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

Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma
Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma
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.
2041-1723
1-17
Mourikis, Thanos P.
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Benedetti, Lorena
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Foxall, Elizabeth
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Temelkovski, Damjan
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Nulsen, Joel
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Perner, Juliane
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Cereda, Matteo
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Lagergren, Jesper
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Howell, Michael
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Yau, Christopher
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Fitzgerald, Rebecca C.
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Scaffidi, Paola
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Underwood, Timothy
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Ciccarelli, Francesca D.
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OCCAMS Consortium
Mourikis, Thanos P.
7cdcefa0-4427-4696-b17d-6799dab49d2f
Benedetti, Lorena
243b4262-9e1c-47db-a51c-dc7c7dd9249f
Foxall, Elizabeth
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Temelkovski, Damjan
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Nulsen, Joel
bd2460b1-a721-45e7-b360-476e3afc3597
Perner, Juliane
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Cereda, Matteo
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Lagergren, Jesper
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Howell, Michael
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Yau, Christopher
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Fitzgerald, Rebecca C.
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Scaffidi, Paola
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Underwood, Timothy
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Ciccarelli, Francesca D.
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Mourikis, Thanos P., Benedetti, Lorena, Foxall, Elizabeth, Temelkovski, Damjan, Nulsen, Joel, Perner, Juliane, Cereda, Matteo, Lagergren, Jesper, Howell, Michael, Yau, Christopher, Fitzgerald, Rebecca C., Scaffidi, Paola and Ciccarelli, Francesca D. , OCCAMS Consortium (2019) Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma. Nature Communications, 10, 1-17, [3101]. (doi:10.1038/s41467-019-10898-3).

Record type: Article

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.

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Accepted/In Press date: 4 June 2019
Published date: 15 July 2019

Identifiers

Local EPrints ID: 432694
URI: http://eprints.soton.ac.uk/id/eprint/432694
ISSN: 2041-1723
PURE UUID: 336bc3c2-2479-42e7-a4cc-d4e9a690cf30
ORCID for Timothy Underwood: ORCID iD orcid.org/0000-0001-9455-2188

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Date deposited: 24 Jul 2019 16:30
Last modified: 16 Mar 2024 03:35

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Contributors

Author: Thanos P. Mourikis
Author: Lorena Benedetti
Author: Elizabeth Foxall
Author: Damjan Temelkovski
Author: Joel Nulsen
Author: Juliane Perner
Author: Matteo Cereda
Author: Jesper Lagergren
Author: Michael Howell
Author: Christopher Yau
Author: Rebecca C. Fitzgerald
Author: Paola Scaffidi
Author: Francesca D. Ciccarelli
Corporate Author: OCCAMS Consortium

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