Identifying cancer driver genes in individual tumours

Gillman, Rhys, Field, Matt A., Schmitz, Ulf, Karamatic, Rozemary, and Hebbard, Lionel (2023) Identifying cancer driver genes in individual tumours. Computational and Structural Biotechnology Journal, 21. pp. 5028-5038.

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Cancer is a heterogeneous disease with a strong genetic component making it suitable for precision medicine approaches aimed at identifying the underlying molecular drivers within a tumour. Large scale population-level cancer sequencing consortia have identified many actionable mutations common across both cancer types and sub-types, resulting in an increasing number of successful precision medicine programs. Nonetheless, such approaches fail to consider the effects of mutations unique to an individual patient and may miss rare driver mutations, necessitating personalised approaches to driver-gene prioritisation. One approach is to quantify the functional importance of individual mutations in a single tumour based on how they affect the expression of genes in a gene interaction network (GIN). These GIN-based approaches can be broadly divided into those that utilise an existing reference GIN and those that construct de novo patient-specific GINs. These single-tumour approaches have several limitations that likely influence their results, such as use of reference cohort data, network choice, and approaches to mathematical approximation, and more research is required to evaluate the in vitro and in vivo applicability of their predictions. This review examines the current state of the art methods that identify driver genes in single tumours with a focus on GIN-based driver prioritisation.

Item ID: 80754
Item Type: Article (Research - C1)
ISSN: 2001-0370
Copyright Information: © 2023 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (
Funders: James Cook University (JCU), National Health and Medical Research Council of Australia (NHMRC)
Projects and Grants: JCU RSP-379-FY2023, NHMRC #1196405
Date Deposited: 23 Oct 2023 22:51
FoR Codes: 31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310202 Biological network analysis @ 30%
31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310204 Genomics and transcriptomics @ 30%
32 BIOMEDICAL AND CLINICAL SCIENCES > 3211 Oncology and carcinogenesis > 321103 Cancer genetics @ 40%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280103 Expanding knowledge in the biomedical and clinical sciences @ 100%
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