Copy number features as a novel biomarker of homologous recombination (HR) status in high-grade serous ovarian cancer (HGSOC)

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Background: Copy number alterations (CNA) arise as a result of somatic changes to chromosome structure, resulting in gain or loss of genomic regions. A likely source of copy number variation is an incorrect repair of DNA damage that causes chromosome instability (CI). Hence, many tumors with a high degree of CNAs suffer CI. This is the case of high grade serous ovarian cancer (HGSOC), where deficiency in homologous recombination (HRD) is highly prevalent, and so are CI and CNAs. Clinically, HR status in HGSOC is a biomarker of iPARP response and required for proper patient management. Available HRD tests measure genomic loss of heterozygosity (LOH) features, closely related to CI. Such features are complex to acquire in samples with low tumor content; overall, 15% of HGSOC specimens fail HR testing. CNA profiling is technically more amenable to lower tumor cellularity and may be captured by a wider range of techniques and applications (gene panels, low pass WGS...). We present here the analysis and identification of CNA features in HGSOC as a novel biomarker of HR status. Methods: A cohort of 123 primary HGSOC tumors were analysed with a custom hybrid capture-based NGS panel (VHIO-300) that provides, along mutations in 450 genes, genome-wide CNA profiles. B-allele fractions, obtained from single nucleotide polymorphisms also in the panel, allowed HRD-LOH score calculation (Marquard et al., 2015). 41 CNA segments correlated to HRD-LOH scores (proportions test, FDR < 0.001) and selected as features to calculate a CNA-HGSOC score (range 0-100). Among them, 10 segments appeared altered almost exclusively in BRCA1/2 pathogenic/likely pathogenic mutant tumors. Results: The density plot of the CNA-HGSOC score showed a bimodal distribution with modes at 7 and 65. A cut-off value of 34 was selected based on the lowest density CNA-score value between the modes. Hence, high CNA-HGSOC was defined as tumors with scores ≥ 34. Our CNA-HGSOC score was strongly correlated as a continuous variable to HRD-LOH (Pearson correlation = 0.72; p < 0.0001) and ROC analysis (AUC = 0.84; CI 95% 0.77-0.91; p < 0.0001) demonstrated high predictability to classify tumors as if using an HRD-LOH test (HRD-LOH score; “very high”: 100-75, “high”: 74-50,”mid”: 49-25 and “low”: 24-0). BRCA mutation status was also accurately predicted using a subset of CNA features (AUC = 0.71; CI 95% 0.59-0.82; p < 0.0001). Conclusions: CNAs may provide a new powerful genomic resource to HRD determination. We identified 41 CNA features in tumors to inform HR status and a subset of 10 revealed mutation status of BRCA1/2 in HGSOC. Upon further validation, a CNA-HCSOC score would be easily implemented in routine analysis pipelines in clinical labs, allowing HR testing or even broaden its application to emerging fields, such as liquid biopsy.