RESUMO
MOTIVATION: Accurate classification of patients into molecular subgroups is critical for the development of effective therapeutics and for deciphering what drives these subgroups to cancer. The availability of multiomics data catalogs for large cohorts of cancer patients provides multiple views into the molecular biology of the tumors with unprecedented resolution. RESULTS: We develop Pathway-based MultiOmic Graph Kernel clustering (PAMOGK) that integrates multiomics patient data with existing biological knowledge on pathways. We develop a novel graph kernel that evaluates patient similarities based on a single molecular alteration type in the context of a pathway. To corroborate multiple views of patients evaluated by hundreds of pathways and molecular alteration combinations, we use multiview kernel clustering. Applying PAMOGK to kidney renal clear cell carcinoma (KIRC) patients results in four clusters with significantly different survival times (P-value =1.24e-11). When we compare PAMOGK to eight other state-of-the-art multiomics clustering methods, PAMOGK consistently outperforms these in terms of its ability to partition KIRC patients into groups with different survival distributions. The discovered patient subgroups also differ with respect to other clinical parameters such as tumor stage and grade, and primary tumor and metastasis tumor spreads. The pathways identified as important are highly relevant to KIRC. AVAILABILITY AND IMPLEMENTATION: github.com/tastanlab/pamogk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.