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Pan-cancer detection of driver genes at the single-patient resolution

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article number12
Pages (from-to)12
JournalGenome medicine
Volume13
Issue number1
DOIs
Accepted/In press8 Jan 2021
Published1 Feb 2021

Bibliographical note

Funding Information: The results published here are in whole or part based upon data generated by The Cancer Genome Atlas managed by the NCI and NHGRI. We thank Damjan Temelkovski for testing sysSVM2. Publisher Copyright: © 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

Documents

  • Nulsen_MainText_rev3

    Nulsen_MainText_rev3.pdf, 1.34 MB, application/pdf

    Uploaded date:19 Jan 2021

    Version:Accepted author manuscript

    Licence:CC BY

King's Authors

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 ).

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