Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours

Madeleine Darbyshire, Zachary du Toit, Mark F. Rogers, Tom R. Gaunt, Colin Campbell

Research output: Contribution to journalArticle (Academic Journal)peer-review

2 Citations (Scopus)
177 Downloads (Pure)

Abstract

For cancers, such as common solid tumours, variants in the genome give a selective growth advantage to certain cells. It has recently been argued that the mean count of coding single nucleotide variants acting as disease-drivers in common solid tumours is frequently small in size, but significantly variable by cancer type (hypermutation is excluded from this study). In this paper we investigate this proposal through the use of integrative machine-learning-based classifiers we have proposed recently for predicting the disease-driver status of single nucleotide variants (SNVs) in the human cancer genome. We find that predicted driver counts are compatible with this proposal, have similar variabilities by cancer type and, to a certain extent, the drivers are identifiable by these machine learning methods. We further discuss predicted driver counts stratified by stage of disease and driver counts in non-coding regions of the cancer genome, in addition to driver-genes.
Original languageEnglish
Article number13452 (2019)
Number of pages11
JournalScientific Reports
Volume9
DOIs
Publication statusPublished - 17 Sep 2019

Structured keywords

  • Bristol Population Health Science Institute
  • ICEP

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