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Related Experiment Video

Updated: Jan 19, 2026

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

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

Madeleine Darbyshire¹, Zachary du Toit², Mark F Rogers¹

¹Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, United Kingdom.

Scientific Reports

|September 19, 2019

Summary

Cancer genomes have few disease-driving single nucleotide variants (SNVs), but this count varies significantly by cancer type. Machine learning methods can identify these crucial SNVs.

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Last Updated: Jan 19, 2026

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Published on: September 20, 2016

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Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

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Published on: June 16, 2011

Area of Science:

Genomics
Computational Biology
Cancer Research

Background:

Cancer development is driven by genomic variants conferring a growth advantage.
The number of disease-driving single nucleotide variants (SNVs) in solid tumors is proposed to be small but cancer-type variable.

Purpose of the Study:

To investigate the proposal that common solid tumors have a small, cancer-type-variable count of disease-driving SNVs.
To assess the utility of integrative machine learning classifiers for identifying cancer driver SNVs.

Main Methods:

Utilized recently developed integrative machine learning-based classifiers.
Predicted the disease-driver status of SNVs in the human cancer genome.
Analyzed predicted driver counts stratified by cancer type and disease stage.

Main Results:

Predicted driver counts align with the proposal of a small, variable number of driver SNVs per cancer type.
Machine learning methods demonstrated the ability to identify a subset of these drivers.
Variability in driver counts across different cancer types was consistent with predictions.

Conclusions:

The study supports the hypothesis of a limited and cancer-specific set of driver SNVs in solid tumors.
Machine learning approaches are effective in predicting and identifying cancer-driving SNVs.
Further analysis explored driver counts in non-coding regions and their relation to driver genes.