GIInger predicts homologous recombination deficiency and patient response to PARPi treatment from shallow genomic profiles.
retrospective_cohort · Level III
Where this comes from
- Record sourced from PubMed, PMID 38118421.
- Also identified by DOI 10.1016/j.xcrm.2023.101344 and PMC identifier 10772634.
- Licence recorded as CC BY-NC-ND.
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Abstract
Homologous recombination deficiency (HRD) is a predictive biomarker for poly(ADP-ribose) polymerase 1 inhibitor (PARPi) sensitivity. Routine HRD testing relies on identifying BRCA mutations, but additional HRD-positive patients can be identified by measuring genomic instability (GI), a consequence of HRD. However, the cost and complexity of available solutions hamper GI testing. We introduce a deep learning framework, GIInger, that identifies GI from HRD-induced scarring observed in low-pass whole-genome sequencing data. GIInger seamlessly integrates into standard BRCA testing workflows and yields reproducible results concordant with a reference method in a multisite study of 327 ovarian cancer samples. Applied to a BRCA wild-type enriched subgroup of 195 PAOLA-1 clinical trial patients, GIInger identified HRD-positive patients who experienced significantly extended progression-free survival when treated with PARPi. GIInger is, therefore, a cost-effective and easy-to-implement method for accurately stratifying patients with ovarian cancer for first-line PARPi treatment.
Medical subject headings
- Ovarian Neoplasms