title: Non-linear Bayesian framework to determine the transcriptional effects of cancer-associated genomic aberrations.
abstract: While the tumorigenic effects of specific recurrent mutations in known cancer driver-genes is well-characterized, not much is known about the functional relevance of the vast majority of recurrent mutations observed across cancers. Prior studies have attempted to identify functional genomic aberrations by integrating multi-omics measurements in cancer samples with community-curated biological pathway networks. However, the majority of these approaches overlook the following biological considerations: i) signaling pathway networks are highly tissue-specific and their regulatory interactions differ across tissue types; ii) regulatory factors exhibit heterogeneous influence on downstream gene transcription; iii) epigenetic and genomic alterations exhibit nonlinear impact on gene transcription. In order to accommodate these biological effects, we propose a hybrid Bayesian method to learn tissue-specific pairwise influence models amongst genes and to predict a gene's expression level as a nonlinear-function of its epigenetic and regulatory influences. We employ a novel tree-based depth-penalization mechanism in order to capture the higher regulatory impact of closer neighbors in the regulatory network. Using a breast cancer multi-omics dataset (N=1190), we show that our proposed method has superior prediction power over optimization-based regression models, with the additional advantage of revealing gene deregulations potentially driven by somatic mutations.
authors.forename: Abolfazl
authors.initials: A
authors.lastname: Razi
authors.forename: Nilanjana
authors.initials: N
authors.lastname: Banerjee
authors.forename: Nevenka
authors.initials: N
authors.lastname: Dimitrova
authors.forename: Vinay
authors.initials: V
authors.lastname: Varadan
mesh_terms: D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism; D057890:Epigenomics / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009154:Mutation; D017711:Nonlinear Dynamics