Abstract
Driver mutations are the genetic variants responsible for oncogenesis, but how specific somatic mutational events arise in cells remains poorly understood. Mutational signatures derive from the frequency of mutated trinucleotides in a given cancer sample, and they provide an avenue for investigating the underlying mutational processes that operate in cancer. Here we analyse somatic mutations from 7,815 cancer exomes from The Cancer Genome Atlas (TCGA) across 26 cancer types. We curate a list of 50 known cancer driver mutations by analysing recurrence in our cohort and annotations of known cancer-associated genes from the Cancer Gene Census, IntOGen database and Cancer Genome Interpreter. We then use these datasets to perform binary univariate logistic regression and establish the statistical relationship between individual driver mutations and known mutational signatures across different cancer types. Our analysis led to the identification of 39 significant associations between driver mutations and mutational signatures (P < 0.004, with a false discovery rate of < 5%). We first validate our methodology by establishing statistical links for known and novel associations between driver mutations and the mutational signature arising from Polymerase Epsilon proofreading deficiency. We then examine associations between driver mutations and mutational signatures for AID/APOBEC enzyme activity and deficient mismatch repair. We also identify negative associations (odds ratio < 1) between mutational signatures and driver mutations, and here we examine the role of aging and cigarette smoke mutagenesis in the generation of driver mutations in IDH1 and KRAS in brain cancers and lung adenocarcinomas respectively. Our study provides statistical foundations for hypothesised links between otherwise independent biological processes and we uncover previously unexplored relationships between driver mutations and mutagenic processes during cancer development. These associations give insights into how cancers acquire advantageous mutations and can provide direction to guide further mechanistic studies into cancer pathogenesis.
Highlights
Cancer occurs following the accumulation of somatic mutations within cellular DNA [1]
We examine known and novel associations between driver mutations and mutational signatures arising from processes such as defective proofreading during DNA replication, activation-induced cytosine deaminase (AID)/apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC) enzyme-associated mutagenesis and deficient mismatch repair
Signature 1 was the most common mutational signature that we identified, contributing an average of 6.21% toward all mutational signatures measured across our cohort
Summary
Cancer occurs following the accumulation of somatic mutations within cellular DNA [1]. Cells undergo malignant transformation following the acquisition of a subset of somatic mutations, termed driver mutations [3]. Driver mutations confer a growth advantage to cells, and subsequently undergo positive selection in a population. Driver mutations typically affect certain cancer-associated genes by, for example, activating an oncogene or inactivating a tumour suppressor gene. Research in recent years has led to the identification of hundreds of driver mutations in cancer-associated genes, but only a handful of driver mutations are sufficient for oncogenesis in a single cancer sample [4]. Driver mutations form in the cancer genome alongside potentially hundreds of thousands of passenger mutations [3]. Passenger mutations are not directly involved in cancer progression and do not confer a selective advantage
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