82. Alexandrov, L. B., Nik-Zainal, S., Wedge, D. C., Campbell, P. J. & Stratton, M. R. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 3, 246–259 (2013).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  83. Rosenthal, R., McGranahan, N., Herrero, J., Taylor, B. S. & Swanton, C. DeconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol. 17, 31 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  84. Bjerregaard, A. M., Nielsen, M., Hadrup, S. R., Szallasi, Z. & Eklund, A. C. MuPeXI: prediction of neo-epitopes from tumor sequencing data. Cancer Immunol. Immunother. 66, 1123–1130 (2017).

    Article  CAS  PubMed  Google Scholar 

  85. Shukla, S. A. et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 33, 1152–1158 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  86. Xie, C. et al. Fast and accurate HLA typing from short-read next-generation sequence data with xHLA. Proc. Natl Acad. USA 114, 8059–8064 (2017).

    Article  CAS  Google Scholar 

  87. Kawaguchi, S., Higasa, K., Shimizu, M., Yamada, R. & Matsuda, F. HLA-HD: an accurate HLA typing algorithm for next-generation sequencing data. Hum. Mutat. 38, 788–797 (2017).

  88. McGranahan, N. et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171, 1259–1271 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

Download references

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.).

Author information

Authors and Affiliations

  1. Division of Cancer Genomics, National Cancer Center Research Institute, Tokyo, Japan

    Yasushi Totoki, Mihoko Saito-Adachi, Hiromi Nakamura, Natsuko Hama, Yasuhito Arai, Fumie Hosoda, Akihiko Fukagawa & Tatsuhiro Shibata

  2. Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan

    Yuichi Shiraishi, Kenichi Chiba & Naoko Iida

  3. Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Daisuke Komura, Hiroto Katoh & Shumpei Ishikawa

  4. Department of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Kanagawa, Japan

    Akihiro Suzuki, Tetsuya Matsuura, Eiji Sakai & Atsushi Nakajima

  5. Genome Science and Medicine Laboratory, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan

    Akihiro Suzuki, Kenji Tatsuno, Shogo Yamamoto, Genta Nagae, Chen Shihang & Hiroyuki Aburatani

  6. Department of Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Hirofumi Rokutan, Hiroyuki Abe & Tetsuo Ushiku

  7. Division of Bioinformatics, National Cancer Center Research Institute, Tokyo, Japan

    Hanako Ono & Mamoru Kato

  8. Biological Data Science, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan

    Hiroki Ueda

  9. Department of Pathology and Clinical Laboratories, National Cancer Center Hospital, Tokyo, Japan

    Shigeki Sekine

  10. Department of Gastrointestinal Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Sachiyo Nomura & Yasuyuki Seto

  11. Department of Gastrointestinal Surgery, Kanagawa Cancer Center, Kanagawa, Japan

    Takashi Ohshima

  12. Department of Surgery, Yokohama City University Graduate School of Medicine, Kanagawa, Japan

    Yasushi Rino

  13. Dept of Medicine, National University of Singapore, Singapore, Singapore

    Khay Guan Yeoh

  14. Dept of Surgery, National University of Singapore, Singapore, Singapore

    Jimmy So

  15. Dept of Surgery, Tan Tock Seng Hospital, Singapore, Singapore

    Kaushal Sanghvi

  16. Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore

    Richie Soong

  17. Department of Cancer Genome Informatics, Graduate School of Medicine, Osaka University, Osaka, Japan

    Shinichi Yachida

  18. Division of Genomic Medicine, National Cancer Center Research Institute, Tokyo, Japan

    Shinichi Yachida

  19. Department of Gastric Surgery, National Cancer Center Hospital, Tokyo, Japan

    Hitoshi Katai

  20. Cancer and Stem Cell Biology, Duke-NUS Medical School Singapore, Singapore, Singapore

    Patrick Tan

  21. Epigenomic and Epitranscriptomic Regulation, Genome Institute of Singapore, Singapore, Singapore

    Patrick Tan

  22. Laboratory of Molecular Medicine, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Tatsuhiro Shibata

Authors
  1. Yasushi Totoki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mihoko Saito-Adachi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuichi Shiraishi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daisuke Komura
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hiromi Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Akihiro Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kenji Tatsuno
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hirofumi Rokutan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Natsuko Hama
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shogo Yamamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hanako Ono
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yasuhito Arai
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Fumie Hosoda
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Hiroto Katoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Kenichi Chiba
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Naoko Iida
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Genta Nagae
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Hiroki Ueda
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Chen Shihang
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Shigeki Sekine
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Hiroyuki Abe
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Sachiyo Nomura
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Tetsuya Matsuura
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Eiji Sakai
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Takashi Ohshima
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Yasushi Rino
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Khay Guan Yeoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Jimmy So
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Kaushal Sanghvi
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Richie Soong
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Akihiko Fukagawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Shinichi Yachida
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Mamoru Kato
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Yasuyuki Seto
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Tetsuo Ushiku
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Atsushi Nakajima
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Hitoshi Katai
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Patrick Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. Shumpei Ishikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  40. Hiroyuki Aburatani
    View author publications

    You can also search for this author in PubMed Google Scholar

  41. Tatsuhiro Shibata
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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.

Corresponding author

Correspondence to Tatsuhiro Shibata.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Reporting Summary

Supplementary Table 1.

Supplementary Tables 1–28.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-023-01333-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing