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Predicting and characterizing a cancer dependency map of tumors with deep learning.
Chiu, Yu-Chiao; Zheng, Siyuan; Wang, Li-Ju; Iskra, Brian S; Rao, Manjeet K; Houghton, Peter J; Huang, Yufei; Chen, Yidong.
Afiliação
  • Chiu YC; Greehey Children's Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
  • Zheng S; Greehey Children's Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
  • Wang LJ; Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
  • Iskra BS; Greehey Children's Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
  • Rao MK; Greehey Children's Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
  • Houghton PJ; Greehey Children's Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
  • Huang Y; Department of Cell Systems and Anatomy, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
  • Chen Y; Greehey Children's Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
Sci Adv ; 7(34)2021 08.
Article em En | MEDLINE | ID: mdl-34417181
ABSTRACT
Genome-wide loss-of-function screens have revealed genes essential for cancer cell proliferation, called cancer dependencies. It remains challenging to link cancer dependencies to the molecular compositions of cancer cells or to unscreened cell lines and further to tumors. Here, we present DeepDEP, a deep learning model that predicts cancer dependencies using integrative genomic profiles. It uses a unique unsupervised pretraining that captures unlabeled tumor genomic representations to improve the learning of cancer dependencies. We demonstrated DeepDEP's improvement over conventional machine learning methods and validated the performance with three independent datasets. By systematic model interpretations, we extended the current dependency maps with functional characterizations of dependencies and a proof-of-concept in silico assay of synthetic essentiality. We applied DeepDEP to pan-cancer tumor genomics and built the first pan-cancer synthetic dependency map of 8000 tumors with clinical relevance. In summary, DeepDEP is a novel tool for investigating cancer dependency with rapidly growing genomic resources.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Neoplasias Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Sci Adv Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Neoplasias Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Sci Adv Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos