Evaluation of two algorithms measuring homologous recombination deficiency status in prognostic assessment for treatment-naïve non-small cell lung cancer

Yidan Ma; Jingyu Huang; Lei He; Jun Du; Longteng Liu; Xiaoguang Li; Peng Jiao; Xiaonan Wu; Wei Zhou; Xiaomao Xu; Li Yang; Jing Di; Changbin Zhu; Lin Li; Dongge Liu; Zheng Wang

doi:10.21147/j.issn.1000-9604.2025.03.05

Evaluation of two algorithms measuring homologous recombination deficiency status in prognostic assessment for treatment-naïve non-small cell lung cancer

Chin J Cancer Res. 2025 Jun 30;37(3):352-364. doi: 10.21147/j.issn.1000-9604.2025.03.05.

Authors

Yidan Ma¹, Jingyu Huang¹, Lei He¹, Jun Du¹, Longteng Liu¹, Xiaoguang Li², Peng Jiao³, Xiaonan Wu⁴, Wei Zhou⁵, Xiaomao Xu⁵, Li Yang¹, Jing Di¹, Changbin Zhu⁶, Lin Li⁴, Dongge Liu¹, Zheng Wang¹

Affiliations

¹ Department of Pathology.
² Department of Minimally Invasive Tumor Therapies Center.
³ Department of Thoracic Surgery.
⁴ Department of Oncology.
⁵ Department of Respiratory and Critical Care Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China.
⁶ Amoy Diagnostics Co., Ltd., Xiamen 361027, China.

PMID: 40642492
PMCID: PMC12240257
DOI: 10.21147/j.issn.1000-9604.2025.03.05

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, BRCA1/2 mutations, TP53 mutations, elevated tumor mutation burden (TMB), and increased copy number variations (CNVs). In contrast, CLR-GSS-defined HRD+ patients were only enriched for BRCA1/2 mutations, TP53 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 TP53 mutations as significant predictors and independent risk factors, respectively. No such associations were observed in the CLR-GSS-defined HRD+ cohort.

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.

Keywords: Non-small cell lung cancer; TP53; homologous recombination deficiency; methodology; prognosis.