Integrating omics data and protein interaction networks to prioritize driver genes in cancer.

In this study, we presented a novel framework to identify driver genes through integrating multi-omics data such as somatic mutation, gene expression, and copy number alterations. We developed a computational approach to detect potential driver genes by virtue of their effect on their neighbors in network. Application to three datasets (head and neck squamous cell carcinoma (HNSC), thyroid carcinoma (THCA) and kidney renal clear cell carcinoma (KIRC)) from The Cancer Genome Atlas (TCGA), by comparing the Precision, Recall and F1 score, our method outperformed DriverNet and MUFFINN in all three datasets. In addition, our method was less affected by protein length compared with DriverNet. Lastly, our method not only identified the known cancer genes but also detected the potential rare drivers (PTPN6 in THCA, SRC, GRB2 and PTPN6 in KIRC, MAPK1 and SMAD2 in HNSC). PMID: 28779046 [PubMed - as supplied by publisher]
Source: Oncotarget - Category: Cancer & Oncology Tags: Oncotarget Source Type: research