Open AccessA novel computational OMICS and non-OMICS approach for identifying true pathogenic risk variants for Asian prostate cancer.
Author(s) -
Anusha Chimmiri,
Haitao Wang,
Eugenia Li Ling Yeo,
Kay Soon Low,
A. Tan,
Wai Yee Woo,
Enya Hui Wen Ong,
Terence Tan,
Wen Shen Looi,
Wen Long Nei,
Jeffrey Tuan,
Michael Lian Chek Wang,
Tan Jian-cheng,
Lui Shiong Lee,
Kiang-Hiong Tay,
Ravindran Kanesvaran,
Li Yan Khor,
Joe Yeong,
Chien Sheng Tan,
Melvin Lee Kiang Chua
Publication year - 2019
Publication title -
journal of global oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.002
H-Index - 17
ISSN - 2378-9506
DOI - 10.1200/jgo.2019.5.suppl.47
Subject(s) - omics , germline , prostate cancer , computational biology , 1000 genomes project , exome sequencing , precision medicine , genome wide association study , personalized medicine , bioinformatics , biology , cancer , medicine , exome , genetics , gene , single nucleotide polymorphism , phenotype , genotype
47 Background: Large-scale genome-wide association studies have established germline polygenic risk loci that underpin the susceptibility to prostate cancer (PCa). However, most trials conducted are in men of European ancestry with data missing for Asian male PCa. Here, we report on an in-house multidimensional bioinformatics pipeline that integrates OMICS and non-OMICS approaches in identifying true germline risk-variants for PCa in Asian men. Methods: We utilized a prospective cohort study of Asian men who were newly diagnosed with PCa. Whole exome sequencing (Illumina Hiseq, CA) of blood (100X) was performed. The OMICS-based approach entailed a stepwise screen for hallmarks of cancer-specific pathways. A genome-proteome network was then developed to filter for known pathogenic variants; this was followed by comparison against a large artificial database of aggregated germline variants (N = 95,000) with reported linkage to PCa susceptibility. Finally, mutations were filtered through a non-OMICS pipeline that entailed data synchronization with population-level statistics and clinical outcomes (recurrence and survival). Results: Preliminary analyses were based on 277 PCa cases; of which 50 were M1 cases. Screening using a non-combined unbiased approach yielded 36,157 germline variants. This contrast against our OMICS-based approach, which reduced the variant calls to 6,144 significantly associated mutations. Next, by focusing on pathway-specific genes related to hormonal regulation and known cancer hotspot mutations, we could further tighten our variant calls to 3,562 hormone-related variants (rs9269958 on HLA-DRB1) and 2,125 variants in known cancer genes, notably (rs8176320 on BRCA1/2, rs2555691 on LILRA2, rs8036934 on TP53BP1). Conclusions: Here, we show that application of an OMICS approach that combines pathway-driven analyses and an artificial dataset, along with population-level statistics and clinical relevance resulted in more robust annotation of germline variants that were associated with PCa.