Ma Yidan, Huang Jingyu, He Lei, Du Jun, Liu Longteng, Li Xiaoguang, Jiao Peng, Wu Xiaonan, Zhou Wei, Xu Xiaomao, Yang Li, Di Jing, Zhu Changbin, Li Lin, Liu Dongge, Wang Zheng
Department of Pathology.
Department of Minimally Invasive Tumor Therapies Center.
Chin J Cancer Res. 2025 Jun 30;37(3):352-364. doi: 10.21147/j.issn.1000-9604.2025.03.05.
Patients with homologous recombination deficiency (HRD) demonstrate distinct clinicopathological and prognostic features. However, standardised and clinically validated HRD detection methodologies specifically tailored for non-small cell lung cancer (NSCLC) have yet to be established. Further research is needed to clarify the precise role and clinical implications of HRD in NSCLC.
A cohort of 580 treatment-naïve NSCLC patients was retrospectively enrolled. Comprehensive genomic profiling (CGP) was performed for all patients, and HRD status was evaluated using two genomic scar score (GSS)-based algorithms: a machine learning-based GSS (ML-GSS) and a continuous linear regression-based GSS (CLR-GSS). To assess the diagnostic performance (sensitivity and specificity) of the ML-GSS and CLR-GSS algorithms for HRD detection, immunohistochemical (IHC) staining was conducted for two HRD-related biomarkers: Schlafen 11 (SLFN11) and RAD51. Survival analysis, including progression-free survival (PFS), along with multivariable Cox proportional hazards models, was performed to compare the prognostic value of the two HRD algorithms.
Among all patients, 146 (25.2%) and 46 (7.9%) were classified as HRD-positive (HRD+) by ML-GSS and CLR-GSS, respectively. Using SLFN11 IHC expression as the reference standard, comparative analysis demonstrated that ML-GSS exhibited significantly higher sensitivity but lower specificity than CLR-GSS. This trend was consistently observed in RAD51 staining analysis. Compared to HRD-negative (HRD-) patients, ML-GSS-defined HRD+ cases displayed distinct clinicopathological and genomic features, including a higher prevalence of homologous recombination (HR)-related genes mutations, mutations, mutations, elevated tumor mutation burden (TMB), and increased copy number variations (CNVs). In contrast, CLR-GSS-defined HRD+ patients were only enriched for mutations, mutations, and elevated TMB. Furthermore, ML-GSS-defined HRD+ status was associated with significantly worse prognosis following first-line therapy compared to HRD- patients. Univariate and multivariable Cox analyses identified ML-GSS-defined HRD+ and mutations as significant predictors and independent risk factors, respectively. No such associations were observed in the CLR-GSS-defined HRD+ cohort.
ML-GSS demonstrated superior performance to CLR-GSS in assessing chromosomal instability (CIN) and showed greater clinical utility. We recommend the ML-GSS algorithm as a robust and clinically validated tool for HRD/CIN evaluation in NSCLC. Furthermore, ML-GSS-defined HRD+ status was identified as both a significant predictor and an independent risk factor.
同源重组缺陷（HRD）患者表现出独特的临床病理和预后特征。然而，专门针对非小细胞肺癌（NSCLC）的标准化且经过临床验证的HRD检测方法尚未建立。需要进一步研究以阐明HRD在NSCLC中的精确作用和临床意义。
回顾性纳入580例未经治疗的NSCLC患者队列。对所有患者进行综合基因组分析（CGP），并使用两种基于基因组疤痕评分（GSS）的算法评估HRD状态：基于机器学习的GSS（ML-GSS）和基于连续线性回归的GSS（CLR-GSS）。为评估ML-GSS和CLR-GSS算法检测HRD的诊断性能（敏感性和特异性），对两种HRD相关生物标志物进行免疫组化（IHC）染色：Schlafen 11（SLFN11）和RAD51。进行生存分析，包括无进展生存期（PFS），并使用多变量Cox比例风险模型比较两种HRD算法的预后价值。
在所有患者中，分别有146例（25.2%）和46例（7.9%）被ML-GSS和CLR-GSS分类为HRD阳性（HRD+）。以SLFN11 IHC表达作为参考标准，比较分析表明ML-GSS的敏感性显著高于CLR-GSS，但特异性较低。在RAD51染色分析中也一致观察到这种趋势。与HRD阴性（HRD-）患者相比，ML-GSS定义的HRD+病例表现出独特的临床病理和基因组特征，包括同源重组（HR）相关基因突变、突变、突变的发生率更高，肿瘤突变负荷（TMB）升高以及拷贝数变异（CNV）增加。相比之下，CLR-GSS定义的HRD+患者仅富集了突变、突变和TMB升高。此外，与HRD-患者相比，ML-GSS定义的HRD+状态与一线治疗后的预后明显更差相关。单变量和多变量Cox分析分别确定ML-GSS定义的HRD+和突变是显著预测因素和独立危险因素。在CLR-GSS定义的HRD+队列中未观察到此类关联。
ML-GSS在评估染色体不稳定性（CIN）方面表现优于CLR-GSS，并显示出更大的临床实用性。我们推荐ML-GSS算法作为一种用于NSCLC中HRD/CIN评估的强大且经过临床验证的工具。此外，ML-GSS定义的HRD+状态被确定为显著预测因素和独立危险因素。
