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. 2025 Sep 8;16(9):1052. doi: 10.3390/genes16091052

Detection of Clinically Significant BRCA Large Genomic Rearrangements in FFPE Ovarian Cancer Samples: A Comparative NGS Study

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, Angelo Minucci 1,2,*
Editors: Yutaka Suzuki, Stefania Bortoluzzi
PMCID: PMC12469499  PMID: 41009998

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.

Keywords: BRCA genes, NGS, large genomic rearrangements, ovarian cancer, copy number variations, FPG500, Diatech Myriapod® NGS BRCA1/2 panel kit, SOPHiA DDM™ Homologous Recombination Solution

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.

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 - - - -
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* 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.

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).

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
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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.

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).

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
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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).

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
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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.

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.

Figure 1.

Figure 1

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Graphical representation of mean Coverage (X), on-target reads (%), and uniformity (%), used to assess sequencing performance intra- and inter-runs.

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.

Figure 2.

Figure 2

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Strategy for interpreting CNV calls from NGS data in FFPE samples, optimized for real-world clinical implementation.

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.

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.

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.

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., A.P. (Alessia Perrucci) and M.D.B.; 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.

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.

Conflicts of Interest

The authors declare that they have no competing interests.

Funding Statement

This research received no external funding.

Footnotes

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References

  • 1.Marmolejo D.H., Wong M.Y.Z. Overview of Hereditary Breast and Ovarian Cancer (HBOC) Guidelines across Europe. Eur. J. Med. Genet. 2021;64:104350. doi: 10.1016/j.ejmg.2021.104350. [DOI] [PubMed] [Google Scholar]
  • 2.Lee J.M., Ledermann J.A., Kohn E.C. PARP Inhibitors for BRCA1/2 Mutation-Associated and BRCA-Like Malignancies. Ann. Oncol. 2014;25:32–40. doi: 10.1093/annonc/mdt384. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Dewani D., Jaiswal A., Karwade P.C. Poly (Adenosine Diphosphate Ribose) Polymerase (PARP) Inhibitors in the Treatment of Advanced Ovarian Cancer: A Narrative Review. Cureus. 2024;16:e68463. doi: 10.7759/cureus.68463. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Guffanti F., Mengoli I., Damia G. Current HRD Assays in Ovarian Cancer: Differences, Pitfalls, Limitations, and Novel Approaches. Front. Oncol. 2024;14:1405361. doi: 10.3389/fonc.2024.1405361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Ratnaparkhi R., Javellana M., Jewell A., Spoozak L. Evaluation of Homologous Recombination Deficiency in Ovarian Cancer. Curr. Treat. Options Oncol. 2024;25:237–260. doi: 10.1007/s11864-024-01176-6. [DOI] [PubMed] [Google Scholar]
  • 6.Arcieri M., Tius V., Andreetta C., Restaino S., Biasioli A., Poletto E., Damante G., Ercoli A., Driul L., Fagotti A., et al. How BRCA and Homologous Recombination Deficiency Change Therapeutic Strategies in Ovarian Cancer: A Review of Literature. Front. Oncol. 2024;14:1335196. doi: 10.3389/fonc.2024.1335196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Mafficini A., Simbolo M., Parisi A., Rusev B., Luchini C., Cataldo I., Piazzola E., Sperandio N., Turri G., Franchi M., et al. BRCA Somatic and Germline Mutation Detection in Paraffin-Embedded Ovarian Cancers by Next-Generation Sequencing. Oncotarget. 2016;7:1076–1083. doi: 10.18632/oncotarget.6834. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Ewald I.P., Ribeiro P.L., Palmero E.I., Cossio S.L., Giugliani R., Ashton-Prolla P. Genomic Rearrangements in BRCA1 and BRCA2: A Literature Review. Genet. Mol. Biol. 2009;32:437–446. doi: 10.1590/S1415-47572009005000049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Concolino P., Mello E., Minucci A., Santonocito C., Scambia G., Giardina B., Capoluongo E. Advanced Tools for BRCA1/2 Mutational Screening: Comparison between Two Methods for Large Genomic Rearrangements (LGRs) Detection. Clin. Chem. Lab. Med. 2014;52:1119–1127. doi: 10.1515/cclm-2013-1114. [DOI] [PubMed] [Google Scholar]
  • 10.Enyedi J., Pintér L., Sükösd F., Gyuris Z., Hajdu A., Határvölgyi E., Priskin K., Haracska L. Simultaneous Detection of BRCA Mutations and Large Genomic Rearrangements in Germline DNA and FFPE Tumor Samples. Oncotarget. 2016;20:61845–61859. doi: 10.18632/oncotarget.11259. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Schmidt A.Y., Hansen T.V.O., Ahlborn L.B., Jønson L., Yde C.W., Nielsen F.C. Next-Generation Sequencing-Based Detection of Germline Copy Number Variations in BRCA1/BRCA2: Validation of a One-Step Diagnostic Workflow. J. Mol. Diagn. 2017;19:809–816. doi: 10.1016/j.jmoldx.2017.07.003. [DOI] [PubMed] [Google Scholar]
  • 12.Rivera D., Paudice M., Gismondi V., Anselmi G., Vellone V.G., Varesco L., Ligurian BRCA Working Group Implementing NGS-based BRCA tumour tissue testing in FFPE ovarian carcinoma specimens: Hints from a real-life experience within the framework of expert recommendations. J. Clin. Pathol. 2021;74:596–603. doi: 10.1136/jclinpath-2020-206840. [DOI] [PubMed] [Google Scholar]
  • 13.Nero C., Duranti S., Giacomini F., Minucci A., Giacò L., Piermattei A., Genuardi M., Pasciuto T., Urbani A., Daniele G., et al. Integrating a Comprehensive Cancer Genome Profiling into Clinical Practice: A Blueprint in an Italian Referral Center. J. Pers. Med. 2022;12:1746. doi: 10.3390/jpm12101746. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Szczerba E., Kamińska K., Mierzwa T., Misiek M., Kowalewski J., Lewandowska M.A. BRCA1/2 Mutation Detection in the Tumor Tissue from Selected Polish Patients with Breast Cancer Using Next Generation Sequencing. Genes. 2021;12:519. doi: 10.3390/genes12040519. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Nishio S.Y., Moteki H., Usami S.I. Simple and efficient germline copy number variant visualization method for the Ion AmpliSeq™ custom panel. Mol. Genet. Genomic. Med. 2018;12:678–686. doi: 10.1002/mgg3.399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Barili V., Ambrosini E., Bortesi B., Minari R., De Sensi E., Cannizzaro I.R., Taiani A., Michiara M., Sikokis A., Boggiani D., et al. Genetic Basis of Breast and Ovarian Cancer: Approaches and Lessons Learnt from Three Decades of Inherited Predisposition Testing. Genes. 2024;15:219. doi: 10.3390/genes15020219. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The original contributions of this study are fully detailed within the article. For any further information, please contact the corresponding author.

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