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Lookup NU author(s): Dr Ed Schwalbe, - Lalchungnunga, Dr Timothy Barrow, Dr Gordon Strathdee
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2021, The Author(s). The identification of cancer-specific vulnerability genes is one of the most promising approaches for developing more effective and less toxic cancer treatments. Cancer genomes exhibit thousands of changes in DNA methylation and gene expression, with the vast majority likely to be passenger changes. We hypothesised that, through integration of genome-wide DNA methylation/expression data, we could exploit this inherent variability to identify cancer subtype-specific vulnerability genes that would represent novel therapeutic targets that could allow cancer-specific cell killing. We developed a bioinformatics pipeline integrating genome-wide DNA methylation/gene expression data to identify candidate subtype-specific vulnerability partner genes for the genetic drivers of individual genetic/molecular subtypes. Using acute lymphoblastic leukaemia as an initial model, 21 candidate subtype-specific vulnerability genes were identified across the five common genetic subtypes, with at least one per subtype. To confirm the approach was applicable across cancer types, we also assessed medulloblastoma, identifying 15 candidate subtype-specific vulnerability genes across three of four established subtypes. Almost all identified genes had not previously been implicated in these diseases. Functional analysis of seven candidate subtype-specific vulnerability genes across the two tumour types confirmed that siRNA-mediated knockdown induced significant inhibition of proliferation/induction of apoptosis, which was specific to the cancer subtype in which the gene was predicted to be specifically lethal. Thus, we present a novel approach that integrates genome-wide DNA methylation/expression data to identify cancer subtype-specific vulnerability genes as novel therapeutic targets. We demonstrate this approach is applicable to multiple cancer types and identifies true functional subtype-specific vulnerability genes with high efficiency.
Author(s): Schwalbe EC, Lalchungnunga H, Lafta F, Barrow TM, Strathdee G
Publication type: Article
Publication status: Published
Journal: Oncogene
Year: 2021
Volume: 40
Issue: 33
Pages: 5213-5223
Print publication date: 19/08/2021
Online publication date: 06/07/2021
Acceptance date: 22/06/2021
Date deposited: 23/06/2022
ISSN (print): 0950-9232
ISSN (electronic): 1476-5594
Publisher: Springer Nature
URL: https://doi.org/10.1038/s41388-021-01923-1
DOI: 10.1038/s41388-021-01923-1
PubMed id: 34230614
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