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

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 ra

SUBMITTER: Nulsen J 

PROVIDER: S-EPMC7849133 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature