Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma
Creators: name: Mourikis, Thanos P.
name: Benedetti, Lorena
name: Foxall, Elizabeth
name: Temelkovski, Damjan
name: Nulsen, Joel
name: Perner, Juliane
name: Cereda, Matteo
name: Lagergren, Jesper
name: Howell, Michael
name: Yau, Christopher
name: Fitzgerald, Rebecca C.
name: Scaffidi, Paola
name: Ciccarelli, Francesca D.
name: Noorani, Ayesha
name: Edwards, Paul A. W.
name: Elliott, Rachael Fels
name: Grehan, Nicola
name: Nutzinger, Barbara
name: Hughes, Caitriona
name: Fidziukiewicz, Elwira
name: Bornschein, Jan
name: MacRae, Shona
name: Crawte, Jason
name: Northrop, Alex
name: Contino, Gianmarco
name: Li, Xiaodun
name: De La Rue, Rachel
name: Katz-Summercorn, Annalise
name: Abbas, Sujath
name: Loureda, Daniel
name: O’Donovan, Maria
name: Miremadi, Ahmad
name: Malhotra, Shalini
name: Tripathi, Monika
name: Tavaré, Simon
name: Lynch, Andy G.
name: Eldridge, Matthew
name: Secrier, Maria
name: Bower, Lawrence
name: Devonshire, Ginny
name: Jammula, Sriganesh
name: Davies, Jim
name: Crichton, Charles
name: Carroll, Nick
name: Safranek, Peter
name: Hindmarsh, Andrew
name: Sujendran, Vijayendran
name: Hayes, Stephen J.
name: Ang, Yeng
name: Sharrocks, Andrew
name: Preston, Shaun R.
name: Oakes, Sarah
name: Bagwan, Izhar
name: Save, Vicki
name: Skipworth, Richard J. E.
name: Hupp, Ted R.
name: Robert O’Neill, J.
name: Tucker, Olga
name: Beggs, Andrew
name: Taniere, Philippe
name: Puig, Sonia
name: Underwood, Timothy J.
name: Walker, Robert C.
name: Grace, Ben L.
name: Barr, Hugh
name: Shepherd, Neil
name: Old, Oliver
name: Gossage, James
name: Davies, Andrew
name: Chang, Fuju
name: Zylstra, Janine
name: Mahadeva, Ula
name: Goh, Vicky
name: Sanders, Grant
name: Berrisford, Richard
name: Harden, Catherine
name: Lewis, Mike
name: Cheong, Ed
name: Kumar, Bhaskar
name: Parsons, Simon L.
name: Soomro, Irshad
name: Kaye, Philip
name: Saunders, John
name: Lovat, Laurence
name: Haidry, Rehan
name: Igali, Laszlo
name: Scott, Michael
name: Sothi, Sharmila
name: Suortamo, Sari
name: Lishman, Suzy
name: Hanna, George B.
name: Peters, Christopher J.
name: Moorthy, Krishna
name: Grabowska, Anna
name: Turkington, Richard
name: McManus, Damian
name: Khoo, David
name: Fickling, Will
Publisher: name: Apollo - University of Cambridge Repository
Year: 2019
Subjects: Article
/631/67
/631/67/69
/631/67/1504/1477
/13/44
/13/31
/13/109
/38/22
/38/23
/38/77
/38/88
/38/90
article

description: 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.