Researchers have identified a genomic scarring score (GSS) derived from next-generation sequencing (NGS) data as a predictive biomarker for response to PARP inhibitors in non-small cell lung cancer (NSCLC). The study, published in Nature, demonstrates that a high GSS (h-GSS) correlates with increased sensitivity to PARP inhibitors such as olaparib, offering a potential avenue for personalized treatment strategies in NSCLC.
The study enrolled 136 patients with stage IA-IIIA NSCLC amenable to surgical resection. Genomic DNA from patient samples was analyzed to assess homologous recombination deficiency (HRD) by calculating a GSS, utilizing the AmoyDx HRD Focus Panel and NGS data analysis. The GSS model, built on machine learning, measures genomic instability by weighing different types of chromosomal copy numbers. A GSS ranging from 0 to 49 was considered low (l-GSS), while a score from 50 to 100 was considered high (h-GSS).
Predictive Value of GSS
The research indicated that NSCLC cell lines with h-GSS were more sensitive to PARP inhibitors. Patient-derived xenografts (PDX) with h-GSS also exhibited a greater response to olaparib compared to those with l-GSS. This suggests that GSS could effectively identify patients who are more likely to benefit from PARP inhibitor therapy.
Methodology
The study involved comprehensive genomic analysis, including germline and somatic mutational status of HRR genes. DNA was extracted from patient PBMCs to assess germline HRR gene mutations using the AmoyDx HANDLE HRR NGS Panel. Somatic mutations were assessed using the AmoyDx HRD Complete Panel, which includes mutational analysis of 20 homologous recombination repair (HRR) related genes.
Patient-derived xenografts (PDX) were established by implanting primary tumor material into immunocompromised mice. Drug testing on PDX was initiated when tumors reached a specific size, with cisplatin and olaparib administered intraperitoneally. Tumor measurements were taken twice weekly, and mice were sacrificed when tumors reached the humane endpoint.
Clinical Implications
These findings have significant implications for the treatment of NSCLC. By using GSS as a predictive biomarker, clinicians may be able to better select patients for PARP inhibitor therapy, potentially improving treatment outcomes and reducing unnecessary exposure to ineffective treatments. Further studies are needed to validate these findings in larger, more diverse patient populations and to explore the potential of GSS in combination with other biomarkers.
