RT Journal Article
SR Electronic
T1 Multi-omic tumor data reveal diversity of molecular mechanisms underlying survival
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 267245
DO 10.1101/267245
A1 Ramazzotti, Daniele
A1 Lal, Avantika
A1 Wang, Bo
A1 Batzoglou, Serafim
A1 Sidow, Arend
YR 2018
UL http://biorxiv.org/content/early/2018/02/16/267245.abstract
AB Outcomes for cancer patients vary greatly even within the same tumor type, and characterization of molecular subtypes of cancer holds important promise for improving prognosis and personalized treatment. This promise has motivated recent efforts to produce large amounts of multidimensional genomic (‘multi-omic’) data, but current algorithms still face challenges in the integrated analysis of such data. Here we present Cancer Integration via Multikernel Learning (CIMLR; based on an algorithm originally developed for analysis of single-cell RNA-Seq data), a new cancer subtyping method that integrates multi-omic data to reveal molecular subtypes of cancer. We apply CIMLR to multi-omic data from 32 cancer types and show significant improvements in both computational efficiency and ability to extract biologically meaningful cancer subtypes. The discovered subtypes exhibit significant differences in patient survival for 21 of the 32 studied cancer types. Our analysis reveals integrated patterns of gene expression, methylation, point mutations and copy number changes in multiple cancers and highlights patterns specifically associated with poor patient outcomes.