Abstract
In this work, we propose iProFun, an integrative analysis tool to screen for proteogenomic functional traits perturbed by DNA copy number alterations (CNAs) and DNA methylations. The goal is to characterize functional consequences of DNA copy number and methylation alterations in tumors and to facilitate screening for cancer drivers contributing to tumor initiation and progression. Specifically, we consider three functional molecular quantitative traits: mRNA expression levels, global protein abundances, and phosphoprotein abundances. We aim to identify those genes whose CNAs and/or DNA methylations have cis-associations with either some or all three types of molecular traits. Compared with analyzing each molecular trait separately, the joint modeling of multi-omics data enjoys several benefits: iProFun experienced enhanced power for detecting significant cis-associations shared across different omics data types, and it also achieved better accuracy in inferring cis-associations unique to certain type(s) of molecular trait(s). For example, unique associations of CNAs/methylations to global/phospho protein abundances may imply posttranslational regulations.We applied iProFun to ovarian high-grade serous carcinoma tumor data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and identified CNAs and methylations of 500 and 121 genes, respectively, affecting the cis-functional molecular quantitative traits of the corresponding genes. We observed substantial power gain via the joint analysis of iProFun. For example, iProFun identified 117 genes whose CNAs were associated with phosphoprotein abundances by leveraging mRNA expression levels and global protein abundances. By comparison, analyses based on phosphoprotein data alone identified none. A network analysis of these 117 genes revealed the known oncogene AKT1 as a key hub node interacting with many of the rest. In addition, iProFun identified one gene, BIN2, whose DNA methylation has cis-associations with its mRNA expression, global protein, and phosphoprotein abundances. These and other genes identified by iProFun could serve as potential drug targets for ovarian cancer.
Highlights
The initiation, progression and metastasis of cancer often results from accumulation of DNA-level variations, such as DNA copy number alterations (CNAs) and epigenetic modifications
Using iProFun we identified a collection of genes whose molecular functional traits at transcriptomic, proteomic and/or phosphoproteomic levels were altered by somatic CNAs and DNA methylations
To characterize the impact of CNAs and DNA methylations on mRNA, protein and phosphoprotein abundances in ovarian tumors, we applied iProFun on TCGA and CPTAC data as described in Methods
Summary
The initiation, progression and metastasis of cancer often results from accumulation of DNA-level variations, such as DNA copy number alterations (CNAs) and epigenetic modifications. It is important to distinguish the driver genes that contribute to oncogenesis and cancer progression from the passengers acquired by random alterations during cancer evolution [2] and changes in gene activities that are the consequences, not causes, of cancer. To address this challenge, previous studies have primarily focused on associating CNAs and DNA methylations to their cis (i.e., local) gene expression levels [15], a form of molecular quantitative trait (QT) that is relatively easier to measure than protein abundances. Significant associations of CNAs and methylations with cis-mRNA expression levels partially reveal the molecular mechanisms of cancer-associated genes
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