Genomic Scar Score: A robust model predicting homologous recombination deficiency based on genomic instability
Abstract
Objective
To develop a novel machine learning‐based algorithm called the Genomic Scar Score (GSS) for predicting homologous recombination deficiency (HRD) events.
Design
Method development study.
Setting
AmoyDx Medical Laboratory and Jiangsu Cancer Hospital.
Population or sample
A cohort of individuals with ovarian or breast cancer (n = 377) were collected from the AmoyDx Medical Laboratory. Another cohort of patients with ovarian cancer treated with PARP inhibitors (n = 58) was enrolled in the Jiangsu Cancer Hospital.
Methods
We used linear support vector machines to build a Genomic Scar (GS) model to predict HRD events, and Kaplan–Meier analyses were performed by comparing the progression‐free survival (PFS) of patients in different groups using a two‐sided log‐rank test.
Main outcome measures
The performance of the GS model and the result of clinical validation.
Results
The GS model displayed more than 97.0% sensitivity to detect BRCA‐deficient events, and the GS model identified patients that could benefit from poly(ADP‐ribose) polymerase inhibitors (PARPi), as the GS score (GSS)‐positive group had a longer progression‐free survival (PFS) (9.4 versus 4.4 months; hazard ratio [HR] = 0.54, P < 0.001) than the GSS‐negative group after PARPi treatment. Meanwhile, the GSS showed high concordance among different NGS panels, which implied the robustness of the GS model.
Conclusions
The GS was a robust model to predict HRD and had broad clinical applications in predicting which patients will respond favourably to PARPi treatment.