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Updated: Nov 19, 2025
Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
Published on: July 22, 2020
Pan-cancer detection of driver genes at the single-patient resolution
Joel Nulsen1,2, Hrvoje Misetic1,2, Christopher Yau3,4
1Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK.
Background:
Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions.
Results:
We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways.
Conclusions:
sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types ( https://github.com/ciccalab/sysSVM2 ).
Insights
New machine learning software, sysSVM2, identifies cancer driver genes in individual patients by integrating genetic alterations with gene system properties. This advances precision oncology for rare cancers and patients with few canonical drivers.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Precision oncology requires identifying individual cancer driver genes.
- Current methods focus on recurrent alterations in patient cohorts, leaving many patients with few identified drivers.
- This limits understanding of cancer mechanisms and therapeutic options.
Purpose of the Study:
- To develop and validate sysSVM2, a machine learning tool for predicting individual cancer driver genes.
- To integrate genetic alterations with gene systems-level properties for enhanced driver prediction.
- To apply sysSVM2 to diverse cancer types, including rare cancers.
Main Methods:
- sysSVM2 integrates cancer genetic alterations with gene systems-level properties.
- The software was optimized using simulated pan-cancer data.
- Performance was benchmarked on real cancer data and validated on a rare cancer type.
Main Results:
- sysSVM2 accurately predicts individual cancer drivers with a low false-positive rate.
- Predicted drivers are stable and disrupt known cancer-related pathways.
- The tool is effective even for rare cancer types with limited cohort data.
Conclusions:
- sysSVM2 enables driver identification in patients with insufficient canonical drivers or rare cancers.
- This supports the advancement of precision oncology goals.
- The sysSVM2 code and pre-trained models are publicly available for the research community.
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