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

Identifying driver genes in unstable, heterogenous tumour types can be challenging. Here, Mourikis, Benedetti, Foxall and colleagues present a machine learning algorithm to tackle this problem in esophageal adenocarcinoma.

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Detalles Bibliográficos
Autores principales:	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, The Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium, Francesca D. Ciccarelli
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/6525c04e989d4f92801eb2e25637bf6b
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Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma

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