Detection of Clinically Significant BRCA Large Genomic Rearrangements in FFPE Ovarian Cancer Samples: A Comparative NGS Study
by
, , , , , , and
Alessia Perrucci
1,2,†, 
Maria De Bonis
1,2,†,
Giulia Maneri
1,2,
Claudio Ricciardi Tenore
1,2,
Paola Concolino
1,2,
Matteo Corsi
1,2,
Alessandra Conca
1,2,
Jessica Evangelista
1,2,
Alessia Piermattei
3,
Camilla Nero
4,5,
Luciano Giacò
6,
Elisa De Paolis
1,2,
Anna Fagotti
4,5 and
Angelo Minucci
1,2,* 1
Departmental Unit of Molecular and Genomic Diagnostics, Fondazione Policlinico Gemelli IRCCS, 00168 Rome, Italy
2
Genomics Core Facility, G-STeP, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
3
Pathology Unit, Department of Woman and Child’s Health and Public Health Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
4
Unit of Oncological Gynecology, Department of Women, Children and Public Health Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
5
Department of Women, Children and Public Health Sciences Università Cattolica del Sacro Cuore, 00168 Rome, Italy
6
Bioinformatics Research Core Facility, Gemelli Science and Technology Park (G-STeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2025, 16(9), 1052; https://doi.org/10.3390/genes16091052 (registering DOI)
Submission received: 22 July 2025
/
Revised: 21 August 2025
/
Accepted: 26 August 2025
/
Published: 8 September 2025
(This article belongs to the Section Human Genomics and Genetic Diseases)
Abstract
Background: Copy number variations (CNVs), also referred to as large genomic rearrangements (LGRs), represent a crucial component of BRCA1/2 (BRCA) testing. Next-generation sequencing (NGS) has become an established approach for detecting LGRs by combining sequencing data with dedicated bioinformatics pipelines. However, CNV detection in formalin-fixed paraffin-embedded (FFPE) samples remains technically challenging, and there is the need to implement a robust and optimized analysis strategy for routine clinical practice. Methods: This study evaluated 40 FFPE ovarian cancer (OC) samples from patients undergoing BRCA testing. The performance of the amplicon-based NGS Diatech Myriapod® NGS BRCA1/2 panel (Diatech Pharmacogenetics, Jesi, Italy) was assessed for its ability to detect BRCA CNVs and results were compared to two hybrid capture-based reference assays. Results: Among the 40 analyzed samples (17 CNV-positive and 23 CNV-negative for BRCA genes), the Diatech pipeline showed a good concordance with the reference method—all CNVs were correctly identified in 16 cases with good enough sequencing quality. Only one result was inconclusive due to low sequencing quality. Conclusions: These findings support the clinical utility of NGS-based CNV analysis in FFPE samples when combined with appropriate bioinformatics tools. Integrating visual inspection of CNV plots with automated CNV calling improves the reliability of CNV detection and enhances the interpretation of results from tumor tissue. Accurate CNV detection directly from tumor tissue may reduce the need for additional germline testing, thus shortening turnaround times. Nevertheless, blood-based testing remains mandatory to determine whether detected BRCA CNVs are of hereditary or somatic origin, particularly in cases with a strong clinical suspicion of inherited predisposition due to young age and a personal and/or family history of OC.
1. Introduction
Hereditary breast and/or ovarian cancer syndrome has traditionally been the primary criterion for genetic counseling, followed by germline BRCA1/2 (BRCA) testing [1]. However, over the past decade, numerous studies have demonstrated that ovarian cancer (OC) patients harboring germline or somatic pathogenic BRCA variants (PVs) show sensitivity to poly (ADP-ribose) polymerase inhibitors (PARPi) and platinum-based chemotherapy [2,3]. In addition, functional defects in homologous recombination repair genes, collectively referred to as homologous recombination deficiency (HRD), have been clinically validated as predictive biomarkers for PARPi treatment in OC [4]. As a result, BRCA and/or HRD testing on formalin-fixed paraffin-embedded (FFPE) tumor samples, which allow simultaneous detection of both somatic and germline PVs, has become increasingly important in the molecular management of OC patients [5,6,7].
Copy number variations (CNVs), also referred to as large genomic rearrangements (LGRs), such as deletions or duplications larger than 1000 base pairs, have been identified in BRCA genes. Their prevalence varies widely among populations, ranging from less than 1% to more than 24% [8]. Consequently, LGRs account for a substantial proportion of BRCA PVs and are now an integral component of BRCA and HRD testing [6].
Next-generation sequencing (NGS) is now a well-established method for comprehensive BRCA screening from blood, enabling the simultaneous detection of single nucleotide variants (SNVs), insertions/deletions (indels), and CNVs [9,10,11]. However, CNV detection in tumor tissue presents specific challenges, including tumor heterogeneity, low tumor cellularity, the absence of a matched normal baseline, poor DNA quality, and the presence of PCR contaminants or artifacts. These factors can lead to uneven sequencing coverage across genomic regions, impairing the accurate identification of CNVs. As a result, NGS-based CNV detection may generate false positives or, more critically, false negatives, particularly when using workflows that lack validated and dedicated bioinformatics pipelines [10,11].
Among various NGS protocols, hybrid capture-based approaches have demonstrated greater reliability for CNV detection compared to amplicon-based PCR protocols. Nonetheless, several BRCA CNV assays are currently available, and not all of these are fully validated for clinical use or supported by robust bioinformatics pipelines [12].
The aim of this study was to evaluate the ability of different NGS bioinformatics pipelines to accurately identify and call BRCA CNVs from FFPE tumor samples. To this end, 40 OC samples were selected, including 17 samples harboring clinically significant LGRs. CNV calls from two hybrid capture-based NGS protocols were compared with those obtained using the amplicon PCR-based Diatech Myriapod® NGS BRCA1/2 panel kit.
Finally, a strategy was proposed to improve the interpretation of NGS data for reliable identification of CNVs in FFPE samples in a clinical setting.
2. Materials and Methods
2.1. Study Design
This study was conducted at the Genomics Core Facility, G-STeP, Fondazione Policlinico Universitario Agostino Gemelli IRCCS. A total of 40 FFPE tumor samples from patients with advanced or relapsed platinum-sensitive OC were selected. All samples were previously screened for BRCA status in our center using specific approaches: 13 of these were tested using the TruSight Oncology 500 High Throughput (TSO500HT) (Illumina Inc., San Diego, CA, USA), according to the FPG500 program [13], 9 using the SOPHiA DDM™ Homologous Recombination Solution (SOPHiA DDM™ HRD) (SOPHiA GENETICS, Lausanne, Switzerland), and 18 underwent both procedures. Sample characteristics and molecular results are summarized in Table 1. As reported, 17 samples were BRCA CNV-positive and 23 were negative. In addition, 8 CNVs detected on tissue samples showed a germline origin.
To evaluate the performance of BRCA CNV calling using the Diatech Myriapod® NGS BRCA1/2 panel kit (Diatech Pharmacogenetics, Jesi, Ancona, Italy), the selected FFPE samples were retested with this assay. All participants provided informed consent to participate in the study (Study ID: FPG500; Ethics Committee Approval No. 3837), which was conducted in accordance with the Declaration of Helsinki.
2.2. DNA Extraction
For DNA extraction, FFPE tissue samples containing >20% tumor cells and <10% necrosis, as determined by the local pathologist, were selected. DNA was extracted using the Qiagen AllPrep DNA/RNA FFPE Kit on the EZ2 Connect workstation (Qiagen, Hilden, Germany), following the manufacturer’s protocol. DNA quantity and quality were assessed using the Qubit 3.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA).
2.3. NGS Methods Used for CNV Calling
The TSO500HT assay is a panel-based NGS assay that uses a hybridization–capture-based target enrichment strategy and assesses 523 cancer-related genes. In a single assay, it enables the detection of SNVs, indels, and CNVs of 523 genes, measures genomic signatures (including MSI and TMB), and supports RNA-based calling of fusions of 55 genes and alternative splicing events in FFPE tumor tissue samples [13].
CNV calling for the TSO500HT assay was conducted using the Dragen TSO500HT v2 software ((Illumina Inc., San Diego, CA, USA). CNV analysis includes 500 CNV associated genes and can call amplifications with a limit of detection at 2.2× fold change and deletions at 0.5× fold change. In addition, a CNV step in the Dragen TSO500HT analysis workflow enables exon-level CNV detection for BRCA genes. The tertiary analysis was performed using PierianDx CGW.
SOPHiA DDM™ HRD is a hybridization-based approach able to combine in a single assay the detection by targeted sequencing of SNVs and indels in 28 homologous recombination repair (HRR) genes, including BRCA genes, and the assessment of the Genomic Integrity Index (GII) for HRD status. CNV calling was performed using the SOPHiA DDM™ platform on the coding regions of 28 genes, including the BRCA genes. For both NGS solutions, CNV calling is performed using a proprietary algorithm.
2.4. BRCA Testing with Myriapod® NGS BRCA1/2 Panel Kit and Primary Sequencing Strategy
The Myriapod® NGS BRCA1/2 panel kit software (Diatech Pharmacogenetics, Jesi, Ancona, Italy) is an in vitro diagnostic assay that enables the detection of SNVs, indels, and splice variants in the BRCA genes.
CNV analysis was performed using the Myriapod NGS data analysis software version (v) 5.0.11, which uses a proprietary algorithm for CNV calling. CNV analysis is based on the evaluation of coverage variations of contiguous amplicons for each exon, after intra-run normalization of the single sample’s coverage. Normalized amplicon coverages are then compared to a global threshold to assess the presence of a potential CNV.
With the aim of evaluating the performance of the CNV detection algorithm of the Myriapod® NGS BRCA1/2 panel kit considering “stressed” testing conditions, a “primary CNV calling strategy” was defined. In this strategy, four NGS runs, each including 10 FFPE samples, were performed on the MiSeq System, using the MiSeq Micro Flow Cell (Illumina Inc., San Diego, CA, USA). Specifically, two runs included 5 BRCA CNV-positive and 5 BRCA CNV-negative cases, while the other two runs consisted of 4 BRCA CNV-positive and 6 BRCA CNV-negative samples, respectively.
2.5. Re-Evaluation of CNV Calling Using Diatech Software by Simulating a Diagnostic Setting
To further evaluate the CNV calling performance of the Myriapod® NGS BRCA 1-2 kit in association with the Myriapod® NGS data analysis software (v 5.0.11), all 40 samples were re-analyzed to simulate a routine clinical setting. Specifically, the analysis was designed to reflect a scenario in which CNVs occur with an estimated prevalence of less than 10% in the general population, corresponding to the likelihood of detecting at most one positive case per sequencing run of 10 samples.
2.6. Read Coverage and Comparative Analyses
Sequencing performance was evaluated across the 4 NGS runs, with the aim of optimizing CNV calling. Key quality metrics assessed included mean coverage, percentage of uniformity, and on-target reads. Results were analyzed separately for each sequencing run and summarized as the mean ± standard deviation (SD) and confidence intervals, across all samples within each run.
In parallel with the assessment of sequencing quality, statistical analyses were conducted to evaluate CNV calling performance using the Myriapod® NGS BRCA1/2 panel kit in association with the Myriapod® NGS data analysis software (v 5.0.11). The overall accuracy, sensitivity, and specificity of the Myriapod® NGS BRCA 1-2 solution were calculated and compared to those of the TSO500HT and SOPHiA DDM™ HRD Solution kits.
2.7. Data Analysis
Sequencing data were processed and interpreted using the Myriapod® NGS data analysis software (v 5.0.11), a CE-marked in vitro diagnostic application for targeted NGS assays within the Diatech NGS Applications portfolio. The software automatically generates an initial variant report, incorporating SNVs, indels, and CNV analysis. For CNV detection, it plots each gene on an independent chart as a graphical visualization, assigning a copy-number score for each exon or amplicon.
3. Results
A total of 40 FFPE samples, previously tested by reference methods for BRCA CNVs (Table 1), were screened using the Myriapod® NGS BRCA1/2 panel in combination with the Myriapod® NGS data analysis software (v 5.0.11). The performance of CNV detection was assessed at three distinct levels:
- (a)
- Graphical visualization and interpretation of CNV plots;
- (b)
- CNV calling by the Myriapod® NGS data analysis software (v 5.0.11);
- (c)
- Final interpretation and reporting of CNV status, as a decision-making result integrating the two previous analysis levels.
3.1. Myriapod® NGS Data Results
3.1.1. BRCA CNV-Negative Samples
Based on the graphical visualization and interpretation of CNV status, out of the 23 CNV-negative samples, 16 could be considered negative for both genes. Four samples were CNV-negative for BRCA2 but showed a potential CNV in BRCA1. One sample was CNV-negative for BRCA1 with an inconclusive CNV result in BRCA2, while another was CNV-negative for BRCA2 with an inconclusive CNV result in BRCA1. Finally, one sample was negative for BRCA1 and showed an “other CNV” in BRCA2.
According to the CNV calling performed by the Myriapod® NGS data analysis software (v 5.0.11), seven samples were classified as CNV Not Positive for both genes. Nine samples were CNV Not Positive for BRCA2 but showed a Potential CNV in BRCA1. Three samples were identified as Potential CNV for both genes, and four samples showed a Potential CNV in BRCA2 and were CNV Not Positive for BRCA1.
The final interpretation and reporting of CNV status, based on both graphical visualization and software-based CNV calling, led to the classification of 18 samples as negative for BRCA CNVs, 3 samples as showing a Potential CNV in BRCA1, 1 sample as Inconclusive in BRCA2, and 1 sample with a potential CNV in BRCA2 (Table 2).
Considering the final interpretation and reporting of CNV results, out of the 23 negative samples, 18 would be considered completely negative, and 5 would be referred for confirmatory testing.
3.1.2. BRCA CNV-Positive Samples
Based on graphical visualization and interpretation of CNV status, among the 17 CNV-positive samples, 10 were confirmed as Positive. In two samples, a CNV was confirmed in BRCA2, while a Potential CNV was suspected in BRCA1. In one sample, the CNV was confirmed in BRCA1 and considered Inconclusive in BRCA2. In two samples, the CNV was not confirmed in BRCA1 and was Inconclusive in BRCA2. In another sample, a CNV was confirmed in BRCA1, and an additional CNV was suspected in BRCA2. Finally, one sample was considered a complete CNV calling failure in both genes. According to CNV calling by the Diatech software, one sample was classified as failed.
In nine samples, the expected CNV was correctly identified. Of these, five also showed a Potential CNV in the other gene, where a negative result was expected. In the remaining four, only the expected CNV was detected. In seven samples, the expected CNV was not detected, but a Potential CNV was identified in the other gene.
The final interpretation and reporting of CNV status, integrating graphical visualization with software-based calling, resulted in 13 samples being classified as definitively CNV-positive. Two samples were negative for the expected CNV but showed inconclusive findings in other gene. One sample was interpreted as Inconclusive for CNVs in both target genes, and one sample was classified as failed (Table 2).
Considering the final interpretation and reporting of CNV results, out of the 17 positive samples, 13 would be considered Positive, 3 would be considered Negative, and 1 would be considered Inconclusive.
Overall, under these analytical conditions, the Myriapod® NGS BRCA1/2 pipeline shows a sensitivity, specificity, and accuracy of about 80% compared to the reference assays (Table 3A).
3.2. CNV Calling in a Simulated Diagnostic Scenario (One BRCA CNV-Positive vs. Nine BRCA CNV-Negative Samples in the Same NGS Run)
3.2.1. BRCA CNV-Negative Samples
Based on the graphical visualization and interpretation of CNV status, out of the 23 CNV-negative samples, 22 could be considered negative for both genes. One sample was Negative for BRCA2 but showed a Potential CNV in BRCA1 (Table 4).
According to the CNV calling performed by the Diatech software, 13 samples were classified as CNV Not Positive for both genes. Four samples were CNV Not Positive for BRCA1 but showed a Potential CNV in BRCA2. Four samples showed a Potential CNV in BRCA2 and were CNV Not Positive for BRCA1. Two samples were identified as CNV Positive for both genes (Table 4).
The final interpretation and reporting of CNV status, integrating both graphical visualization and software- based CNV calling, classified 22 samples as Negative, and 1 sample as showing a Potential CNV in BRCA1 while being CNV Negative in BRCA2. Based on this final interpretation and reporting, among the 23 samples initially classified as CNV-negative, 22 were considered definitively negative, and 1 sample was recommended for confirmatory testing (Table 4).
3.2.2. BRCA CNV-Positive Samples
Based on the graphical visualization and interpretation of CNV status, among the 17 CNV-positive samples, 14 were confirmed as Positive. One sample showed a confirmed CNV in BRCA1 with an Inconclusive result in BRCA2, while another sample lacked the expected CNV in BRCA1 and was also Inconclusive in BRCA2 (Table 4).
According to the CNV calling performed by the Diatech software, one sample was classified as failed. In 12 samples, the expected CNV was correctly identified. Of these, four also showed a Potential CNV in the other gene, where a negative result was expected. In the remaining eight samples, only the expected CNV was detected. Four samples did not show the expected CNV in BRCA1 but instead showed a CNV in BRCA2 (Table 4).
The final interpretation and reporting of CNV status, integrating graphical visualization with software-based calling, resulted in 15 samples being classified as definitively CNV-positive. One sample was interpreted as Negative in BRCA1 (despite the expected CNV) and Inconclusive in BRCA2. One sample was classified as failed (Table 4).
Overall, under these analytical conditions, the Diatech pipeline showed a sensitivity, specificity, and accuracy of about 95% when compared to the reference assays (Table 3B).
3.3. Sequencing Metrics and Performance
The distribution of sequencing quality metrics across individual samples in different runs is shown in Figure 1. Mean coverage was consistently high. Specifically, each sequenced sample achieved an average coverage exceeding 4000×, indicating sufficient depth in line with the expected performance for tumor sequencing. Coverage uniformity further confirmed efficient and balanced target enrichment across the panel.
The percentage of on-target reads showed minimal variation between samples, demonstrating the high specificity and robustness of the protocol, and confirming that the vast majority of reads were aligned to the intended targets, resulting in reliable and uniform coverage.
Among the three metrics reported in the table, the mean coverage, coverage uniformity, and on-target read percentage, coverage uniformity is the one that most strongly impacts the accuracy and reliability of CNV detection across different samples and sequencing runs.
Mean coverage was maintained at elevated levels across all four runs, with a total average of 6680 ± 1346×. The mean uniformity across all samples was 94.9% ± 0.88, indicating efficient and balanced coverage of the targeted regions. The percentage of on-target reads was remarkably high across all runs, with minimal variability, with a total mean of 99.8% ± 0.05. Taken together, these results demonstrate a high level of technical reliability across runs, with all quality metrics falling within expected and acceptable thresholds. Although this analysis was performed on a relatively small sample set, the data reflect the robustness and high quality of the sequencing process. Collectively, these metrics demonstrate a strong technical reliability across runs, with all quality parameters well within expected and acceptable thresholds.
4. Discussion
This study evaluated the analytical performance of different NGS strategies for the detection of BRCA CNVs in FFPE samples from OC patients. Specifically, we focused on the concordance of CNV calls between two hybrid capture-based protocols and the amplicon-based Diatech Myriapod® NGS BRCA1/2 panel. Particular attention was given to the clarity and reliability of result interpretation, as well as the practical feasibility of integrating these methods into routine clinical diagnostics. A key emphasis was placed on the crucial role of bioinformatics pipelines in enabling accurate and robust CNV detection, particularly when handling FFPE-derived DNA, which is often degraded and affected by tumor heterogeneity [14]. Amplicon-based sequencing protocols are widely used in clinical diagnostics due to their high efficiency in detecting SNVs and indels [15]. However, several studies have highlighted the limitations of these methods in accurately detecting CNVs, particularly in FFPE samples [12].
An interesting aspect was the strategy used to evaluate the Diatech Myriapod® NGS BRCA1/2 solution. Specifically, the bioinformatics pipeline was first intentionally stressed by assessing CNV calling under diagnostic conditions, with up to five BRCA CNVs in a run of 10 samples. Subsequently, a more clinically realistic scenario was simulated, in which only one CNV might be present in an NGS run of 10 OC samples. In both analytical conditions, the performance of the pipeline was satisfactory. In the second analysis mode, CNV calling showed 96% accuracy in correctly identifying CNVs. Among the positive cases, excluding one sample that failed sequencing (ID: 9), only one case (ID: 17) was missed, while all the remaining CNVs were correctly identified (Table 4). We recognize that this limitation may arise particularly in the context of somatic CNVs, where different NGS strategies can show variable detection performance, especially due to amplicon-specific dynamics within each run. However, missing a somatic CNV is generally considered less critical than failing to identify a germline CNV, which has significant implications for patient management and familial risk assessment [16]. In this contest, we emphasize that all germline rearrangements were successfully identified by the new NGS strategy (Table 4). Regarding the clinical classification, all CNVs were reported as oncogenic according to the OncoKB Database (Table 1). In addition, most of these are currently reported in germline databases, such as ClinVar https://www.ncbi.nlm.nih.gov/clinvar/ (accessed on 3 June 2025) and LOVD “https://databases.lovd.nl/shared/genes” (accessed on 3 June 2025), and classified as pathogenic. The clinical applicability of these variants has allowed our patients to benefit from treatment with PARP inhibitors. Furthermore, the identification of a germline variant enables genetic testing for the patient’s closest relatives.
From a diagnostic perspective, our findings suggest that graphical visualization and software-based interpretation should be considered complementary tools. Therefore, a multi-step approach combining algorithmic CNV calling, graphical visualization, and expert review is confirmed as the most reliable strategy, particularly for CNV detection in amplicon-based protocols using FFPE samples (Figure 2). It is essential to note, however, that the manufacturer’s instructions require that samples automatically classified by the software as potentially CNV-positive must always be confirmed by an orthogonal method.
The reliability of CNV detection is also intrinsically linked to the quality of sequencing data. Accurate variant calling requires that key quality metrics, such as the mean coverage, coverage uniformity, and on-target read percentage, meet established thresholds. When these metrics fall below recommended levels, the risk of inconclusive or incorrect calls increases, primarily due to insufficient read depth or uneven read distribution. In our study, sequencing metrics remained consistently high across runs, with mean coverage exceeding 6600× and on-target rates approaching 100%, supporting the technical robustness of the workflow and likely contributing to successful CNV detection.
Among the strengths of this study are the use of real-world clinical FFPE samples, comparison across different NGS platforms, and simulation of practical diagnostic scenarios. However, certain limitations must be acknowledged. First, the sample size was relatively small and may not fully capture the heterogeneity of BRCA CNVs observed in routine clinical practice. Second, orthogonal validation methods (e.g., MLPA and qPCR) were not employed for all discordant or borderline cases, which may have introduced uncertainty in result interpretation.
Looking ahead, integrating automated quality control checkpoints and confidence scoring for CNV calls could reduce the burden of manual review while enhancing overall reliability. Furthermore, continued development and validation of dedicated CNV detection algorithms specifically optimized for amplicon-based sequencing will be essential for broader clinical implementation.
5. Conclusions
Our study highlights the importance of having validated NGS workflows and bioinformatics pipelines for the accurate detection of BRCA CNVs in FFPE tumor samples. The amplicon-based Myriapod® NGS BRCA1/2 panel in combination with the Myriapod® NGS data analysis software (v 5.0.11) proved effective in identifying CNVs, demonstrating strong concordance with hybrid capture-based approaches when combined with optimized bioinformatics analysis, expert interpretation, and reporting.
These elements are essential for the reliable molecular identification of BRCA CNVs, which have critical implications for the management of OC patients, including therapeutic decisions involving PARP inhibitors and cascade testing in hereditary cancer syndromes.
For these reasons, it is possible to hypothesize a decision-making workflow, as described in Figure 2, where suspected oncogenic CNVs can guide either a therapeutic approach or germline screening, similar to other PVs in BRCA genes. Therefore, the accurate and reliable detection of these alterations remains a fundamental requirement in the genomic evaluation of OC patients.
Limitations
This study has some limitations. Despite the significant number of BRCA CNVs analyzed, all were CNV losses; therefore, we were unable to assess the performance of the evaluated software in detecting BRCA CNV gains.
An additional limitation is the lack of detailed information on the CNV calling algorithms used by each platform. As stated in the manuscript, each bioinformatics pipeline is proprietary, which limits transparency regarding the specific parameters or thresholds applied during CNV detection.
Author Contributions
Conceptualization, A.M. and A.P. (Alessia Perrucci); methodology, M.C.; software, L.G.; validation, A.M., J.E., and M.D.B.; formal analysis, E.D.P.; investigation, A.C.; resources, A.P. (Alessia Piermattei); data curation, P.C.; writing—original draft preparation, A.M.; writing—review and editing, A.M., C.N., A.P. (Alessia Perrucci), and M.D.B.; visualization, C.R.T. and G.M.; supervision, A.F. and A.M. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee Research of Catholic University of the Sacred Heart of Rome (Study ID: FPG500; protocol code No. 3837 and July 2023). All participants provided informed consent to participate in the study (Study ID: FPG500; Ethics Committee Approval No. 3837).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.
Data Availability Statement
The original contributions of this study are fully detailed within the article. For any further information, please contact the corresponding author.
Acknowledgments
We gratefully acknowledge the Diatech team for their technical support in contributing to the results presented. We also thank the Genomics Core Facility Group at the Gemelli Science and Technology Park (G-STeP) for their valuable contribution. This work is dedicated to the memory of Giovanni Scambia, whose untimely passing represents a profound loss to the scientific community. His exceptional dedication, visionary leadership, and significant contributions to the field continue to inspire and guide both this project and future research efforts.
Conflicts of Interest
The authors declare that they have no competing interests.
Abbreviations
CNVs: copy number variations; LGRs: large genomic rearrangements; BRCA: BRCA1/2; NGS: next-generation sequencing; FFPE: formalin-fixed paraffin-embedded; OC: ovarian cancer; PVs: pathogenic variants; PARP-1: poly-(ADP-ribose) polymerase; HRD: homologous recombination repair; SNVs: single nucleotide variants; INDELS: insertions/deletions; HRR: homologous recombination repair; GII: Genomic Integrity Index; MLPA: multiplex ligation-dependent probe amplification; SD: standard deviation; CI: confidence interval.
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Figure 1.
Graphical representation of mean Coverage (X), on-target reads (%), and uniformity (%), used to assess sequencing performance intra- and inter-runs.
Figure 1.
Graphical representation of mean Coverage (X), on-target reads (%), and uniformity (%), used to assess sequencing performance intra- and inter-runs.
Figure 2.
Strategy for interpreting CNV calls from NGS data in FFPE samples, optimized for real-world clinical implementation.
Figure 2.
Strategy for interpreting CNV calls from NGS data in FFPE samples, optimized for real-world clinical implementation.
Table 1.
Sample characteristics and molecular results.
| ID | Disease | Timing | Age of the Sample | TC (%) | Reference Assay | CNV Status | Detected CNVs * | CNV Status | OncoKB Database Classification *** | Clinical Implication |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | HGSC | PRIMARY | 2024 | 90 | TSO500HT | Positive | BRCA2 exon 2–3 deletion | Somatic | Oncogenic (loss of function) | PARPi treatment |
| 2 | HGSC | PRIMARY | 2024 | 90 | TSO500HT; SOPHiA DDM HRD | Positive | BRCA1 exon 19 deletion | Somatic | Oncogenic (loss of function) | PARPi treatment |
| 3 | HGSC | PRIMARY | 2024 | 60 | TSO500HT; SOPHiA DDM HRD | Positive | BRCA1 exon 8–11 deletion | Germline ** | Oncogenic (loss of function) | PARPi treatment; cascade screening |
| 4 | HGSC | PRIMARY | 2024 | 70 | TSO500HT; SOPHiA DDM HRD | Positive | BRCA1 exon 20 deletion | Germline ** | Oncogenic (loss of function) | PARPi treatment; Cascade screening |
| 5 | HGSC | RELAPSE | 2023 | 90 | TSO500HT; SOPHiA DDM HRD | Positive | BRCA1 exon 2 deletion | Germline ** | Oncogenic (loss of function) | PARPi treatment; cascade screening |
| 6 | HGSC | RELAPSE | 2024 | 90 | SOPHiA DDM HRD | Positive | BRCA1 exon 16–17 deletion | Germline ** | Oncogenic (loss of function) | PARPi treatment; cascade screening |
| 7 | HGSC | RELAPSE | 2024 | 70 | TSO500HT; SOPHiA DDM HRD | Positive | BRCA1 exon 4–7 deletion | Germline ** | Oncogenic (loss of function) | PARPi treatment; cascade screening |
| 8 | HGSC | PRIMARY | 2023 | 90 | TSO500HT; SOPHiA DDM HRD | Positive | BRCA2 exon 9–21 deletion | Somatic | Oncogenic (loss of function) | PARPi treatment |
| 9 | ENOC | PRIMARY | 2024 | 90 | TSO500HT | Positive | BRCA1 exon 11 deletion | Somatic | Oncogenic (loss of function) | PARPi treatment |
| 10 | HGSC | PRIMARY | 2024 | 95 | TSO500HT; SOPHiA DDM HRD | Positive | BRCA1 exon 2–3 deletion | Somatic | Oncogenic (loss of function) | PARPi treatment |
| 11 | HGSC | PRIMARY | 2024 | 60 | TSO500HT; SOPHiA DDM HRD | Positive | BRCA1 exon 19 deletion | Germline * | Oncogenic (loss of function) | PARPi treatment; cascade screening |
| 12 | HGSC | RELAPSE | 2024 | 80 | SOPHiA DDM HRD | Positive | BRCA1 exon 2 deletion | Germline ** | Oncogenic (loss of function) | PARPi treatment; cascade screening |
| 13 | HGSC | RELAPSE | 2024 | 80 | SOPHiA DDM HRD | Positive | BRCA1 exon 2–19 deletion | Somatic | Oncogenic (loss of function) | PARPi treatment |
| 14 | HGSC | PRIMARY | 2024 | 80 | TSO500HT | Positive | BRCA1 exon 15 deletion | Somatic | Oncogenic (loss of function) | PARPi treatment |
| 15 | HGSC | PRIMARY | 2024 | 55 | TSO500HT; SOPHiA DDM HRD | Positive | BRCA1 whole gene deletion | Germline ** | Oncogenic (loss of function) | PARPi treatment; cascade screening |
| 16 | HGSC | PRIMARY | 2025 | 30 | TSO500HT; SOPHiA DDM HRD | Positive | BRCA2 exon 11–27 deletion | Somatic | Oncogenic (loss of function) | PARPi treatment |
| 17 | HGSC | PRIMARY | 2024 | 80 | TSO500HT | Positive | BRCA1 exon 3–23 deletion | Somatic | Oncogenic (loss of function) | PARPi treatment |
| 18 | OCS | RELAPSE | 2024 | 60 | TSO500HT; SOPHiA DDM HRD | Negative | - | - | - | - |
| 19 | HGSC | PRIMARY | 2024 | 80 | TSO500HT; SOPHiA DDM HRD | Negative | - | - | - | - |
| 20 | HGSC | PRIMARY | 2025 | 20 | TSO500HT | Negative | - | - | - | |
| 21 | HGSC | PRIMARY | 2024 | 80 | TSO500HT; SOPHiA DDM HRD | Negative | - | - | - | - |
| 22 | HGSC | PRIMARY | 2024 | 80 | TSO500HT | Negative | - | - | - | - |
| 23 | CCOC | PRIMARY | 2024 | 80 | TSO500HT | Negative | - | - | - | - |
| 24 | ENOC | PRIMARY | 2024 | 90 | TSO500HT | Negative | - | - | - | - |
| 25 | HGSC | PRIMARY | 2024 | 25 | TSO500HT | Negative | - | - | - | - |
| 26 | HGSC | PRIMARY | 2024 | 80 | TSO500HT; SOPHiA DDM HRD | Negative | - | - | - | - |
| 27 | HSGC | RELAPSE | 2024 | 95 | SOPHiA DDM HRD | Negative | - | - | - | - |
| 28 | ENOC | RELAPSE | 2024 | 30 | SOPHiA DDM HRD | Negative | - | - | - | - |
| 29 | HSGC | RELAPSE | 2024 | 30 | SOPHiA DDM HRD | Negative | - | - | - | - |
| 30 | HSGC | RELAPSE | 2024 | 35 | SOPHiA DDM HRD | Negative | - | - | - | - |
| 31 | HGSC | PRIMARY | 2023 | 70 | TSO500HT; SOPHiA DDM HRD | Negative | - | - | - | - |
| 32 | CCOC | PRIMARY | 2023 | 80 | TSO500HT | Negative | - | - | - | - |
| 33 | HGSC | PRIMARY | 2024 | 40 | TSO500HT; SOPHiA DDM HRD | Negative | - | - | - | - |
| 34 | HGSC | PRIMARY | 2024 | 36 | SOPHiA DDM HRD | Negative | - | - | - | - |
| 35 | HGSC | PRIMARY | 2023 | 80 | SOPHiA DDM HRD | Negative | - | - | - | - |
| 36 | HGSC | PRIMARY | 2025 | 70 | TSO500HT; SOPHiA DDM HRD | Negative | - | - | - | - |
| 37 | CCOC | PRIMARY | 2025 | 20 | TSO500HT | Negative | - | - | - | - |
| 38 | HGSC | PRIMARY | 2025 | 30 | TSO500HT; SOPHiA DDM HRD | Negative | - | - | - | - |
| 39 | CCOC | PRIMARY | 2025 | 70 | TSO500HT | Negative | - | - | - | - |
| 40 | HGSC | PRIMARY | 2025 | 25 | TSO500HT | Negative | - | - | - | - |
* The reference sequences were NG_005905.2/NM_007294.3 and NG_012772.3/NM_000059.3 for BRCA1 and BRCA2, respectively. ** MLPA was performed as a reflex test on peripheral blood samples using the SALSA P002 BRCA1 and SALSA P045 BRCA2 MLPA kits (MRC Holland). *** OncoKB Database https://www.oncokb.org/ (accessed on 3 June 2025). Abbreviations: HGSC: high-grade serous carcinoma; ENOC: endometrioid ovarian cancer, OCS: ovarian carcinosarcoma; CCOC: Clear Cell Ovarian Carcinoma; TC: tumor content.
Table 2.
Myriapod® NGS data results.
| ID Samples | Graphical Visualization and Interpretation of CNV Plots | CNV Calling by Myriapod® NGS Data Analysis Software | Final Interpretation and Reporting CNV | |||
|---|---|---|---|---|---|---|
| BRCA1 | BRCA2 | BRCA1 | BRCA2 | BRCA1 | BRCA2 | |
| BRCA CNV-negative samples | ||||||
| 18 | cCNV | oCNV | Potential CNV | Potential CNV | Negative | oCNV |
| 19 | oCNV | cCNV | Potential CNV | CNV Not Positive | Negative | Negative |
| 20 | cCNV | cCNV | CNV Not Positive | Potential CNV | Negative | Negative |
| 21 | cCNV | cCNV | Potential CNV | Potential CNV | Negative | Negative |
| 22 | cCNV | cCNV | Potential CNV | CNV Not Positive | Negative | Negative |
| 23 | cCNV | cCNV | Potential CNV | CNV Not Positive | Negative | Negative |
| 24 | cCNV | cCNV | Potential CNV | CNV Not Positive | Negative | Negative |
| 25 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 26 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 27 | oCNV | cCNV | Potential CNV | CNV Not Positive | oCNV | Negative |
| 28 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 29 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 30 | oCNV | cCNV | Potential CNV | CNV Not Positive | oCNV | Negative |
| 31 | cCNV | cCNV | Potential CNV | CNV Not Positive | Negative | Negative |
| 32 | cCNV | cCNV | Potential CNV | CNV Not Positive | Negative | Negative |
| 33 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 34 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 35 | cCNV | cCNV | CNV Not Positive | Potential CNV | Negative | Negative |
| 36 | cCNV | cCNV | CNV Not Positive | Potential CNV | Negative | Negative |
| 37 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 38 | iCNV | cCNV | Potential CNV | CNV Not Positive | Negative | Negative |
| 39 | cCNV | iCNV | CNV Not Positive | Potential CNV | Negative | iCNV |
| 40 | oCNV | cCNV | Potential CNV | Potential CNV | oCNV | Negative |
| BRCA CNV-positive samples | ||||||
| 1 | oCNV | cCNV | Potential CNV | Potential CNV | Positive | Positive |
| 2 | cCNV | cCNV | Potential CNV | Potential CNV | Positive | Negative |
| 3 | cCNV | cCNV | Potential CNV | Potential CNV | Positive | Negative |
| 4 | cCNV | cCNV | Potential CNV | CNV Not Positive | Positive | Negative |
| 5 | cCNV | cCNV | Potential CNV | CNV Not Positive | Positive | Negative |
| 6 | cCNV | cCNV | CNV Not Positive | Potential CNV | Positive | Negative |
| 7 | cCNV | oCNV | CNV Not Positive | Potential CNV | Negative | iCNV |
| 8 | oCNV | cCNV | Potential CNV | Potential CNV | oCNV | Positive |
| 9 | fCNV | fCNV | CNV Failed | CNV Failed | fCNV | fCNV |
| 10 | cCNV | cCNV | CNV Not Positive | Potential CNV | Positive | Negative |
| 11 | cCNV | cCNV | Potential CNV | CNV Not Positive | Positive | Negative |
| 12 | cCNV | iCNV | CNV Not Positive | Potential CNV | Positive | iCNV |
| 13 | cCNV | cCNV | CNV Not Positive | Potential CNV | Positive | Negative |
| 14 | cCNV | cCNV | Potential CNV | Potential CNV | Positive | Positive |
| 15 | ntCNV | iCNV | CNV Not Positive | Potential CNV | Negative | iCNV |
| 16 | cCNV | cCNV | CNV Not Positive | Potential CNV | Negative | Positive |
| 17 | ntCNV | iCNV | CNV Not Positive | Potential CNV | iCNV | iCNV |
Abbreviations: oCNV: other CNV calling in contrast with the reference test; cCNV: confirmed CNV results considering reference tests; fCNV: failed CNV; iCNV: inconclusive CNV; ntCNV: not detected CNV.
Table 3.
Concordance analysis of CNV calling between the Myriapod® NGS BRCA1/2 panel and TSO500HT and the SOPHiA DDM™ HRD assays. (A) Primary CNV calling strategy and concordance analysis (5/4 BRCA CNV-positive vs. 5/6 BRCA CNV-negative samples). (B) CNV calling in a simulated BRCA diagnostic setting (one BRCA CNV-positive vs. nine BRCA CNV-negative samples).
Table 3.
Concordance analysis of CNV calling between the Myriapod® NGS BRCA1/2 panel and TSO500HT and the SOPHiA DDM™ HRD assays. (A) Primary CNV calling strategy and concordance analysis (5/4 BRCA CNV-positive vs. 5/6 BRCA CNV-negative samples). (B) CNV calling in a simulated BRCA diagnostic setting (one BRCA CNV-positive vs. nine BRCA CNV-negative samples).
| (A) Primary CNV calling strategy and concordance analysis | ||
| Diatech Myriapod® NGS CNVs final interpretation | TSO500HT/SOPHiA DDM™ HRD | |
| CNV-Positive | CNV-Negative | |
| BRCA CNV-Positive | 13 | 5 |
| BRCA CNV-Negative | 3 | 18 |
| Inconclusive | 1 | |
| Analytical Performance | Value (%) | |
| Sensitivity | 81.25 | |
| Specificity | 78.26 | |
| Positive predictive value | 72.22 | |
| Negative predictive value | 85.71 | |
| Accuracy | 79.49 | |
| (B) CNV calling in a simulated BRCA diagnostic setting | ||
| Diatech Myriapod® NGS CNVs final interpretation | TSO500HT/SOPHiA DDM™ HRD | |
| CNV-Positive | CNV-Negative | |
| BRCA CNV-Positive | 15 | 1 |
| BRCA CNV-Negative | 1 | 22 |
| Inconclusive | 1 | |
| Analytical Performance | Value (%) | |
| Sensitivity | 93.75 | |
| Specificity | 95.65 | |
| Positive predictive value | 94.87 | |
| Negative predictive value | 93.75 | |
| Accuracy | 95.65 | |
Table 4.
Myriapod® NGS data analysis in a simulated BRCA diagnostic setting (one BRCA CNV-positive vs. nine BRCA CNV-negative samples).
Table 4.
Myriapod® NGS data analysis in a simulated BRCA diagnostic setting (one BRCA CNV-positive vs. nine BRCA CNV-negative samples).
| ID Samples | Graphical Visualization and Interpretation of CNV Plots | CNV Calling By Myriapod® NGS Data Analysis Software | Final Interpretation and Reporting CNV | |||
|---|---|---|---|---|---|---|
| BRCA1 | BRCA2 | BRCA1 | BRCA2 | BRCA1 | BRCA2 | |
| BRCA CNV-negative samples | ||||||
| 18 | cCNV | cCNV | CNV Not Positive | Potential CNV | Negative | Negative |
| 19 | cCNV | cCNV | CNV Not Positive | Potential CNV | Negative | Negative |
| 20 | cCNV | cCNV | Potential CNV | CNV Not Positive | Negative | Negative |
| 21 | cCNV | cCNV | CNV Not Positive | Potential CNV | Negative | Negative |
| 22 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 23 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 24 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 25 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 26 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 27 | cCNV | cCNV | Potential CNV | CNV Not Positive | Negative | Negative |
| 28 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 29 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 30 | cCNV | cCNV | Potential CNV | CNV Not Positive | Negative | Negative |
| 31 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 32 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 33 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 34 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 35 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 36 | cCNV | cCNV | CNV Not Positive | Potential CNV | Negative | Negative |
| 37 | cCNV | cCNV | CNV Not Positive | CNV Not Positive | Negative | Negative |
| 38 | cCNV | cCNV | Potential CNV | CNV Not Positive | Negative | Negative |
| 39 | oCNV | cCNV | Potential CNV | Potential CNV | oCNV | Negative |
| 40 | cCNV | cCNV | Potential CNV | Potential CNV | Negative | Negative |
| BRCA CNV-positive samples | ||||||
| 1 | cCNV | cCNV | CNV Not Positive | Potential CNV | Negative | Positive |
| 2 | cCNV | cCNV | Potential CNV | CNV Not Positive | Positive | Negative |
| 3 | cCNV | cCNV | Potential CNV | Potential CNV | Positive | Negative |
| 4 | cCNV | cCNV | Potential CNV | CNV Not Positive | Positive | Negative |
| 5 | cCNV | cCNV | Potential CNV | CNV Not Positive | Positive | Negative |
| 6 | cCNV | cCNV | Potential CNV | CNV Not Positive | Positive | Negative |
| 7 | cCNV | iCNV | CNV Not Positive | Potential CNV | Positive | iCNV |
| 8 | cCNV | cCNV | Potential CNV | Potential CNV | Negative | Positive |
| 9 | fCNV | fCNV | CNV failed | CNV failed | fCNV | fCNV |
| 10 | cCNV | cCNV | Potential CNV | CNV Not Positive | Positive | Negative |
| 11 | cCNV | cCNV | Potential CNV | CNV Not Positive | Positive | Negative |
| 12 | cCNV | cCNV | Potential CNV | Potential CNV | Positive | Negative |
| 13 | cCNV | cCNV | CNV Not Positive | Potential CNV | Positive | Negative |
| 14 | cCNV | cCNV | Potential CNV | Potential CNV | Positive | Negative |
| 15 | cCNV | cCNV | CNV Not Positive | Potential CNV | Positive | Negative |
| 16 | cCNV | cCNV | CNV Not Positive | Potential CNV | Negative | Positive |
| 17 | ntCNV | iCNV | CNV Not Positive | Potential CNV | Negative | iCNV |
Abbreviations: oCNV: other CNV calling in contrast to the reference test; cCNV: confirmed CNV results considering reference tests; fCNV: failed CNV; iCNV: inconclusive CNV; ntCNV: not detected CNV.
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Perrucci, A.; De Bonis, M.; Maneri, G.; Ricciardi Tenore, C.; Concolino, P.; Corsi, M.; Conca, A.; Evangelista, J.; Piermattei, A.; Nero, C.; et al. Detection of Clinically Significant BRCA Large Genomic Rearrangements in FFPE Ovarian Cancer Samples: A Comparative NGS Study. Genes 2025, 16, 1052. https://doi.org/10.3390/genes16091052
AMA Style
Perrucci A, De Bonis M, Maneri G, Ricciardi Tenore C, Concolino P, Corsi M, Conca A, Evangelista J, Piermattei A, Nero C, et al. Detection of Clinically Significant BRCA Large Genomic Rearrangements in FFPE Ovarian Cancer Samples: A Comparative NGS Study. Genes. 2025; 16(9):1052. https://doi.org/10.3390/genes16091052
Chicago/Turabian StylePerrucci, Alessia, Maria De Bonis, Giulia Maneri, Claudio Ricciardi Tenore, Paola Concolino, Matteo Corsi, Alessandra Conca, Jessica Evangelista, Alessia Piermattei, Camilla Nero, and et al. 2025. "Detection of Clinically Significant BRCA Large Genomic Rearrangements in FFPE Ovarian Cancer Samples: A Comparative NGS Study" Genes 16, no. 9: 1052. https://doi.org/10.3390/genes16091052
APA StylePerrucci, A., De Bonis, M., Maneri, G., Ricciardi Tenore, C., Concolino, P., Corsi, M., Conca, A., Evangelista, J., Piermattei, A., Nero, C., Giacò, L., De Paolis, E., Fagotti, A., & Minucci, A. (2025). Detection of Clinically Significant BRCA Large Genomic Rearrangements in FFPE Ovarian Cancer Samples: A Comparative NGS Study. Genes, 16(9), 1052. https://doi.org/10.3390/genes16091052
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