Homologous recombination deficiency testing implementation in the routine clinical setting: real-life experience and...

Area of Science:

• Genomic medicine
• Oncology
• Biomarker discovery

Background:

• Homologous recombination deficiency (HRD) is a key biomarker for predicting poly (ADP-ribose) polymerase (PARP) inhibitor response in high-grade serous ovarian cancer (HGSOC).
• Genomic scar-based HRD testing requires standardization in clinical laboratories.
• This study compares available HRD testing applications in a real-world clinical cohort.

Purpose of the Study:

• To perform a head-to-head comparison of HRD testing applications.
• To guide the standardization of HRD testing in clinical practice.
• To validate HRD scar quantification for clinical implementation.

Main Methods:

• HRD scores were generated from HGSOC samples using VHIO-HRD (n=229) and shallow sequencing-based HRD score (LSTsh-HRD; n=123).
• VHIO-HRD utilized a custom hybrid-capture panel measuring large-scale transitions (LST), telomeric allelic imbalance (TAI), and genomic loss of heterozygosity (LOH).
• LSTsh-HRD used shallow whole-genome sequencing, and results were benchmarked against commercial platforms, with tumor fraction (TF) estimated computationally.

Main Results:

• Optimized HRD status cutoffs were established: ≥47 for VHIO-HRD and ≥20 for LSTsh-HRD.
• Strong concordance in HRD classification was observed across assays despite differences in chemistry and scoring.
• Tumor fraction significantly impacted HRD performance, with optimal sensitivity at TF ≥0.4 for VHIO-HRD and ≥0.2 for LSTsh-HRD.

Conclusions:

• HRD scar quantification is implementable across laboratories using diverse assays and algorithms.
• Tumor fraction is critical for accurate HRD scoring; LSTsh-HRD shows improved sensitivity in low-cellularity samples.
• VHIO-HRD allows status classification in samples with TF ≥0.4, supporting a multiplatform strategy for PARP inhibitor selection in HGSOC.