In BJOG : an international journal of obstetrics and gynaecology
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.
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
2022-Nov
Genomic Scar Score, genomic instability, homologous recombination deficiency
