Open AccessPredicting and characterizing a cancer dependency map of tumors with deep learning
Author(s) -
Yi-Chang Chiu,
Siyuan Zheng,
Li-Ju Wang,
Brian S. Iskra,
Manjeet K. Rao,
Peter J. Houghton,
Yufei Huang,
Yidong Chen
Publication year - 2021
Publication title -
science advances
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.928
H-Index - 146
ISSN - 2375-2548
DOI - 10.1126/sciadv.abh1275
Subject(s) - dependency (uml) , cancer , computational biology , deep learning , computer science , genomics , relevance (law) , cancer cell , cancer cell lines , in silico , artificial intelligence , genome , function (biology) , machine learning , biology , gene , genetics , political science , law
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.