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PubMed2025

Comprehensive assessment of homologous recombination deficiency via simultaneous methylation and mutation analysis in epithelial ovarian cancer: implications for PARP inhibitors efficacy.

BACKGROUND: The advent of poly (ADP-ribose) polymerase inhibitors (PARPi) over the past decade has significantly altered the management of epithelial ovarian cancer (EOC). We proposed that the etiology of homologous recombination deficiency (HRD) might underlie the variable responses to PARPi observed across patient populations. METHODS: As part of the phase 2 study of the Chinese HRD Harmonization Project, we developed a genomic methylation sequencing (GM-seq) pipeline facilitated by the TET enzyme for the simultaneous identification of methylated modifications and genetic variations in EOC tumor samples, and compared with established DNA sequencing-based HRD assays. RESULTS: Somatic mutation and HRD scores were confounded by low tumor purity in our cohort of 98 locally advanced/advanced EOC patients. In samples with tumor purity ≥ 30% (n = 45), the GM-seq pipeline showed high consistency with DNA sequencing-based HRD assay, identifying genetic variations in homologous recombination repair (HRR) genes and HRD score with 92.6% (25/27) and 97.1% (33/34) consistency respectively, in addition to conducting methylation profiling. Moreover, different underlying mechanisms of HRD were associated with varying degrees of PARPi efficacy, with BRCA1/2 LOH group having the best efficacy (median PFS, undefined), followed by BRCA1 methylation group (median PFS, 23.4 months), and those with unknown etiology of HRD having the worst efficacy (median PFS, 8.8 months, p < 0.001). CONCLUSION: Our findings underscore the importance of considering HRD etiology when evaluating PARPi efficacy in EOC patients. The GM-seq pipeline, represents a significant advancement in HRD detection, enabling more accurate predictions of PARPi response.

Epithelial ovarian cancer (EOC)↗

PubMed2025

MRDtarget: A heuristic Gaussian approach for optimizing targeted capture regions to enhance Minimal Residual Disease detection.

Molecular residual disease (MRD) detection, initially developed for hematologic malignancies, has become a critical biomarker for monitoring solid tumors. MRD detection primarily relies on circulating tumor DNA (ctDNA) analysis using next-generation sequencing, offering high sensitivity and broad genomic coverage. However, challenges remain in designing cost-effective panels that maximize mutation detection while maintaining biological relevance. Fixed panels often lack sufficient patient-specific mutation coverage, while WES-based personalized MRD assays, despite their high sensitivity, are costly and less accessible. We developed a tumor comprehensive genomic profiling (CGP)-informed personalized MRD assay to detect tumor-derived mutations, which allowed us to design patient-specific personalized panels and meanwhile, provide a cost-effective alternative to whole exome sequencing (WES). To address these limitations, we developed MRDtarget, a heuristic multivariate Gaussian model-based targeted capture region selection method. By expanding beyond traditional hotspot regions, MRDtarget optimizes variant tracking for MRD detection, significantly improving sensitivity. Using a Bayesian inference-based heuristic approach, MRDtarget integrates multi-feature informativeness rates to identify optimal genomic regions for capture. Experimental results demonstrate that MRDtarget enables the detection of more variants per patient. This study underscores the importance of rational panel design to improve MRD sensitivity and provides a novel approach to enhance precision diagnostics and treatment for solid tumor patients.

Humans↗