Patient-specific detection of cancer genes reveals recurrently perturbed processes in esophageal adenocarcinoma

Abstract

The identification of somatic alterations with a cancer promoting role is challenging in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we developed a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously (mutations, copy number alterations and structural rearrangements). Analysing 261 EACs from the OCCAMS Consortium, we discovered a large number of novel cancer genes that, together with well-known drivers, help promote cancer. Validation using 107 additional EACs confirmed the robustness of the approach. Unlike known drivers whose alterations recur across patients, the large majority of the newly discovered cancer genes are rare or patient-specific. Despite this, they converge towards perturbing cancer-related processes, including intracellular signalling, cell cycle regulation, proteasome activity and Toll-like receptor signalling. Recurrence of process perturbation, rather than individual genes, divides EACs into six clusters that differ in their molecular and clinical features and suggest patient stratifications for personalised treatments. By experimentally mimicking or reverting alterations of predicted cancer genes, we validated their contribution to cancer progression and revealed EAC acquired dependencies, thus demonstrating their potential as therapeutic targets.