Background Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development. Results Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation. Conclusions Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/.

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).

Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Established methods for driver detection focus mostly on genes that are recurrently altered across cohorts of cancer patients.However, mapping these genes back to patients leaves a sizeable fraction with few or no driver events, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. Here we present sysSVM2, a tool based on machine learning that integrates somatic alteration data with systems-level gene properties to predict drivers in individual patients. We develop sysSVM2 for pancancer applicability, demonstrating robust performance on real and simulated cancer data. We benchmark its performance against other driver detection methods and show that sysSVM2 has a lower false positive rate and superior patient driver coverage. Applying sysSVM2 to 7,646 samples from 34 cancer types, we find that predicted drivers are often rare or patient-specific. However, they converge to disrupt well-known cancer-related processes including DNA repair, chromatin organisation and the cell cycle. sysSVM2 is a resource to enhance personalised predictions of cancer driver events with possible use in research and clinical settings. Code to implement sysSVM2 and the trained models in simulated cancer-agnostic data as well as in 34 cancer types are available at https://github.com/ciccalab/sysSVM2.