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.
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Ferlay, J. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136, E359–E386 (2015).
Coleman, H. G., Xie, S. H. & Lagergren, J. The epidemiology of esophageal adenocarcinoma. Gastroenterology 154, 390–405 (2018).
Smyth, E. C. et al. Oesophageal cancer. Nat. Rev. Dis. Primers 3, 17048 (2017).
Campbell, P.J., Getz, G., Stuart, J.M., Korbel, J.O. & Stein, L.D. Pan-cancer analysis of whole genomes. Preprint at https://www.biorxiv.org/content/early/2017/07/12/162784 (2017).
Ciriello, G. et al. Emerging landscape of oncogenic signatures across human cancers. Nat. Genet. 45, 1127–1133 (2013).
Secrier, M. et al. Mutational signatures in esophageal adenocarcinoma define etiologically distinct subgroups with therapeutic relevance. Nat. Genet. 48, 1131–1141 (2016).
Tamborero, D. et al. Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci. Rep. 3, 2650 (2013).
Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).
Cancer Genome Atlas Research Network. et al. Integrated genomic characterization of oesophageal carcinoma. Nature 541, 169–175 (2017).
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).
Rheinbay, E. et al. Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes. Preprint at https://www.biorxiv.org/content/early/2017/12/23/237313 (2017).
Cancer Genome Atlas Research Network. Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507, 315–322 (2014).
Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202–209 (2014).
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).
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).
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).
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).
Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).
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).
Nones, K. et al. Genomic catastrophes frequently arise in esophageal adenocarcinoma and drive tumorigenesis. Nat. Commun. 5, 5224 (2014).
Martincorena I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041.e21.
Wadi, L. et al. Candidate cancer driver mutations in super-enhancers and long-range chromatin interaction networks. Preprint at https://www.biorxiv.org/content/early/2017/12/19/236802 (2017).
Gonzalez-Perez, A. & Lopez-Bigas, N. Functional impact bias reveals cancer drivers. Nucleic Acids Res. 40, e169 (2012).
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).
Porta-Pardo, E. & Godzik, A. e-Driver: a novel method to identify protein regions driving cancer. Bioinformatics 30, 3109–3114 (2014).
Porta-Pardo, E., Hrabe, T. & Godzik, A. Cancer3D: understanding cancer mutations through protein structures. Nucleic Acids Res. 43, D968–D973 (2015).
Futreal, P. A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).
Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).
Shuai, S., Gallinger, S. & Stein, L.D. DriverPower: combined burden and functional impact tests for cancer driver discovery. Preprint at https://www.biorxiv.org/content/early/2017/11/06/215244 (2017).
Taylor, A. M. et al. Genomic and functional approaches to understanding cancer aneuploidy. Cancer Cell 33, 676–689.e3 (2018).
Turner, K. M. et al. Extrachromosomal oncogene amplification drives tumour evolution and genetic heterogeneity. Nature 543, 122–125 (2017).
Chang, M. T. et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat. Biotechnol. 34, 155–163 (2016).
Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).
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).
Giannakis, M. et al. Genomic correlates of immune-cell infiltrates in colorectal carcinoma. Cell Rep. 17, 1206 (2016).
Pei, X. H. & Xiong, Y. Biochemical and cellular mechanisms of mammalian CDK inhibitors: a few unresolved issues. Oncogene 24, 2787–2795 (2005).
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).
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).
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).
Tamborero, D. et al. Cancer genome interpreter annotates the biological and clinical relevance of tumor alterations. Preprint at https://www.biorxiv.org/content/early/2017/06/21/140475 (2017).
Contino, G. et al. Whole-genome sequencing of nine esophageal adenocarcinoma cell lines. F1000Res. 5, 1336 (2016).
Liston, D. R. & Davis, M. Clinically relevant concentrations of anticancer drugs: a guide for nonclinical studies. Clin. Cancer Res. 23, 3489–3498 (2017).
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).
Li, X. et al. Organoid cultures recapitulate esophageal adenocarcinoma heterogeneity providing a model for clonality studies and precision therapeutics. Nat. Commun. 9, 2983 (2018).
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).
Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).
Grasso, C. S. et al. Genetic mechanisms of immune evasion in colorectal cancer. Cancer Discov. 8, 730–749 (2018).
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).
Ding, J. et al. Systematic analysis of somatic mutations impacting gene expression in 12 tumour types. Nat. Commun. 6, 8554 (2015).
Lee, A. Y. et al. Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection. Genome Biol. 19, 188 (2018).
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).
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).
Ng, P. C. & Henikoff, S. Predicting the effects of amino acid substitutions on protein function. Annu. Rev. Genomics Hum. Genet. 7, 61–80 (2006).
Reimand, J., Wagih, O. & Bader, G. D. The mutational landscape of phosphorylation signaling in cancer. Sci. Rep. 3, 2651 (2013).
Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017).
Wala, J.A. et al. Selective and mechanistic sources of recurrent rearrangements across the cancer genome. Preprint at https://www.biorxiv.org/content/early/2017/09/14/187609 (2017).
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).
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).
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.
The authors declare no competing interests.
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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). https://doi.org/10.1038/s41588-018-0331-5
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