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

Research output: Contribution to journalArticlepeer-review

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Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma. / Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium; 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; Ciccarelli, Francesca D.

In: Nature Communications, Vol. 10, 3101, 15.07.2019.

Research output: Contribution to journalArticlepeer-review

Harvard

Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium, Mourikis, TP, Benedetti, L, Foxall, E, Temelkovski, D, Nulsen, J, Perner, J, Cereda, M, Lagergren, J, Howell, M, Yau, C, Fitzgerald, RC, Scaffidi, P & Ciccarelli, FD 2019, 'Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma', Nature Communications, vol. 10, 3101. https://doi.org/10.1038/s41467-019-10898-3

APA

Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium, Mourikis, T. P., Benedetti, L., Foxall, E., Temelkovski, D., Nulsen, J., Perner, J., Cereda, M., Lagergren, J., Howell, M., Yau, C., Fitzgerald, R. C., Scaffidi, P., & Ciccarelli, F. D. (2019). Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma. Nature Communications, 10, [3101]. https://doi.org/10.1038/s41467-019-10898-3

Vancouver

Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium, Mourikis TP, Benedetti L, Foxall E, Temelkovski D, Nulsen J et al. Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma. Nature Communications. 2019 Jul 15;10. 3101. https://doi.org/10.1038/s41467-019-10898-3

Author

Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium ; 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 ; Ciccarelli, Francesca D. / Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma. In: Nature Communications. 2019 ; Vol. 10.

Bibtex

@article{637f1f1172674e9e8a34766de96198f4,
title = "Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma",
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.",
author = "{Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium} and Mourikis, {Thanos P} and Lorena Benedetti and Elizabeth Foxall and Damjan Temelkovski and Joel Nulsen and Juliane Perner and Matteo Cereda and Jesper Lagergren and Michael Howell and Christopher Yau and Fitzgerald, {Rebecca C} and Paola Scaffidi and Ciccarelli, {Francesca D}",
year = "2019",
month = jul,
day = "15",
doi = "10.1038/s41467-019-10898-3",
language = "English",
volume = "10",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Springer",

}

RIS

TY - JOUR

T1 - Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma

AU - Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium

AU - Mourikis, Thanos P

AU - Benedetti, Lorena

AU - Foxall, Elizabeth

AU - Temelkovski, Damjan

AU - Nulsen, Joel

AU - Perner, Juliane

AU - Cereda, Matteo

AU - Lagergren, Jesper

AU - Howell, Michael

AU - Yau, Christopher

AU - Fitzgerald, Rebecca C

AU - Scaffidi, Paola

AU - Ciccarelli, Francesca D

PY - 2019/7/15

Y1 - 2019/7/15

N2 - 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.

AB - 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.

U2 - 10.1038/s41467-019-10898-3

DO - 10.1038/s41467-019-10898-3

M3 - Article

C2 - 31308377

VL - 10

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 3101

ER -