Genomic Scar Score: A robust model predicting homologous recombination deficiency based on genomic instability

基因组不稳定性 一致性 同源重组 内科学 遗传学 危险系数 医学 肿瘤科 卵巢癌 队列 比例危险模型 乳腺癌 生物 癌症 基因 DNA 置信区间 DNA损伤
作者
Wuzhou Yuan,Jing Ni,Hao Wen,Weijie Shi,Xuejun Chen,Hongwei Huang,Shouxin Zhang,Xuan Lu,Changbin Zhu,Hua Dong,Shuang Yang,Xiaohua Wu,Xiaoxiang Chen
出处
期刊:Bjog: An International Journal Of Obstetrics And Gynaecology [Wiley]
卷期号:129 (S2): 14-22 被引量:6
标识
DOI:10.1111/1471-0528.17324
摘要

To develop a novel machine learning-based algorithm called the Genomic Scar Score (GSS) for predicting homologous recombination deficiency (HRD) events.Method development study.AmoyDx Medical Laboratory and Jiangsu Cancer Hospital.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.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.The performance of the GS model and the result of clinical validation.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.The GS was a robust model to predict HRD and had broad clinical applications in predicting which patients will respond favourably to PARPi treatment.
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