Science Advances: Predicting and characterizing a cancer dependency map of tumors with deep learning (Aune, Chen, Rao, Houghton & Zheng Labs)
- Yu-Chiao Chiu1,
- Siyuan Zheng1,2,
- Li-Ju Wang1,
- View ORCID ProfileBrian S. Iskra1,
- View ORCID ProfileManjeet K. Rao1,3,
- Peter J. Houghton1,4,
- View ORCID ProfileYufei Huang5,6,* and
- View ORCID ProfileYidong Chen1,2,*
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.
Since 2004, UT Health San Antonio, Greehey Children’s Cancer Research Institute’s (Greehey CCRI) mission has been to advance scientific knowledge relevant to childhood cancer, contribute to the understanding of its causes, and accelerate the translation of knowledge into novel therapies. Through discovery, development, and dissemination of new scientific knowledge, Greehey CCRI strives to have a national and global impact on childhood cancer. Our mission consists of three key areas — research, clinical, and education.