ArticleChinese journal of cancer research = Chung-kuo yen cheng yen chiu2025
Evaluation of two algorithms measuring homologous recombination deficiency status in prognostic assessment for treatment-naïve non-small cell lung cancer.
Article in Chinese journal of cancer research = Chung-kuo yen cheng yen chiu, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Revisiting nature of stage III non-small cell lung cancer: Anatomically defined, biologically systemic.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
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16 authors.
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Abstract
Objective: 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. Methods: 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. Results: 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, Conclusions: 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.
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