A pathway graph kernel based multi-omics approach for patient clustering

Official URL: https://risc01.sabanciuniv.edu/record=b2486391 _(Table of contents)

Accurate classification of patients into molecular subgroups is critical for the development of effective therapeutics and for deciphering the underlining mechanisms for these subgroups. The availability of multi-omics data catalogs for large cohorts of cancer patients provides multiple views into the molecular biology of the tumors and the alterations that take place in patient genes such as mutations and differential expression patterns. At the same time, the molecular interaction networks provide the biological context for these alterations. We develop PAMOGK (Pathway based Multi Omic Graph Kernel clustering framework) that integrates multi-omics patient data with existing biological knowledge on pathways. We use 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 that are evaluated by hundreds of pathways and molecular alteration combinations, we use a multi-view kernel clustering approach. 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 multi-omics 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. We also extend our analysis to eight other cancer types with available mutation, protein and gene expression data. PAMOGK framework is available in github.com/tastanlab/pamogk