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Discovering functional evolutionary dependencies in human cancers.
Mina, Marco; Iyer, Arvind; Tavernari, Daniele; Raynaud, Franck; Ciriello, Giovanni.
Afiliação
  • Mina M; Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
  • Iyer A; Swiss Cancer Center Leman, Lausanne, Switzerland.
  • Tavernari D; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
  • Raynaud F; Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
  • Ciriello G; Swiss Cancer Center Leman, Lausanne, Switzerland.
Nat Genet ; 52(11): 1198-1207, 2020 11.
Article em En | MEDLINE | ID: mdl-32989323
ABSTRACT
Cancer cells retain genomic alterations that provide a selective advantage. The prediction and validation of advantageous alterations are major challenges in cancer genomics. Moreover, it is crucial to understand how the coexistence of specific alterations alters response to genetic and therapeutic perturbations. In the present study, we inferred functional alterations and preferentially selected combinations of events in >9,000 human tumors. Using a Bayesian inference framework, we validated computational predictions with high-throughput readouts from genetic and pharmacological screenings on 2,000 cancer cell lines. Mutually exclusive and co-occurring cancer alterations reflected, respectively, functional redundancies able to rescue the phenotype of individual target inhibition, or synergistic interactions, increasing oncogene addiction. Among the top scoring dependencies, co-alteration of the phosphoinositide 3-kinase (PI3K) subunit PIK3CA and the nuclear factor NFE2L2 was a synergistic evolutionary trajectory in squamous cell carcinomas. By integrating computational, experimental and clinical evidence, we provide a framework to study the combinatorial functional effects of cancer genomic alterations.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Evolução Molecular / Biologia Computacional / Neoplasias Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Evolução Molecular / Biologia Computacional / Neoplasias Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article