Discovery of Cancer Driver Long Noncoding RNAs across 1112 Tumour Genomes: New Candidates and Distinguishing Features

Abstract

Long noncoding RNAs (lncRNAs) represent a vast unexplored genetic space that may hold missing drivers of tumourigenesis, but few such “driver lncRNAs” are known. Until now, they have been discovered through changes in expression, leading to problems in distinguishing between causative roles and passenger effects. We here present a different approach for driver lncRNA discovery using mutational patterns in tumour DNA. Our pipeline, ExInAtor, identifies genes with excess load of somatic single nucleotide variants (SNVs) across panels of tumour genomes. Heterogeneity in mutational signatures between cancer types and individuals is accounted for using a simple local trinucleotide background model, which yields high precision and low computational demands. We use ExInAtor to predict drivers from the GENCODE annotation across 1112 entire genomes from 23 cancer types. Using a stratified approach, we identify 15 high-confidence candidates: 9 novel and 6 known cancer-related genes, including MALAT1, NEAT1 and SAMMSON. Both known and novel driver lncRNAs are distinguished by elevated gene length, evolutionary conservation and expression. We have presented a first catalogue of mutated lncRNA genes driving cancer, which will grow and improve with the application of ExInAtor to future tumour genome projects.

Discovering novel long non-coding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

“…Two recent efforts attempted to identify driver lncRNA mutations that were positively selected in human cancers (Juul et al 2017;Lanzós et al 2017). In some form, each method identified non-coding loci or non-coding genes that were mutated at a frequency significantly greater than the background rate across all non-coding loci or across samples, respectively.…”

“…However, these methods, by definition, cannot infer somatic mutations in the non-coding genome. Recent efforts have been directed toward identifying somatic drivers that are specifically located in the lncRNA genome (Li et al 2015;Lanzos et al 2017). To identify lncRNA genes with positively selected somatic mutations in the cancer cell lines, we adopt a two-step approach that first selects lncRNA genes with variation frequency higher than the noncoding genome background across all available cell lines, and second, scores lncRNA genes based on mutation frequency per cell line.…”

A pan‐cancer atlas of cancer hallmark‐associated candidate driver lncRNAs

“…To exclude potential confounders induced by proximal known cancer drivers, we extracted 235 candidate driver lncRNAs with any transcripts overlapping proteincoding genes on the opposite strand, or within 10 kb at their closest point on the same strand (Lanzos et al, 2017). In addition, their overlapped (or proximal) PCGs were also extracted.…”

“…To address this challenge, several methods have been proposed. ExInAtor employed a parametric statistical test to identify lncRNAs with excess of exonic mutations, accounting for individual mutational signatures (Lanzos et al, 2017). Onco-driveFML integrated several functional impact scoring metrics to identify driver regions with functional mutation bias (Mularoni et al, 2016).…”

Pan-cancer analysis of expressed somatic nucleotide variants in long intergenic non-coding RNA

“…The expression of thousands of lincRNAs are deregulated in cancer, and many lincRNAs have been proposed as biomarkers for tumor tissues and patient prognosis [1]–[3]. There is also strong evidence that lincRNAs may serve as drivers of tumorigenesis, cause drug resistance or cause metastasis [4]–[7]. Mutations in cancer driver genes lead to a series of downstream events, including gene expression changes [8], [9].…”