Finding cancer driver mutations in the era of big data research

Abstract

In the last decade, the costs of genome sequencing have decreased considerably. The commencement of large-scale cancer sequencing projects has enabled cancer genomics to join the big data revolution. One of the challenges still facing cancer genomics research is determining which are the driver mutations in an individual cancer, as these contribute only a small subset of the overall mutation profile of a tumour. Focusing primarily on somatic single nucleotide mutations in this review, we consider both coding and non-coding driver mutations, and discuss how such mutations might be identified from cancer sequencing datasets. We describe some of the tools and database that are available for the annotation of somatic variants and the identification of cancer driver genes. We also address the use of genome-wide variation in mutation load to establish background mutation rates from which to identify driver mutations under positive selection. Finally, we describe the ways in which mutational signatures can act as clues for the identification of cancer drivers, as these mutations may cause, or arise from, certain mutational processes. By defining the molecular changes responsible for driving cancer development, new cancer treatment strategies may be developed or novel preventative measures proposed.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

“…The excellent literature reviews and research papers in this area cover clinical and social benefits, as well as genomic sequence analysis, pattern identification, and approaches and/ or corroboration of target discovery. This latter category produces vital software tools [83–96]. Recently, functional advances were reviewed as well [97].…”

Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations

“…The cBio Cancer Genomics Portal (https://www.cbioportal.org/) is an open-access resource for exploration of large cancer genomics data sets (Cerami et al, 2012; Gao et al, 2013). These datasets have allowed for comprehensive genome-wide analyses of genetic alterations in multiple tumor types (Poulos and Wong, 2018). A relatively small fraction of somatic variants known as driver mutations have considerable functional effects and can be acquired over time as a result of a range of mutational processes, rather than inherited (Haber and Settleman, 2007; Lawrence et al, 2013; Vogelstein et al, 2013).…”

Biomarkers for Early Cancer Diagnosis: Prospects for Success through the Lens of Tumor Genetics

“…[ 40 ] Deep sequencing of different types of cancer has shown that each tumor has its own set of somatic mutations in several genes, with tumors of the same histotype and tissue origin showing great heterogeneity in their somatic mutation profiles. [ 41–43 ] Oncogenomics research is also evidencing enormous intratumoral heterogeneity, which manifests as distinct cell populations within a patient's tumor (and between a primary lesion and its metastases); intratumoral heterogeneity has been observed in various solid and hematological cancers. [ 44–46 ] These forms of tumor heterogeneity pose challenges for both diagnosis and treatment.…”