RT Journal Article
SR Electronic
T1 PAMOGK: A Pathway Graph Kernel based Multi-Omics Clustering Approach for Discovering Cancer Patient Subgroups
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 834168
DO 10.1101/834168
A1 Yasin Ilkagan Tepeli
A1 Ali Burak Ünal
A1 Furkan Mustafa Akdemir
A1 Oznur Tastan
YR 2019
UL http://biorxiv.org/content/early/2019/11/07/834168.abstract
AB Accurate classification of patients into homogeneous molecular subgroups is critical for the development of effective therapeutics and for deciphering what drives these different subtypes to cancer. However, the extensive molecular heterogeneity observed among cancer patients presents a challenge. The availability of multi-omic data catalogs for large cohorts of cancer patients provides multiple views into the molecular biology of the tumors with unprecedented resolution. In this work, we develop PAMOGK, which integrates multi-omics patient data and incorporates the existing knowledge on biological pathways. PAMOGK is well suited to deal with the sparsity of alterations in assessing patient similarities. We develop a novel graph kernel which we denote as smoothed shortest path graph kernel, which evaluates patient similarities based on a single molecular alteration type in the context of pathway. To corroborate multiple views of patients evaluated by hundreds of pathways and molecular alteration combinations, PAMOGK uses multi-view kernel clustering. We apply PAMOGK to find subgroups of kidney renal clear cell carcinoma (KIRC) patients, which results in four clusters with significantly different survival times (p-value = 7.4e-10). The patient subgroups also differ with respect to other clinical parameters such as tumor stage and grade, and primary tumor and metastasis tumor spreads. When we compare PAMOGK to 8 other state-of-the-art existing multi-omics clustering methods, PAMOGK consistently outperforms these in terms of its ability to partition patients into groups with different survival distributions. PAMOGK enables extracting the relative importance of pathways and molecular data types. PAMOGK is available at github.com/tastanlab/pamogk