Skip to main content

Thank you for visiting 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.

The landscape of selection in 551 esophageal adenocarcinomas defines genomic biomarkers for the clinic


Esophageal adenocarcinoma (EAC) is a poor-prognosis cancer type with rapidly rising incidence. Understanding of the genetic events driving EAC development is limited, and there are few molecular biomarkers for prognostication or therapeutics. Using a cohort of 551 genomically characterized EACs with matched RNA sequencing data, we discovered 77 EAC driver genes and 21 noncoding driver elements. We identified a mean of 4.4 driver events per tumor, which were derived more commonly from mutations than copy number alterations, and compared the prevelence of these mutations to the exome-wide mutational excess calculated using non-synonymous to synonymous mutation ratios (dN/dS). We observed mutual exclusivity or co-occurrence of events within and between several dysregulated EAC pathways, a result suggestive of strong functional relationships. Indicators of poor prognosis (SMAD4 and GATA4) were verified in independent cohorts with significant predictive value. Over 50% of EACs contained sensitizing events for CDK4 and CDK6 inhibitors, which were highly correlated with clinically relevant sensitivity in a panel of EAC cell lines and organoids.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Detection of EAC driver genes.
Fig. 2: Copy number variation under positive selection.
Fig. 3: The driver-gene landscape of EAC.
Fig. 4: Biological pathways undergoing selective dysregulation in EAC.
Fig. 5: Clinical importance of driver events in 379 clinically annotated EACs.
Fig. 6: CDK4/CDK6 inhibitors in EAC.

Code availability

Code associated with the analysis is available upon request.

Data availability

The WGS and RNA expression data can be found at the European Genome-phenome Archive under accession numbers EGAD00001004417 and EGAD00001004423, respectively.


  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  Google Scholar 

  2. Coleman, H. G., Xie, S. H. & Lagergren, J. The epidemiology of esophageal adenocarcinoma. Gastroenterology 154, 390–405 (2018).

    Article  Google Scholar 

  3. Smyth, E. C. et al. Oesophageal cancer. Nat. Rev. Dis. Primers 3, 17048 (2017).

    Article  Google Scholar 

  4. Campbell, P.J., Getz, G., Stuart, J.M., Korbel, J.O. & Stein, L.D. Pan-cancer analysis of whole genomes. Preprint at (2017).

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

    CAS  Article  Google Scholar 

  6. Secrier, M. et al. Mutational signatures in esophageal adenocarcinoma define etiologically distinct subgroups with therapeutic relevance. Nat. Genet. 48, 1131–1141 (2016).

    CAS  Article  Google Scholar 

  7. Tamborero, D. et al. Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci. Rep. 3, 2650 (2013).

    Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  9. Cancer Genome Atlas Research Network. et al. Integrated genomic characterization of oesophageal carcinoma. Nature 541, 169–175 (2017).

    Article  Google Scholar 

  10. Lin, D. C. et al. Identification of distinct mutational patterns and new driver genes in oesophageal squamous cell carcinomas and adenocarcinomas. Gut 67, 1769–1779 (2017).

    Article  Google Scholar 

  11. Rheinbay, E. et al. Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes. Preprint at (2017).

  12. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507, 315–322 (2014).

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. 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  Google Scholar 

  15. Dulak, A. M. et al. Gastrointestinal adenocarcinomas of the esophagus, stomach, and colon exhibit distinct patterns of genome instability and oncogenesis. Cancer Res. 72, 4383–4393 (2012).

    CAS  Article  Google Scholar 

  16. Frankel, A. et al. Genome-wide analysis of esophageal adenocarcinoma yields specific copy number aberrations that correlate with prognosis. Genes Chromosom. Cancer 53, 324–338 (2014).

    CAS  Article  Google Scholar 

  17. Secrier, M. & Fitzgerald, R. C. Signatures of mutational processes and associated risk factors in esophageal squamous cell carcinoma: a geographically independent stratification strategy? Gastroenterology 150, 1080–1083 (2016).

    Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  19. 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).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  21. Martincorena I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041.e21.

    Article  Google Scholar 

  22. Wadi, L. et al. Candidate cancer driver mutations in super-enhancers and long-range chromatin interaction networks. Preprint at (2017).

  23. Gonzalez-Perez, A. & Lopez-Bigas, N. Functional impact bias reveals cancer drivers. Nucleic Acids Res. 40, e169 (2012).

    CAS  Article  Google Scholar 

  24. Tamborero, D., Gonzalez-Perez, A. & Lopez-Bigas, N. OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics 29, 2238–2244 (2013).

    CAS  Article  Google Scholar 

  25. Porta-Pardo, E. & Godzik, A. e-Driver: a novel method to identify protein regions driving cancer. Bioinformatics 30, 3109–3114 (2014).

    CAS  Article  Google Scholar 

  26. Porta-Pardo, E., Hrabe, T. & Godzik, A. Cancer3D: understanding cancer mutations through protein structures. Nucleic Acids Res. 43, D968–D973 (2015).

    CAS  Article  Google Scholar 

  27. Futreal, P. A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).

    CAS  Article  Google Scholar 

  28. Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

    CAS  Article  Google Scholar 

  29. Shuai, S., Gallinger, S. & Stein, L.D. DriverPower: combined burden and functional impact tests for cancer driver discovery. Preprint at (2017).

  30. Taylor, A. M. et al. Genomic and functional approaches to understanding cancer aneuploidy. Cancer Cell 33, 676–689.e3 (2018).

    CAS  Article  Google Scholar 

  31. Turner, K. M. et al. Extrachromosomal oncogene amplification drives tumour evolution and genetic heterogeneity. Nature 543, 122–125 (2017).

    CAS  Article  Google Scholar 

  32. Chang, M. T. et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat. Biotechnol. 34, 155–163 (2016).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  34. Chen, Z. et al. Mammalian drug efflux transporters of the ATP binding cassette (ABC) family in multidrug resistance: a review of the past decade. Cancer Lett. 370, 153–164 (2016).

    CAS  Article  Google Scholar 

  35. Giannakis, M. et al. Genomic correlates of immune-cell infiltrates in colorectal carcinoma. Cell Rep. 17, 1206 (2016).

    CAS  Article  Google Scholar 

  36. Pei, X. H. & Xiong, Y. Biochemical and cellular mechanisms of mammalian CDK inhibitors: a few unresolved issues. Oncogene 24, 2787–2795 (2005).

    CAS  Article  Google Scholar 

  37. Leiserson, M. D. et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet. 47, 106–114 (2015).

    CAS  Article  Google Scholar 

  38. Singhi, A. D. et al. Smad4 loss in esophageal adenocarcinoma is associated with an increased propensity for disease recurrence and poor survival. Am. J. Surg. Pathol. 39, 487–495 (2015).

    Article  Google Scholar 

  39. Levy, L. & Hill, C. S. Alterations in components of the TGF-beta superfamily signaling pathways in human cancer. Cytokine Growth Factor Rev. 17, 41–58 (2006).

    CAS  Article  Google Scholar 

  40. Tamborero, D. et al. Cancer genome interpreter annotates the biological and clinical relevance of tumor alterations. Preprint at (2017).

  41. Contino, G. et al. Whole-genome sequencing of nine esophageal adenocarcinoma cell lines. F1000Res. 5, 1336 (2016).

    Article  Google Scholar 

  42. Liston, D. R. & Davis, M. Clinically relevant concentrations of anticancer drugs: a guide for nonclinical studies. Clin. Cancer Res. 23, 3489–3498 (2017).

    CAS  Article  Google Scholar 

  43. Herrera-Abreu, M. T. et al. Early adaptation and acquired resistance to CDK4/6 inhibition in estrogen receptor-positive breast cancer. Cancer Res. 76, 2301–2313 (2016).

    CAS  Article  Google Scholar 

  44. Li, X. et al. Organoid cultures recapitulate esophageal adenocarcinoma heterogeneity providing a model for clonality studies and precision therapeutics. Nat. Commun. 9, 2983 (2018).

    Article  Google Scholar 

  45. Llosa, N. J. et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints. Cancer Discov. 5, 43–51 (2015).

    CAS  Article  Google Scholar 

  46. Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).

    CAS  Article  Google Scholar 

  47. Grasso, C. S. et al. Genetic mechanisms of immune evasion in colorectal cancer. Cancer Discov. 8, 730–749 (2018).

    CAS  Article  Google Scholar 

  48. Ismail, A. et al. Early G1 cyclin-dependent kinases as prognostic markers and potential therapeutic targets in esophageal adenocarcinoma. Clin. Cancer Res. 17, 4513–4522 (2011).

    CAS  Article  Google Scholar 

  49. Ding, J. et al. Systematic analysis of somatic mutations impacting gene expression in 12 tumour types. Nat. Commun. 6, 8554 (2015).

    CAS  Article  Google Scholar 

  50. Lee, A. Y. et al. Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection. Genome Biol. 19, 188 (2018).

    Article  Google Scholar 

  51. Nagai, K. et al. Differential expression profiles of sense and antisense transcripts between HCV-associated hepatocellular carcinoma and corresponding non-cancerous liver tissue. Int. J. Oncol. 40, 1813–1820 (2012).

    CAS  PubMed  Google Scholar 

  52. Adzhubei, I., Jordan, D. M. & Sunyaev, S. R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. 79(7), 20 (2013).

    Google Scholar 

  53. Ng, P. C. & Henikoff, S. Predicting the effects of amino acid substitutions on protein function. Annu. Rev. Genomics Hum. Genet. 7, 61–80 (2006).

    CAS  Article  Google Scholar 

  54. Reimand, J., Wagih, O. & Bader, G. D. The mutational landscape of phosphorylation signaling in cancer. Sci. Rep. 3, 2651 (2013).

    Article  Google Scholar 

  55. Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017).

    CAS  Article  Google Scholar 

  56. Wala, J.A. et al. Selective and mechanistic sources of recurrent rearrangements across the cancer genome. Preprint at (2017).

  57. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).

    Article  Google Scholar 

  58. Finn, R. S. et al. PD 0332991, a selective cyclin D kinase 4/6 inhibitor, preferentially inhibits proliferation of luminal estrogen receptor-positive human breast cancer cell lines in vitro. Breast Cancer Res. 11, R77 (2009).

    Article  Google Scholar 

Download references


We thank A. J. Bass and N. Waddell for providing data in Dulak et al.19 and Nones et al.20, respectively, which were also included in our previous publication18. Inclusion of these data allowed for augmentation of our ICGC cohort and greater sensitivity for the detection of EAC driver variants. OCCAMS was funded by a Programme Grant from Cancer Research UK (RG66287), and the laboratory of R.C.F. is funded by a Core Programme Grant from the Medical Research Council. We thank the Human Research Tissue Bank, which is supported by the UK National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, from Addenbrooke’s Hospital. Additional infrastructure support was provided from the Cancer Research UK–funded Experimental Cancer Medicine Centre.

Author information

Authors and Affiliations




R.C.F. and A.M.F. conceived the overall study. A.M.F. and S.J. analyzed the genomic data and performed statistical analyses. R.C.F., A.M.F. and X.L. designed the experiments. A.M.F., X.L. and J.M. performed the experiments. G.C. contributed to the structural variant analysis and data visualization. S.K. helped compile the clinical data and aided in statistical analyses. J.P. and S.A. produced and performed quality control on the RNA-seq data. E.O. aided in WGS of EAC cell lines. S.M. and N.G. coordinated the clinical centers and were responsible for sample collection. M.D.E. benchmarked our mutation-calling pipelines. M.O. led the pathological sample quality control for sequencing. L.B. and G.D. constructed and managed the sequencing alignment and variant-calling pipelines. R.C.F. and S.T. supervised the research. R.C.F. and S.T. obtained funding. A.M.F. and R.C.F. wrote the manuscript. All authors approved the manuscript.

Corresponding author

Correspondence to Rebecca C. Fitzgerald.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12, Supplementary Tables 8, 10, 12 and 13, and Supplementary Note

Reporting Summary

Supplementary Tables 1–7, 9 and 11

Supplementary Data

Lollipop plots

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Frankell, A.M., Jammula, S., Li, X. et al. The landscape of selection in 551 esophageal adenocarcinomas defines genomic biomarkers for the clinic. Nat Genet 51, 506–516 (2019).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


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