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.