Genomic Scar Score: A robust model predicting homologous recombination deficiency based on genomic instability. (9th December 2022)

• Record Type: Journal Article
• Authors: Yuan, Wuzhou; Ni, Jing; Wen, Hao; Shi, Weijie; Chen, Xuejun; Huang, Hongwei; Zhang, Xiaotian; Lu, Xuan; Zhu, Changbin; Dong, Hua; Yang, Shuang; Wu, Xiaohua; Chen, Xiaoxiang
• Other Names: Ma Ding guestEditor.; Gao Qinglei guestEditor.
• Is Part Of: BJOG. Volume 129(2022)Supplement 2
• Page Start: 14
• Page End: 22
• Publication Date: 2022-12-09
• Subjects: genomic instability -- Genomic Scar Score -- homologous recombination deficiency; Obstetrics -- Periodicals; Gynecology -- Periodicals; 618
• DOI: 10.1111/1471-0528.17324
• Languages: English
• ISSNs: 1470-0328

Abstract

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.