Genomic Scar Score: A robust model predicting homologous recombination deficiency based on genomic instability. (9th December 2022) - Yuan, Wuzhou; Ni, Jing; Wen, Hao; Shi, Weijie; Chen, Xuejun; Huang, Hongwei; Zhang, Xiaotian; Lu, Xuan; Zhu, Changbin; Dong, Hua; Yang, Shuang; Wu, Xiaohua; et al.
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.