Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine

Abstract

High-throughput DNA sequencing is revolutionizing the study of cancer and enabling the measurement of the somatic mutations that drive cancer development. However, the resulting sequencing datasets are large and complex, obscuring the clinically important mutations in a background of errors, noise, and random mutations. Here, we review computational approaches to identify somatic mutations in cancer genome sequences and to distinguish the driver mutations that are responsible for cancer from random, passenger mutations. First, we describe approaches to detect somatic mutations from high-throughput DNA sequencing data, particularly for tumor samples that comprise heterogeneous populations of cells. Next, we review computational approaches that aim to predict driver mutations according to their frequency of occurrence in a cohort of samples, or according to their predicted functional impact on protein sequence or structure. Finally, we review techniques to identify recurrent combinations of somatic mutations, including approaches that examine mutations in known pathways or protein-interaction networks, as well as de novo approaches that identify combinations of mutations according to statistical patterns of mutual exclusivity. These techniques, coupled with advances in high-throughput DNA sequencing, are enabling precision medicine approaches to the diagnosis and treatment of cancer.

Cancer progression modeling using static sample data

“…One of them is to identify genetic alterations that drive tumor progression, which is one of the central goals of some recent large-scale cancer studies [7,8,[73][74][75][76]. The primary analytical strategy used today is prevalence-based approaches that search through a large number of tumor samples for genetic changes that occur with a higher frequency than would be expected by chance alone [73,74].…”

MSEA: detection and quantification of mutation hotspots through mutation set enrichment analysis

“…However, this approach suffers from several known limitations, such as a high false positive rate, and it often misses low-frequency yet genuine cancer genes [3]. The recent explosion of NGS data has placed a strong demand on bioinformatics approaches for cancer gene prediction [4][5][6][7][8]. In general, current methods can be categorized into three groups.…”

DGPathinter: a novel model for identifying driver genes via knowledge-driven matrix factorization with prior knowledge from interactome and pathways

“…In the last decade, studies based on advanced DNA sequencing technologies have highlighted the fact that the development and progression of cancer hinges on somatic abnormalities of DNA (Hudson et al, 2010;Vogelstein et al, 2013;Raphael et al, 2014). Despite a small number of driver genes conferring a selective growth advantage for tumor cells, a considerable number of somatic mutations are sporadic passenger mutations that have no impact on cancer process (Sjöblom et al, 2006;Youn & Simon, 2011;Dees et al, 2012;Lawrence et al, 2013;Hua et al, 2013;Cho et al, 2016).…”

“…Nevertheless, although some driver genes are mutated at high frequencies among tumor samples, previous studies have reported that some driver genes are mutated at low frequencies, and the mutation frequencies of these genes are too low to be tested as statistically significant (Vandin, Upfal & Raphael, 2011;Leiserson et al, 2014;Raphael et al, 2014). A prevalent assumption to explain the long tail phenomenon is that genes usually interact with other genes, and some genes with no mutation can be perturbed by their interacting neighbors (Vandin, Upfal & Raphael, 2011;Leiserson et al, 2014;Raphael et al, 2014;Cho et al, 2016).…”

“…A prevalent assumption to explain the long tail phenomenon is that genes usually interact with other genes, and some genes with no mutation can be perturbed by their interacting neighbors (Vandin, Upfal & Raphael, 2011;Leiserson et al, 2014;Raphael et al, 2014;Cho et al, 2016). Based on this assumption, many studies for driver gene identification have been proposed by incorporating interactome information as prior knowledge (Vandin, Upfal & Raphael, 2011;Leiserson et al, 2014;Raphael et al, 2014;Hofree et al, 2013;Bashashati et al, 2012;Cho et al, 2016). The interactome information is employed as gene interaction network obtained from databases including iRefIndex (Razick, Magklaras & Donaldson, 2008), STRING (Szklarczyk et al, 2011) and others (Prasad et al, 2009;Lee et al, 2011;Das & Yu, 2012;Khurana et al, 2013).…”