Abstract
Cancer is driven by mutations of many types: single nucleotide variants, insertion-deletion events within coding exons, and prolific DNA copy-number alterations (CNAs). CNAs have been often viewed as a passenger event due to seemingly inconsistent patterns between tumors and a large fraction of the genome altered, preventing traditional statistical analysis. Here, we present a tool which incorporates phenotypic data including haploinsufficiency and triploproficiency of genes and protein-protein interaction studies, which enables the identification of consistently altered pathways in cancer. This tool, HAPTRIG (for HAPloinsufficient and TRIplosensitive Gene) can be used with RNA, log2 CNA, absolute CNA data, or any other normalized gene-level data. As part of the pathway scoring process, control CNA networks are generated to provide a permuted-genome control, enabling detection of alterations in very heterogeneous cancer backgrounds. The customizable gene-scores which build these networks allow prioritization of altered network hubs: single genes which have exceptional influence within an altered pathway, facilitating hypothesis-testing in cell and animal models. A beta-version was validated in a recent publication from our lab to better predict tumor suppressor genes and oncogenes than Gene Set Enrichment Analysis, and additionally identified targetable pathway hubs which improved murine treatment in ovarian cancer chemo-resistant models. However, its applicability was limited in the beta. Now HAPTRIG can work with any user-supplied human dataset, not just TCGA samples, is an order of magnitude faster, able to run in parallel processors for unlimited speed improvement, and can produce pan-pathway outputs. The tool can use expected background rates from permuted data or compare to a second control dataset, which can be used to compare altered CNA pathways in tumor subtypes or unusual alterations of RNA compared to CNAs, among many other possibilities. We provide this tool for free community use online and in App form, so molecular biologists without bioinformatics training may produce well-informed hypotheses. We find that altered CNA pathways can predict chemoresistance risk groups within tumor types. A tumor suppressor gene hub was found to accelerate tumorigenesis upon deletion in a spontaneous mouse model. Overall, HAPTRIG represents a user-friendly tool to better understand a whole new landscape of tumor mutations: that of copy number alterations.
Citation Format: Joe Ryan Delaney, Dwayne G. Stupack. Customizable weighted DNA copy-number networks inform oncogenic pathway alterations and chemoresistance risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2294.
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