So Yeon Kim1,2, Eun Kyung Choe2,3, Manu Shivakumar2, Dokyoon Kim2,4, Kyung-Ah Sohn1,5. 1. European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK. 2. Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. 3. Department of Surgery, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, South Korea. 4. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA. 5. Department of Artificial Intelligence, Ajou University, Suwon 16499, South Korea.
Abstract
MOTIVATION: To better understand the molecular features of cancers, a comprehensive analysis using multiomics data has been conducted. Additionally, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW, and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene-gene graph using pathway information by assigning interactions between genes in multiple layers of networks. RESULTS: : As a proof-of-concept study, it was evaluated using three genomic profiles of urologic cancer patients. The proposed integrative approach achieved improved outcome prediction performances compared with a single genomic profile alone and other existing pathway activity inference methods. The integrative approach also identified common/cancer-specific candidate driver pathways as predictive prognostic features in urologic cancers. Furthermore, it provides better biological insights into the prioritized pathways and genes in an integrated view using a multi-layered gene-gene network. Our framework is not specifically designed for urologic cancers and can be generally applicable for various datasets. AVAILABILITY: iDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: To better understand the molecular features of cancers, a comprehensive analysis using multiomics data has been conducted. Additionally, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW, and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene-gene graph using pathway information by assigning interactions between genes in multiple layers of networks. RESULTS: : As a proof-of-concept study, it was evaluated using three genomic profiles of urologic cancer patients. The proposed integrative approach achieved improved outcome prediction performances compared with a single genomic profile alone and other existing pathway activity inference methods. The integrative approach also identified common/cancer-specific candidate driver pathways as predictive prognostic features in urologic cancers. Furthermore, it provides better biological insights into the prioritized pathways and genes in an integrated view using a multi-layered gene-gene network. Our framework is not specifically designed for urologic cancers and can be generally applicable for various datasets. AVAILABILITY: iDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.