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Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma

2019; Nature Portfolio; Volume: 10; Issue: 1 Linguagem: Inglês

10.1038/s41467-019-10898-3

2041-1723

Thanos P. Mourikis, Lorena Benedetti, Elizabeth Foxall, Damjan Temelkovski, Joel Nulsen, Juliane Perner, Matteo Cereda, Jesper Lagergren, Michael Howell, Christopher Yau, Rebecca C. Fitzgerald, Paola Scaffidi, Ayesha Noorani, Paul A. Edwards, Rachael Fels Elliott, Nicola Grehan, Barbara Nutzinger, Caitriona Hughes, Elwira Fidziukiewicz, Jan Bornschein, Shona MacRae, Jason Crawte, Alex Northrop, Gianmarco Contino, Xiaodun Li, Rachel de la Rue, Annalise Katz‐Summercorn, Sujath Abbas, Daniel Loureda, Maria O’Donovan, Ahmad Miremadi, Shalini Malhotra, Monika Tripathi, Simon Tavaré, Andy G. Lynch, Matthew Eldridge, Maria Secrier, Lawrence Bower, Ginny Devonshire, Sriganesh Jammula, Jim Davies, Charles Crichton, Nick Carroll, Peter Safranek, Andrew Hindmarsh, Vijayendran Sujendran, Stephen J. Hayes, Yeng Ang, Andrew D Sharrocks, Shaun R. Preston, Sarah Oakes, Izhar Bagwan, Vicki Save, Richard J. E. Skipworth, Ted R. Hupp, J. Robert O’Neill, Olga Tucker, Andrew D. Beggs, Philippe Tanière, Susana Puig, Timothy J. Underwood, Robert Walker, Ben Grace, Hugh Barr, Neil A. Shepherd, Oliver Old, James Gossage, Andrew Davies, Fuju Chang, Janine Zylstra, Ula Mahadeva, Vicky Goh, Grant Sanders, Richard Berrisford, Catherine Harden, Mike Lewis, Ed Cheong, Bhaskar Kumar, Simon L. Parsons, Irshad Soomro, Philip Kaye, John Saunders, Laurence Lovat, Rehan Haidry, Laszlo Igali, Michael A. Scott, Sharmila Sothi, Sari Suortamo, Suzy Lishman, George B. Hanna, Christopher J. Peters, Krishna Moorthy, Anna M. Grabowska, Richard Turkington, Damian McManus, David Khoo, W E Fickling, Francesca D. Ciccarelli,

Epigenetics and DNA Methylation

Abstract The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously. Analysing 261 EACs from the OCCAMS Consortium, we discover helper genes that, alongside well-known drivers, promote cancer. We confirm the robustness of our approach in 107 additional EACs. Unlike recurrent alterations of known drivers, these cancer helper genes are rare or patient-specific. However, they converge towards perturbations of well-known cancer processes. Recurrence of the same process perturbations, rather than individual genes, divides EACs into six clusters differing in their molecular and clinical features. Experimentally mimicking the alterations of predicted helper genes in cancer and pre-cancer cells validates their contribution to disease progression, while reverting their alterations reveals EAC acquired dependencies that can be exploited in therapy.