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 language | English |
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Article number | 13452 (2019) |
Number of pages | 11 |
Journal | Scientific Reports |
Volume | 9 |
DOIs | |
Publication status | Published - 17 Sept 2019 |
Research Groups and Themes
- Bristol Population Health Science Institute
- ICEP
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Dive into the research topics of 'Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours'. Together they form a unique fingerprint.Profiles
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Dr I C G Campbell
- School of Engineering Mathematics and Technology - Associate Professor in Mathematics for Information Technology
- Cancer
- Intelligent Systems Laboratory
Person: Academic , Member