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

Published version
Peer-reviewed

Type

Article

Change log

Authors

Benedetti, Lorena 
Temelkovski, Damjan 
Nulsen, Joel 

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.

Description

Keywords

Adenocarcinoma, Antineoplastic Agents, Biomarkers, Tumor, Computational Biology, Datasets as Topic, Disease Progression, Esophageal Neoplasms, Gene Dosage, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Genomic Instability, Humans, Machine Learning, Models, Genetic, Multigene Family, Mutation Rate, Polymorphism, Single Nucleotide, Precision Medicine

Journal Title

Nat Commun

Conference Name

Journal ISSN

2041-1723
2041-1723

Volume Title

10

Publisher

Springer Science and Business Media LLC
Sponsorship
Cancer Research UK (20406)