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Qian, D.C.* ; Byun, J.* ; Han, Y.* ; Greene, C.S.* ; Field, J.K.* ; Hung, R.J.* ; Brhane, Y.* ; McLaughlin, J.R.* ; Fehringer, G.* ; Landi, M.T.* ; Rosenberger, A.* ; Bickeböller, H.* ; Malhotra, J.* ; Risch, A.* ; Heinrich, J. ; Hunter, D.J.* ; Henderson, B.E.* ; Haiman, C.A.* ; Schumacher, F.R.* ; Eeles, R.A.* ; Easton, D.F.* ; Seminara, D.* ; Amos, C.I.*

Identification of shared and unique susceptibility pathways among cancers of the lung, breast, and prostate from genome-wide association studies and tissue-specific protein interactions.

Hum. Mol. Genet. 24, 7406-7420 (2015)

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Results from genome-wide association studies (GWAS) have indicated that strong single-gene effects are the exception, not the rule, for most diseases. We assessed the joint effects of germline genetic variations through a pathway-based approach that considers the tissue-specific contexts of GWAS findings. From GWAS meta-analyses of lung cancer (12 160 cases/16 838 controls), breast cancer (15 748 cases/18 084 controls) and prostate cancer (14 160 cases/12 724 controls) in individuals of European ancestry, we determined the tissue-specific interaction networks of proteins expressed from genes that are likely to be affected by disease-associated variants. Reactome pathways exhibiting enrichment of proteins from each network were compared across the cancers. Our results show that pathways associated with all three cancers tend to be broad cellular processes required for growth and survival. Significant examples include the nerve growth factor (P = 7.86 × 10(-33)), epidermal growth factor (P = 1.18 × 10(-31)) and fibroblast growth factor (P = 2.47 × 10(-31)) signaling pathways. However, within these shared pathways, the genes that influence risk largely differ by cancer. Pathways found to be unique for a single cancer focus on more specific cellular functions, such as interleukin signaling in lung cancer (P = 1.69 × 10(-15)), apoptosis initiation by Bad in breast cancer (P = 3.14 × 10(-9)) and cellular responses to hypoxia in prostate cancer (P = 2.14 × 10(-9)). We present the largest comparative cross-cancer pathway analysis of GWAS to date. Our approach can also be applied to the study of inherited mechanisms underlying risk across multiple diseases in general.

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