Genome Sequencing and RNA-Motif Analysis Reveal Novel Damaging Noncoding Mutations in Human Tumors

Babita Singh,Eduardo Eyras,Stephen R Piccolo,Juan L Trincado,Pj Tatlow

doi:10.1158/1541-7786.mcr-17-0601

Abstract

A major challenge in cancer research is to determine the biological and clinical significance of somatic mutations in noncoding regions. This has been studied in terms of recurrence, functional impact, and association to individual regulatory sites, but the combinatorial contribution of mutations to common RNA regulatory motifs has not been explored. Therefore, we developed a new method, MIRA (mutation identification for RNA alterations), to perform an unbiased and comprehensive study of significantly mutated regions (SMR) affecting binding sites for RNA-binding proteins (RBP) in cancer. Extracting signals related to RNA-related selection processes and using RNA sequencing (RNA-seq) data from the same specimens, we identified alterations in RNA expression and splicing linked to mutations on RBP binding sites. We found SRSF10 and MBNL1 motifs in introns, HNRPLL motifs at 5' UTRs, as well as 5' and 3' splice-site motifs, among others, with specific mutational patterns that disrupt the motif and impact RNA processing. MIRA facilitates the integrative analysis of multiple genome sites that operate collectively through common RBPs and aids in the interpretation of noncoding variants in cancer. MIRA is available at https://github.com/comprna/miraImplications: The study of recurrent cancer mutations on potential RBP binding sites reveals new alterations in introns, untranslated regions, and long noncoding RNAs that impact RNA processing and provide a new layer of insight that can aid in the interpretation of noncoding variants in cancer genomes. Mol Cancer Res; 16(7); 1112-24. ©2018 AACR.

Highlights

Cancer arises from genetic and epigenetic alterations that interfere with essential mechanisms of the normal life cycle of cells such as DNA repair, replication control, and cell death [1]
Compared with other existing approaches to detect relevant mutations in noncoding regions, our study provides several novelties and advantages: (i) we searched exhaustively along gene loci, increasing the potential to uncover deep intronic pathological mutations; (ii) we studied the enrichment of a large compendium of potential RNA regulatory motifs, allowing us to identify potentially novel mechanisms affecting RNA processing in cancer; (iii) we showed that multiple mutated genomic loci potentially interact with common RNA-binding proteins (RBP), suggesting novel cancer-related selection mechanisms; and (iv) unlike previous methods, we used RNA sequencing data from the same samples to measure the impact on RNA processing
We used somatic mutations from whole genome sequencing for 505 tumor samples from 14 tumor types: bladder carcinoma (BLCA; 21 samples), breast carcinoma (BRCA; 96 samples), colorectal carcinoma (42 samples), glioblastoma multiforme (GBM; 27 samples), head and neck squamous carcinoma (HNSC; 27 samples), kidney chromophobe (KICH; 15 samples), kidney renal carcinoma (KIRC; 29 samples), low grade glioma (LGG; 18 samples), lung adenocarcinoma (LUAD; 46 samples), lung squamous cell carcinoma (LUSC; 45 samples), prostate adenocarcinoma (PRAD; 20 samples), skin carcinoma (SKCM; 38 samples), thyroid carcinoma (THCA; 34 samples), and uterine corpus endometrial carcinoma (UCEC; 47 samples)

Summary

Introduction

Cancer arises from genetic and epigenetic alterations that interfere with essential mechanisms of the normal life cycle of cells such as DNA repair, replication control, and cell death [1]. The search for driver mutations, which confer a selective advantage to cancer cells, is generally performed in terms of the impact on protein sequences [2]. Systematic studies of cancer genomes have highlighted mutational processes outside of protein-coding regions [3,4,5] and tumorigenic mutations at noncoding regions have been described, like those in the TERT promoter [6, 7]. Current methods to detect driver mutations in noncoding regions are based on (i) the recurrence of mutations in predefined regions in combination with measure-. Note: Supplementary data for this article are available at Molecular Cancer Research Online (http://mcr.aacrjournals.org/).

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