Identifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks
Authors: Kim Minsu , Huffman Jennifer E. , Justice Amy , Goethert Ian , Agasthya Greeshma ,   VA Million Veteran Program, Sun Yan , McArdle Rachel , Dellitalia Louis , Stephens Brady , Cho Kelly , Pyarajan Saiju , Mattocks Kristin , Harley John , Whittle Jeffrey , Mathew Roy , Beckham Jean , Smith River , Wells. John , Gutierrez Salvador , Hammer Kimberly , Iruvanti Pran , Ballas Zuhair , Mastorides Stephen , Moorman Jonathan , Gappy Saib , Klein Jon , Ratcliffe Nora , Palacio Ana , Okusaga Olaoluwa , Murdoch Maureen , Sriram Peruvemba , Argyres Dean P. , Connor Todd , Villareal Gerardo , Kinlay Scott , Yeh Shing Shing , Jhala Darshana , Tandon Neeraj , Chang Kyong-Mi , Aguayo Samuel , Cohen David , Sharma Satish , Hamner Mark , Liangpunsakul Suthat , Godschalk Michael , Oursler Kris Ann , Whooley Mary , Greco Jennifer , Ahuja Sunil , Constans Joseph , Meyer Paul , Rauchman Michael , Servatius Richard , Ramoni Rachel , Muralidhar Sumitra , Gaziano J. Michael , Gaddy Melinda , Wallbom Agnes , Norton James , Morgan Timothy , Stapley Todd , Liang Peter , Bhushan Sujata , Jacono Frank , Fujii Daryl , Tsao Philip , Humphries Donald E. , Huang Grant , Breeling James , Moser Jennifer , Brewer Jessica V. , Casas Juan P. , Cho Kelly , Churby Lori , Selva Luis E. , Brophy Mary T. , Do Nhan , Tsao Philip S. , Shayan Shahpoor Alex , Whitbourne Stacey B. , Strollo Patrick , Boyko Edward , Walsh Jessica , Pyarajan Saiju , Hauser Elizabeth , DuVall Scott L. , Gupta Samir , Huq Mostaqul , Fayad Joseph , Hung Adriana , Xu Junzhe , Alexander Kathrina , Hurley Robin , Lichy Jack , Zhao Hongyu , Wilson Peter , Robey Brooks , Balasubramanian Prakash , Danciu Ioana 
Published: July 2022

Background

Genome-wide Association Studies (GWAS) aims to uncover the link between genomic variation and phenotype. They have been actively applied in cancer biology to investigate associations between variations and cancer phenotypes, such as susceptibility to certain types of cancer and predisposed responsiveness to specific treatments. Since GWAS primarily focuses on finding associations between individual genomic variations and cancer phenotypes, there are limitations in understanding the mechanisms by which cancer phenotypes are cooperatively affected by more than one genomic variation.

Results

This paper proposes a network representation learning approach to learn associations among genomic variations using a prostate cancer cohort. The learned associations are encoded into representations that can be used to identify functional modules of genomic variations within genes associated with early- and late-onset prostate cancer. The proposed method was applied to a prostate cancer cohort provided by the Veterans Administration’s Million Veteran Program to identify candidates for functional modules associated with early-onset prostate cancer. The cohort included 33,159 prostate cancer patients, 3181 early-onset patients, and 29,978 late-onset patients. The reproducibility of the proposed approach clearly showed that the proposed approach can improve the model performance in terms of robustness.

Conclusions

To our knowledge, this is the first attempt to use a network representation learning approach to learn associations among genomic variations within genes. Associations learned in this way can lead to an understanding of the underlying mechanisms of how genomic variations cooperatively affect each cancer phenotype. This method can reveal unknown knowledge in the field of cancer biology and can be utilized to design more advanced cancer-targeted therapies.