Haan, D. | bioRxiv Science

bioRxiv2019

Using Transcriptional Signatures to Find Cancer Drivers with LURE

Cancer genome projects have produced multidimensional datasets on thousands of samples. Yet, depending on the tumor type, 5-50% of samples have no known driving event. We introduce a semi-supervised method called Learning UnRealized Events (LURE) that uses a progressive label learning framework and minimum spanning analysis to predict cancer drivers based on their altered samples sharing a gene expression signature with the samples of a known event. We demonstrate the utility of the method on the TCGA dataset for which it produced a high-confidence result relating 53 new to 18 known mutation events including alterations in the same gene, family, and pathway. We give examples of predicted drivers involved in TP53, telomere maintenance, and MAPK/RTK signaling pathways. LURE identifies connections between genes with no known prior relationship, some of which may offer clues for targeting specific forms of cancer. Code and Supplemental Material are available on the LURE website https://sysbiowiki.soe.ucsc.edu/lure.

systems biology↗

bioRxiv2018

Pathway and network analysis of more than 2,500 whole cancer genomes

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We found that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

cancer biology↗