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Mutational signatures in esophageal adenocarcinoma define etiologically distinct subgroups with therapeutic relevance

A Corrigendum to this article was published on 31 January 2017

This article has been updated

Abstract

Esophageal adenocarcinoma (EAC) has a poor outcome, and targeted therapy trials have thus far been disappointing owing to a lack of robust stratification methods. Whole-genome sequencing (WGS) analysis of 129 cases demonstrated that this is a heterogeneous cancer dominated by copy number alterations with frequent large-scale rearrangements. Co-amplification of receptor tyrosine kinases (RTKs) and/or downstream mitogenic activation is almost ubiquitous; thus tailored combination RTK inhibitor (RTKi) therapy might be required, as we demonstrate in vitro. However, mutational signatures showed three distinct molecular subtypes with potential therapeutic relevance, which we verified in an independent cohort (n = 87): (i) enrichment for BRCA signature with prevalent defects in the homologous recombination pathway; (ii) dominant T>G mutational pattern associated with a high mutational load and neoantigen burden; and (iii) C>A/T mutational pattern with evidence of an aging imprint. These subtypes could be ascertained using a clinically applicable sequencing strategy (low coverage) as a basis for therapy selection.

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Figure 1: Recurrent genomic events in the cohort (n = 129).
Figure 2: RTK gene copy number profiling and responses to targeted RTK therapy (n = 129).
Figure 3: Mutational-signature-based clustering shows differences in disease etiology and is spatially consistent within a single tumor.
Figure 4: DNA damage repair pathways altered through nonsynonymous mutations/indels in the cohort.
Figure 5: Neoantigen burden is significantly higher in the mutagenic subgroup and is associated with an increased CD8+ T cell density.
Figure 6: Treatment response in different mutational signature groups.
Figure 7: Proposed subclassification of EAC based on mutational signatures informs etiology and, consequently, potential tailored therapies to be further investigated for the disease.

Change history

  • 19 September 2016

    In the version of this article initially published online, the mutation signature illustrations for S1 and S2 in Figure 3a were switched. Additionally, in the Online Methods, the text originally stated that structural variants were called using BWA-MEM, when it should have stated that these were called using BWA. These errors have been corrected for the print, PDF and HTML versions of this article.

References

  1. Ferlay, J. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136, E359–E386 (2015).

    CAS  PubMed  Google Scholar 

  2. Brown, L.M., Devesa, S.S. & Chow, W.H. Incidence of adenocarcinoma of the esophagus among white Americans by sex, stage, and age. J. Natl. Cancer Inst. 100, 1184–1187 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Cunningham, D., Okines, A.F. & Ashley, S. Capecitabine and oxaliplatin for advanced esophagogastric cancer. N. Engl. J. Med. 362, 858–859 (2010).

    Article  CAS  PubMed  Google Scholar 

  4. Allum, W.H., Stenning, S.P., Bancewicz, J., Clark, P.I. & Langley, R.E. Long-term results of a randomized trial of surgery with or without preoperative chemotherapy in esophageal cancer. J. Clin. Oncol. 27, 5062–5067 (2009).

    Article  PubMed  Google Scholar 

  5. Cunningham, D. et al. Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer. N. Engl. J. Med. 355, 11–20 (2006).

    Article  CAS  PubMed  Google Scholar 

  6. van Hagen, P. et al. Preoperative chemoradiotherapy for esophageal or junctional cancer. N. Engl. J. Med. 366, 2074–2084 (2012).

    Article  CAS  PubMed  Google Scholar 

  7. Bang, Y.J. et al. Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): a phase 3, open-label, randomised controlled trial. Lancet 376, 687–697 (2010).

    Article  CAS  PubMed  Google Scholar 

  8. Gao, Y.B. et al. Genetic landscape of esophageal squamous cell carcinoma. Nat. Genet. 46, 1097–1102 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. Schulze, K. et al. Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat. Genet. 47, 505–511 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cancer Genome Atlas Network. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015).

  11. Waddell, N. et al. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature 518, 495–501 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

  14. Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).

  15. Chantrill, L.A. et al. Precision medicine for advanced pancreas cancer: The Individualized Molecular Pancreatic Cancer Therapy (IMPaCT) Trial. Clin. Cancer Res. 21, 2029–2037 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Dulak, A.M. et al. Exome and whole-genome sequencing of esophageal adenocarcinoma identifies recurrent driver events and mutational complexity. Nat. Genet. 45, 478–486 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Weaver, J.M. et al. Ordering of mutations in preinvasive disease stages of esophageal carcinogenesis. Nat. Genet. 46, 837–843 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Nones, K. et al. Genomic catastrophes frequently arise in esophageal adenocarcinoma and drive tumorigenesis. Nat. Commun. 5, 5224 (2014).

    Article  CAS  PubMed  Google Scholar 

  19. Cancer Genome Atlas Research Network. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

  20. Paterson, A.L. et al. Mobile element insertions are frequent in oesophageal adenocarcinomas and can mislead paired-end sequencing analysis. BMC Genomics 16, 473 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Tubio, J.M. et al. Mobile DNA in cancer. Extensive transduction of nonrepetitive DNA mediated by L1 retrotransposition in cancer genomes. Science 345, 1251343 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Mermel, C.H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 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  PubMed Central  Google Scholar 

  24. Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Paterson, A.L. et al. Characterization of the timing and prevalence of receptor tyrosine kinase expression changes in oesophageal carcinogenesis. J. Pathol. 230, 118–128 (2013).

    Article  CAS  PubMed  Google Scholar 

  26. Van Cutsem, E. et al. HER2 screening data from ToGA: targeting HER2 in gastric and gastroesophageal junction cancer. Gastric Cancer 18, 476–484 (2015).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Shiraishi, Y., Tremmel, G., Miyano, S. & Stephens, M. A simple model-based approach to inferring and visualizing cancer mutation signatures. PLoS Genet. 11, e1005657 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Gehring, J.S., Fischer, B., Lawrence, M. & Huber, W. SomaticSignatures: inferring mutational signatures from single-nucleotide variants. Bioinformatics 31, 3673–3675 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Pearl, L.H., Schierz, A.C., Ward, S.E., Al-Lazikani, B. & Pearl, F.M. Therapeutic opportunities within the DNA damage response. Nat. Rev. Cancer 15, 166–180 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Shen, J. et al. ARID1A deficiency impairs the DNA damage checkpoint and sensitizes cells to PARP inhibitors. Cancer Discov. 5, 752–767 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Hodi, F.S. et al. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363, 711–723 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Larkin, J. et al. Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. N. Engl. J. Med. 373, 23–34 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Herbst, R.S. et al. Pembrolizumab versus docetaxel for previously treated, PD-L1–positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet 387, 1540–1550 (2016).

    Article  CAS  PubMed  Google Scholar 

  35. Rizvi, N.A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer. Science 348, 124–128 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Van Allen, E.M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Tumeh, P.C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hamanishi, J. et al. Programmed cell death 1 ligand 1 and tumor-infiltrating CD8+ T lymphocytes are prognostic factors of human ovarian cancer. Proc. Natl. Acad. Sci. USA 104, 3360–3365 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Benafif, S. & Hall, M. An update on PARP inhibitors for the treatment of cancer. Onco Targets Ther. 8, 519–528 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Oza, A.M. et al. Olaparib combined with chemotherapy for recurrent platinum-sensitive ovarian cancer: a randomised phase 2 trial. Lancet Oncol. 16, 87–97 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. Demel, H.R. et al. Effects of topoisomerase inhibitors that induce DNA damage response on glucose metabolism and PI3K/Akt/mTOR signaling in multiple myeloma cells. Am. J. Cancer Res. 5, 1649–1664 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Farmer, H. et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434, 917–921 (2005).

    Article  CAS  PubMed  Google Scholar 

  45. Di Leonardo, A., Linke, S.P., Clarkin, K. & Wahl, G.M. DNA damage triggers a prolonged p53-dependent G1 arrest and long-term induction of Cip1 in normal human fibroblasts. Genes Dev. 8, 2540–2551 (1994).

    Article  CAS  PubMed  Google Scholar 

  46. Agarwal, M.L. et al. A p53-dependent S-phase checkpoint helps to protect cells from DNA damage in response to starvation for pyrimidine nucleotides. Proc. Natl. Acad. Sci. USA 95, 14775–14780 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Brooks, K. et al. A potent Chk1 inhibitor is selectively cytotoxic in melanomas with high levels of replicative stress. Oncogene 32, 788–796 (2013).

    Article  CAS  PubMed  Google Scholar 

  48. Vera, J. et al. Chk1 and Wee1 control genotoxic-stress induced G2-M arrest in melanoma cells. Cell. Signal. 27, 951–960 (2015).

    Article  CAS  PubMed  Google Scholar 

  49. Liu, Q. et al. Chk1 is an essential kinase that is regulated by Atr and required for the G2/M DNA damage checkpoint. Genes Dev. 14, 1448–1459 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Watanabe, N., Broome, M. & Hunter, T. Regulation of the human WEE1Hu CDK tyrosine 15-kinase during the cell cycle. EMBO J. 14, 1878–1891 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Sato, T. et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009).

    CAS  PubMed  Google Scholar 

  53. Ciriello, G. et al. Emerging landscape of oncogenic signatures across human cancers. Nat. Genet. 45, 1127–1133 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Osato, M. Point mutations in the RUNX1/AML1 gene: another actor in RUNX leukemia. Oncogene 23, 4284–4296 (2004).

    Article  CAS  PubMed  Google Scholar 

  55. Watkins, J.A., Irshad, S., Grigoriadis, A. & Tutt, A.N. Genomic scars as biomarkers of homologous recombination deficiency and drug response in breast and ovarian cancers. Breast Cancer Res. 16, 211 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Alexandrov, L.B., Nik-Zainal, S., Siu, H.C., Leung, S.Y. & Stratton, M.R. A mutational signature in gastric cancer suggests therapeutic strategies. Nat. Commun. 6, 8683 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Ledermann, J. et al. Olaparib maintenance therapy in patients with platinum-sensitive relapsed serous ovarian cancer: a preplanned retrospective analysis of outcomes by BRCA status in a randomised phase 2 trial. Lancet Oncol. 15, 852–861 (2014).

    Article  CAS  PubMed  Google Scholar 

  58. Verhagen, C.V. et al. Extent of radiosensitization by the PARP inhibitor olaparib depends on its dose, the radiation dose and the integrity of the homologous recombination pathway of tumor cells. Radiother. Oncol. 116, 358–365 (2015).

    Article  CAS  PubMed  Google Scholar 

  59. Kelly, R.J. et al. Adaptive immune resistance in gastro-esophageal cancer: correlating tumoral/stromal PDL1 expression with CD8+ cell count. J. Clin. Oncol. 33, abstr. 4031 (2015).

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  61. Bridges, K.A. et al. MK-1775, a novel Wee1 kinase inhibitor, radiosensitizes p53-defective human tumor cells. Clin. Cancer Res. 17, 5638–5648 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Wang, Y. et al. Radiosensitization of p53 mutant cells by PD0166285, a novel G2 checkpoint abrogator. Cancer Res. 61, 8211–8217 (2001).

    CAS  PubMed  Google Scholar 

  63. Liu, D.S. et al. APR-246 potently inhibits tumour growth and overcomes chemoresistance in preclinical models of oesophageal adenocarcinoma. Gut 64, 1506–1516 (2015).

    Article  CAS  PubMed  Google Scholar 

  64. Stewart, A., Thavasu, P., de Bono, J.S. & Banerji, U. Titration of signalling output: insights into clinical combinations of MEK and AKT inhibitors. Ann. Oncol. 26, 1504–1510 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Saunders, C.T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics 28, 1811–1817 (2012).

    Article  CAS  PubMed  Google Scholar 

  67. McLaren, W. et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Van Loo, P. et al. Allele-specific copy number analysis of tumors. Proc. Natl. Acad. Sci. USA 107, 16910–16915 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  69. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Zack, T.I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Boeva, V. et al. Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics 28, 423–425 (2012).

    Article  CAS  PubMed  Google Scholar 

  72. Chen, X. et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 32, 1220–1222 (2016).

    Article  CAS  PubMed  Google Scholar 

  73. Schulte, I. et al. Structural analysis of the genome of breast cancer cell line ZR-75-30 identifies twelve expressed fusion genes. BMC Genomics 13, 719 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Le Tallec, B. et al. Common fragile site profiling in epithelial and erythroid cells reveals that most recurrent cancer deletions lie in fragile sites hosting large genes. Cell Reports 4, 420–428 (2013).

    Article  CAS  PubMed  Google Scholar 

  75. Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  CAS  PubMed  Google Scholar 

  76. Wilkerson, M.D. & Hayes, D.N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572–1573 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Nilsen, G. et al. Copynumber: efficient algorithms for single- and multi-track copy number segmentation. BMC Genomics 13, 591 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Korbel, J.O. & Campbell, P.J. Criteria for inference of chromothripsis in cancer genomes. Cell 152, 1226–1236 (2013).

    Article  CAS  PubMed  Google Scholar 

  79. Puente, X.S. et al. Non-coding recurrent mutations in chronic lymphocytic leukaemia. Nature 526, 519–524 (2015).

    Article  CAS  PubMed  Google Scholar 

  80. Kumar, P., Henikoff, S. & Ng, P.C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).

    Article  CAS  PubMed  Google Scholar 

  81. Adzhubei, I.A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Lundegaard, C. et al. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11. Nucleic Acids Res. 36, W509–W512 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Adiconis, X. et al. Comparative analysis of RNA sequencing methods for degraded or low-input samples. Nat. Methods 10, 623–629 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This paper is dedicated to Nadeera de Silva, who tragically and unexpectedly died while this paper was undergoing revision. He made an important contribution to this research, particularly bringing his clinical oncology perspective to bear on the translational relevance of the findings.

Whole-genome sequencing of esophageal adenocarcinoma samples was carried out in concert with the International Cancer Genome Consortium (ICGC) through the OCCAMS Consortium and was funded by program grants from Cancer Research UK (RG66287, RG81771, RG84119). We thank the ICGC members for their input on verification standards as part of the benchmarking exercise. We thank the Human Research Tissue Bank, which is supported by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, from Addenbrooke's Hospital and UCL. We also thank the University Hospital of Southampton Trust; the Southampton, Birmingham, Edinburgh and UCL Experimental Cancer Medicine Centres; and the QEHB charities. R.C.F. is funded by an NIHR Professorship (RG67258) and receives core funding from the Medical Research Council (RG84369) and infrastructure support from the Biomedical Research Centre (RG64237) and the Experimental Cancer Medicine Centre (RG62923). We acknowledge the support of the University of Cambridge, Cancer Research UK (C14303/A17197) and Hutchison Whampoa Limited. We thank P. Van Loo for providing the NGS version of ASCAT for copy number calling. We are grateful to all the patients who provided written consent for participation in this study and to the staff at all participating centres.

Some of the work was undertaken at UCLH/UCL, which received a proportion of funding from the Department of Health's NIHR Biomedical Research Centres funding scheme. The views expressed in this publication are those of the authors and are not necessarily those of the Department of Health. The work at UCLH/UCL was also supported by the CRUK UCL Early Cancer Medicine Centre.

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Contributions

R.C.F. conceived the overall study. M.S., X.L. and P.A.W.E. analyzed the data. R.C.F., M.S., X.L., N.d.S., P.A.W.E. and A.G.L. conceived and designed the experiments. M.S. performed the statistical analysis. X.L., G.C., S.M., M.O., A.M., J.C. and N.G.-D. performed the experiments. M.D.E. performed benchmarking studies on the variant calls, and implemented and ran several variant-calling and analysis pipelines. G.C. contributed to the structural variant analysis. J.B. contributed expression data and curated the clinical data collection. S.M. and N.G. coordinated sample processing with clinical centers and was responsible for sample collections. T.-P.Y. performed the BFB analysis. L.B. ran the variant-calling pipelines. H.C. contributed to the RTK analysis. A.G., J.S. and T.U. contributed cell lines. N.W. and A.P.B. contributed sequencing data for validation. B.N. coordinated data and tissue collection from centers for the study. A.A. helped develop the copy-number-calling pipeline. R.C.F. and S.T. jointly supervised the research. M.S., N.d.S., X.L. and R.C.F. wrote the manuscript. All authors approved the final version of the manuscript.

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Correspondence to Rebecca C Fitzgerald.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–28, Supplementary Tables 1, 2, 4–9, 11 and 12 and Supplementary Note (PDF 5505 kb)

Supplementary Table 3

Significantly deleted loci in the cohort according to GISTIC2.0. Loci with residual q-value (XLSX 13 kb)

Supplementary Table 10

Microsatellite instability analysis results. The potentially microsatellite unstable samples that were removed from the analysis are highlighted at the top. (XLSX 13 kb)

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Secrier, M., Li, X., de Silva, N. et al. Mutational signatures in esophageal adenocarcinoma define etiologically distinct subgroups with therapeutic relevance. Nat Genet 48, 1131–1141 (2016). https://doi.org/10.1038/ng.3659

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