Joel Nulsen1,2, Hrvoje Misetic1,2, Christopher Yau3,4, Francesca D Ciccarelli5,6. 1. Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK. 2. School of Cancer and Pharmaceutical Sciences, King's College London, London, SE1 1UL, UK. 3. School of Health Sciences, University of Manchester, Manchester, M13 9PL, UK. 4. The Alan Turing Institute, London, NW1 2DB, UK. 5. Cancer Systems Biology Laboratory, The Francis Crick Institute, London, NW1 1AT, UK. francesca.ciccarelli@crick.ac.uk. 6. School of Cancer and Pharmaceutical Sciences, King's College London, London, SE1 1UL, UK. francesca.ciccarelli@crick.ac.uk.
Abstract
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 ).
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 ).
Entities:
Keywords:
Cancer driver genes; Cancer genomics; Patient-level driver detection; Systems-level properties
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