Genomic signatures as predictive biomarkers of homologous recombination deficiency in ovarian cancer
- PMID: 28950147
- DOI: 10.1016/j.ejca.2017.08.029
Genomic signatures as predictive biomarkers of homologous recombination deficiency in ovarian cancer
Abstract
DNA repair deficiency is a common hallmark of many cancers and is increasingly recognised as a target for cancer therapeutics. Selecting patients for these treatments requires a functional assessment of multiple redundant DNA repair pathways. With the advent of whole-genome sequencing of cancer genomes, it is increasingly recognised that multiple signatures of mutational and chromosomal alterations can be correlated with specific DNA repair defects. The clinical relevance of this approach is underlined by the use of poly-(ADP-ribose) polymerase inhibitors (PARPi) in homologous recombination (HR) deficient high-grade serous ovarian cancers. Beyond deleterious mutations in HR-related genes such as BRCA1/2, it is recognised that HR deficiency endows ovarian cancers with specific signatures of base substitutions and structural chromosomal variation. Multiple metrics quantifying loss-of-heterozygosity (LOH) events were proposed and implemented in trials with PARPi. However, it was shown that some of the HR-deficient cases, i.e. CDK12-mutated tumours, were not associated with high LOH-based scores, but with distinct patterns of genomic alterations such as tandem duplication. Therefore, more complex signatures of structural genomic variation were identified and quantified. Ultimately, optimal prediction models for treatments targeting DNA repair will need to integrate multiples of these genomic signatures and will also need to assess multiple resistance mechanisms such as genomic reversion events that partially or fully re-activate DNA repair.
Keywords: Alternative end joining; DNA repair; Double-strand breaks; Homologous recombination; Mutational signatures; Non-homologous end joining; Ovarian cancer; PARP inhibitor; Rearrangement signatures; Whole-genome sequencing.
Copyright © 2017 Elsevier Ltd. All rights reserved.
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