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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.

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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.


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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





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.

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Correspondence to Rebecca C. Fitzgerald.

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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).

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