BRACNAC: A BRCA1 and BRCA2 Copy Number Alteration Caller from Next-Generation Sequencing Data

Abstract

Detecting copy number variations (CNVs) and alterations (CNAs) in the BRCA1 and BRCA2 genes is essential for testing patients for targeted therapy applicability. However, the available bioinformatics tools were initially designed for identifying CNVs/CNAs in whole-genome or -exome (WES) NGS data or targeted NGS data without adaptation to the BRCA1/2 genes. Most of these tools were tested on sample cohorts of limited size, with their use restricted to specific library preparation kits or sequencing platforms. We developed BRACNAC, a new tool for detecting CNVs and CNAs in the BRCA1 and BRCA2 genes in NGS data of different origin. The underlying mechanism of this tool involves various coverage normalization steps complemented by CNV probability evaluation. We estimated the sensitivity and specificity of our tool to be 100% and 94%, respectively, with an area under the curve (AUC) of 94%. The estimation was performed using the NGS data obtained from 213 ovarian and prostate cancer samples tested with in-house and commercially available library preparation kits and additionally using multiplex ligation-dependent probe amplification (MLPA) (12 CNV-positive samples). Using freely available WES and targeted NGS data from other research groups, we demonstrated that BRACNAC could also be used for these two types of data, with an AUC of up to 99.9%. In addition, we determined the limitations of the tool in terms of the minimum number of samples per NGS run (≥20 samples) and the minimum expected percentage of CNV-negative samples (≥80%). We expect that our findings will improve the efficacy of BRCA1/2 diagnostics. BRACNAC is freely available at the GitHub server.

1. Introduction

The clinical significance of BRCA1 and BRCA2 pathogenic germline variants and somatic alterations is of great importance for patients with breast and ovarian cancer. Their detection allows at-risk patients to be identified and more effective treatment strategies to be chosen, such as PARP inhibitors for carriers of pathogenic variants. Numerous commercial tests were developed to detect point and short mutations. These include the AmpliSeq BRCA Panel (Illumina, San Diego, CA, USA), the GeneRead QIAact BRCA 1/2 Panel (Qiagen, Hilden, Germany), the Ion AmpliSeq™ BRCA1 and BRCA2 Panel (Thermo Fisher Scientific, Waltham, MA, USA), and the ACCEL-AMPLICON^® BRCA1 and BRCA2 Panel (Swift Biosciences, Ann Arbor, MI, USA). These developments improved the statistics of the occurrence of pathogenic BRCA1/2 variants worldwide and enlarged the patient groups suitable for targeted therapy. Moreover, somatic large rearrangements (LRs) are considered to be involved in the mechanism of drug resistance [1], which further increases their clinical relevance.

However, the study of large rearrangements, including germline copy number variations (CNVs) and somatic copy number alterations (CNAs), remains limited due to the scarcity of reliable programs that can be tested on a large sample of patients [2,3]. In some studies, large rearrangements (LRs) were detected by combining several methods, including NGS dosage, microarray comparative genomic hybridization (CGH), and/or multiplex ligation-dependent probe amplification (MLPA) [4]. The latter has long been considered the gold standard for CNV detection. The methodology involves the amplification of probes that can be ligated only if they both hybridize to neighboring regions, yielding PCR products with different lengths for multiple target regions [5]. It would be more significant to detect CNVs and CNAs along with short mutations routinely identified with targeted NGS. Several new algorithms for CNV detection were developed for targeted NGS data, with some organized into bioinformatic tools, e.g., panelcn.MOPS [6], CNVpytor [7], and several simple R scripts [8]. All of them use coverage depth values for target regions, detecting outlier values, i.e., the signals of potential CNVs. At the same time, these algorithms have some differences. For example, the R scripts developed by Singh et al. [8] create a pool of normal samples, evaluate the coverage for many sliding windows, and calculate the ratio of coverage depth for the query sample and a pool of normal samples for each nucleotide of the sliding windows. Panelcn.MOPS additionally applies one-step normalization to the third quartile, also selecting control samples. The unique feature of CNVpytor is the correction of the GC content and the use of variant allele frequency (VAF) values. Despite the high sensitivity and specificity exhibited by the tools mentioned above for the datasets tested, their suitability for other datasets and in-house targeted NGS panels remains unverified in large cohorts, particularly for the two genes BRCA1 and BRCA2, with frequent CNVs.

Here, we present a new tool for the detection of BRCA1/2 CNVs, called BRACNAC (BRAC1/2 Copy Number Alteration Caller). It should be noted that our tool does not require a control sample set. The method involves several steps of data normalization and the identification of sets of target regions with elevated or lowered copy numbers. Of critical relevance is the estimation of the probability of an observed copy number increase or decrease to be random, i.e., the p-value. We compared the performance of our tool with those of the multiplex ligation-dependent probe amplification (MLPA) method regarded as the gold standard for CNV detection and of another recently reported tool for targeted NGS panels, referred to as panelcn.MOPS. We confirmed the suitability of our tool for targeted NGS data from other studies and whole-exome (WES) sequencing data. Furthermore, we show that many BRCA1/2 NGS datasets previously described could be reanalyzed using our tool to extend our knowledge of BRCA1/2 CNV occurrence. Finally, we describe our attempt to define the limits of BRACNAC by identifying CNVs with different numbers of samples per run and different ratios of patients with and without CNVs.

2. Results

The BRACNAC algorithm and initial values of different parameters were chosen based on the in-house dataset of 213 leukocyte DNA samples. The whole coding sequences of the BRCA1 and BRCA2 genes were obtained with in-house and commercial targeted NGS panels. Other datasets were used for comparison with the panelcn.MOPS results and validation on other NGS library enrichment approaches and sequencing technologies (

Table 1. NGS datasets used in this study.

Step	Type of Dataset	Number of Samples	Method of CNV Validation
- Initial algorithm development - Optimization of threshold values - Comparison with panelcn.MOPS results	in-house targeted NGS data	147	Only BRCA1 MRC-Holland-179 BRCA1 and BRCA2/CHEK2 MRC-Holland-34
	GeneRead BRCA panel v2 (Qiagen)	66
- Comparison with panelcn.MOPS results - Validation using other researchers’ targeted NGS data	TruSightTM Cancer targeted NGS-panel	99	BRCA1 and BRCA2 MLPA
- Validation using other researchers’ targeted NGS data	AmpliSeq BRCA1 and BRCA2 targeted NGS panel	192	No
- Validation using other researchers’ WES data	Whole-exome sequencing data	60	No

).

2.1. Algorithm

The main idea of the BRACNAC algorithm is to remove any potential variation between samples (first normalization step), between target regions (second normalization step), and arising from differences in multiplex reactions (third (optional) normalization) or clusters of sample sets (e.g., samples whose libraries were prepared in different experiments) (Figure 1A–D). The normalization steps are carried out by dividing each value by the median value of the corresponding sets of values. The resulting value should be 1.0 if there are no changes in copy number for a target. Therefore, by multiplying the resulting value by 2, we should obtain the number of copies of the target in the diploid genome. Then, the algorithm searches for values that deviate from 2.0. The theoretical values for deletion and duplication are 1.0 and 3.0, respectively. In the case of CNVs/CNAs of several exons or one exon but covered by several target regions, these deviating values follow each other, forming a continuous set. However, natural variation in the number of reads makes these values vary around 1.0 and 3.0, respectively. Exons comprising several target regions may demonstrate values with lower deviation than that obtained for one copy (e.g., 1.3 instead of 1.0 for deletions and 2.7 instead of 3.0 for amplifications). Therefore, the initial algorithm allows determining four thresholds to consider some coverage value of a target region for deletions or amplifications. The second values (higher for deletions, “del2”, e.g., 1.7 and lower for amplifications, “dupl2”, e.g., 2.4) are used for calculating the CNV/CNA score. The first values (lower for deletions, “del1”, e.g., 1.3 and higher for amplifications, “dupl1”, e.g., 2.7) are used for lowering the score if some values are between the first and the second thresholds. We set the limits that any potential CNV/CNA score should include at least one value less than “del1” (for deletions) or higher than “dupl1” (for amplifications) and no more than one value higher than “del2” (for deletions) or lower than “dupl2” (for amplifications) (Figure 1E,F). Additionally, the lower threshold of deletions is used as the basis of the power function for decreasing the score (1.3 in the equations of the Section 4).

We collected different overlapping sets of target regions by extracting the normalized coverage values of neighboring target regions exhibiting copy numbers below 2.0 (for deletions) or above 2.0 (for amplifications). Subsequently, we calculated the scores and estimated the p-values based on the probability of a given set of target regions to have such a score at random. For that purpose, the normalized coverage values of the analyzed sample were randomly shuffled. Then, this procedure was repeated for the values of the same target regions of other samples. In this way, we accounted for variation of the coverage values of the examined sample and target regions among all samples. The p-value evaluation by the procedure described above was performed in two steps. In the first step, BRACNAC identified CNVs/CNAs with a high score (by default, 9.9) and a low p-value (by default, 0.01). Then, it excluded such samples from the second step of p-value evaluation to avoid their influence on the probability values for other potential CNVs/CNAs. We implemented this two-step p-value evaluation procedure to decrease the false-negative rate.

2.2. BRACNAC vs. MLPA and Panelcn.MOPS for Targeted NGS

Two hundred eleven leukocyte DNA samples from ovarian cancer patients and two samples from prostate cancer patients were analyzed using targeted NGS, followed by BRACNAC and panelcn.MOPS analysis and MLPA. MLPA identified twelve positive cases (

Table 2. CNV-positive cases identified with MLPA. The patients with a complete match between the MLPA and the BRACNAC results are shown in bold. del_BRCA1_up-ex2 and del_BRCA1_ex2-ex2 were regarded as matched because the targeted NGS panel used did not cover the BRCA1 non-coding (exon 1 and exon 2, 5’-ends) and promoter regions.

Sample ID	CNV Detected with MLPA	CNV Detected with BRACNAC
mlpa_1	del_BRCA1_ex20-ex23	del_BRCA1_ex20-ex23
mlpa_2	del_BRCA1_ex10-ex11	del_BRCA1_ex10-ex11
mlpa_3	del_BRCA2_ex21-ex24	del_BRCA2_ex21-ex24
mlpa_4	del_BRCA1_up-ex2	del_BRCA1_ex2-ex2
mlpa_5	dupl_BRCA1_ex4-ex6	dupl_BRCA1_ex5-ex6
mlpa_6	del_BRCA1_ex3-ex12	del_BRCA1_ex4-ex12
mlpa_7	del_BRCA1_ex20-ex21	del_BRCA1_ex20-ex21
mlpa_8	del_BRCA1_up-ex2	del_BRCA1_ex2-ex2
mlpa_9	del_BRCA1_ex19-ex23	del_BRCA1_ex19-ex23
mlpa_10	del_BRCA1_ex19-ex23	del_BRCA1_ex20-ex22
mlpa_11	del_BRCA1_up-ex2	del_BRCA1_ex2-ex2
mlpa_12	del_BRCA1_ex20-ex23	del_BRCA1_ex20-ex23

). The coverage values are provided in Supplementary Table S1, and the ratio plots are shown in Supplementary Figures S1–S12. An example of BRACNAC output plot for a positive sample is shown in Figure 2.

For BRACNAC, several sets of parameters were tested (Figure 3). CNVs with a match between mutation type and gene (BRCA1 or BRCA2) were considered true positive, with the remaining cases deemed to be false positive. Therefore, four (if BRCA2 was also tested with MLPA) or two comparisons were performed for each patient. Unfortunately, the low number of positive cases prevented us from performing fair tests (subdividing the samples into training and test groups) and ROC analysis for deletions and amplifications, separately. However, even with no optimization of the BRACNAC parameters, its performance yielded an area under the curve (AUC) of more than 94% (Figure 3), with a sensitivity of 92–100% and a specificity of 94%.

Panelcn.MOPS testing of the resulting NGS data identified all the samples with CNVs. However, there were a lot of false-positive exon deletions/duplications for the true-positive samples and 178 false-positive samples, yielding a sensitivity of 100% and a specificity of 11%. The percentage of complete matches for the CNV-positive samples was 58%. Provided that the clinical values were based on the number of correctly determined copies of exons, the sensitivity and specificity were 64 and 84%, respectively. The accuracy of CNV detection did not correlate with the NGS assay used (Qiagen or in-house assay).

The panelcn.MOPS data included only three BRCA1/2 CNV-positive and 96 CNV-negative samples. BRACNAC and panelcn.MOPS identified all positive samples correctly. BRACNAC detected six false-positive CNVs but only one with p-value = 0.006, which was observed for all true-positive samples. Thus, the AUC for BRACNAC and panelcn.MOPS was 99.9% and 100%, respectively.

To confirm that BRACNAC can also be used for other targeted NGS panels and NGS platforms, we applied it to call CNVs from the AmpliSeq BRCA1 and BRCA2 NGS Panel and the Ion Proton platform data. The procedure involved 192 leukocyte DNA samples. The BRACNAC excluded eight samples due to a low median coverage (less than 100). We detected seven BRCA1 exon deletions and two exon amplifications, respectively, with a p-value of ≤0.001 (

Table 3. CNVs detected in the BRCA1/2 NGS data obtained by Solodskikh et al. [9]. The sample ID corresponds to the NCBI sequence read archive (SRA) accession number. LRs confirmed during the manual figure analysis are in bold.

Sample ID	Age	Short Pathogenic Variants	CNV	p-Value
SRR7910157	33	No	del_BRCA1_ex2-ex22	0.001
SRR7910176	49	No	del_BRCA2_ex2-ex3	0.001
SRR7910204	59	No	dupl_BRCA2_ex3-ex6	0.001
SRR7910262	62	No	dupl_BRCA1_ex21-ex23	0.001
SRR7910265	62	No	del_BRCA1_ex2-ex2	0.001
SRR7910283	35	No	del_BRCA1_ex23-ex23	0.001

and Supplementary Figures S13–S18). During manual figure analysis, at least two CNVs identified (BRCA1 ex2-ex22 and BRCA1 ex23-ex23 deletions) could be considered true-positive (see Supplementary Figures S13–S18). These two patients were also diagnosed at early ages (33 and 35). Other cases should be confirmed by an alternative assay.

2.3. BRACNAC for WES

To confirm that BRACNAC can also be used for WES NGS data, we used it to call CNVs from the available data, although they were poorly described. The database included 60 FFPE DNA samples containing germline BRCA1/2 mutations (

Table 4. CNVs/CNAs detected in the PRJNA388048 WES NGS data from FFPE DNA samples. The sample ID corresponds to the NCBI SRA accession number. Likely true-positive LRs are highlighted in bold.

Sample ID	Age	Cancer	Short Pathogenic Variants	CNV	p-Value
SRR5604273	55	Ovary	No	del_BRCA1_ex13-ex13	0.018
SRR5604275	33	Breast	BRCA2 c.271_271delTA	del_BRCA1_ex2-ex23	0.001
SRR5604279	58	Breast	BRCA1 c.5266dupC	del_BRCA1_ex17-ex21	0.018
SRR5604281	43	Breast	BRCA2 c.5946delT	dupl_BRCA1_ex13-ex23	0.001
SRR5604292	35	Breast	BRCA2 c.8364G>A	dupl_BRCA1_ex2-ex23	0.001
SRR5604295	39	Breast	No	del_BRCA1_ex13-ex19	0.001
SRR5604298	57	Ovary	BRCA1 c.68_69delAG	del_BRCA1_ex11-ex12	0.001
SRR5604299	49	Breast	BRCA2 c.5645C>A	del_BRCA1_ex2-ex23	0.018
SRR5604308	43	Breast	BRCA2 c.3922G>T	dupl_BRCA1_ex17-ex22	0.001
SRR5604312	34	Breast	BRCA1 c.68_69delAG	del_BRCA2_ex15-ex18	0.001
SRR5604313	50	Breast	BRCA2 c.1054dupT	dupl_BRCA1_ex15-ex16	0.001
SRR5604314	40	Ovary	BRCA1 c.3155delA	dupl_BRCA1_ex13-ex17	0.001
				dupl_BRCA1_ex19-ex23	0.001
SRR5604315	40	Ovary	BRCA1 c.3155delA	del_BRCA1_ex12-ex13	0.001

). Large deletions and amplifications were found for eight and five patients, respectively. Based on the BRACNAC plots (Supplementary Figures S19–S31), we can suggest that the SRR5604273 and SRR5604295 samples contained germline CNVs, and the others only somatic CNAs.

For the SRR5604279 sample, a CNV was identified as a deletion of BRCA1 exons 17–19. However, we suggest the complete BRCA1 deletion for this sample to be due to a possible low tumor cell percent. For the SRR5604275 and SRR5604299 samples, deletion of the whole BRCA1 was identified.

2.4. BRACNAC Limitations

To identify the limitations of BRACNAC, we applied it to the in-house data of patients for which MLPA analysis had been performed. We tested three types of limits: (1) a minimal number of samples per NGS run; (2) a maximally acceptable percentage of BRCA1/2 CNV-positive samples per random set tested (with at least one CNV-positive sample per dataset); (3) the opportunity to use several NGS run data for the same library preparation kit. First, we tested BRACNAC on 100 random sets of samples with up to 70 patients from one (Figure 4a) or several (Figure 4b) NGS runs. At least six samples were necessary to obtain an AUC higher than 80%. Furthermore, using several runs of NGS data considerably decreased the CNV detection efficacy, especially for the sets with more than 60 samples. To determine which maximal percentage of CNV-positive samples per NGS dataset was acceptable for accurate CNV detection, we tested BRACNAC on 100 random sets of 100 samples with a share of CNV-positive samples from 0.1 to 0.9 (Figure 4c). We combined the NGS data of several NGS runs due to the low number of CNV-positive samples (12) that led to low AUCs. However, we identified a rapid decline in the AUC value for sets with more than 40% of CNV-positive samples.

3. Discussion

This work described a new BRCA1/2 CNV/CNA detection tool and compared it with the freely available program panelcn.MOPS [6] and the gold standard MLPA. We obtained high sensitivity (100%) and specificity (94%) values. In many cases, MLPA confirmation and clarification of rearrangement boundaries was necessary, as in other studies [5]. We believe that even this two-step CNV detection (NGS + MLPA) could significantly simplify the process of BRCA1/2 LR identification. Compared with panelcn.MOPS, we observed many false-positive CNVs and CNVs with boundaries incorrectly determined by panelcn.MOPS when using the in-house amplicon-based targeted NGS data. These results were likely due to the fact that the normalized copy numbers for each amplified region were not taken into account when evaluating the probability of whole exon deletion or duplication performed with BRACNAC. In contrast, panelcn.MOPS showed a slightly better performance for the hybridization-based targeted NGS data.

We also demonstrated that BRACNAC could be applied to WES data. However, we could not compare our method with any alternative assay, and the coverage depth required can be higher than the usual one. Another limitation of using BRACNAC for WES data is the absence of non-coding exon coverage. A high frequency of BRCA1 exons 1–2 deletion was shown in our study (25% of all CNVs confirmed with MLPA) and other investigations [10]. Thus, the inclusion of exons 1–2 and a promoter region may improve the detection of such CNVs. Another improvement for in-house and commercial NGS assays could be using additional amplicons for short exons and flanking introns covered by only one amplicon, potentially increasing the accuracy of such exon CNV detection.

One of the limitations of BRACNAC is the inability to detect automatically the partial CNVs of exons with only some part of the exon deleted or amplified. However, the operator could suggest some CNVs based on the BRACNAC ratio plots. Other limitations discovered in this study can be extended to similar CNV detection tools, such as panelcn.MOPS. We found that it is inadvisable to combine the data from multiple runs, as it could lead to a decreased accuracy of the detection of large rearrangements. Furthermore, we demonstrated that the minimally acceptable number of samples per single NGS run is 20 samples with at least 80% of CNV-negative samples. This percentage is significantly lower than the expected CNV occurrence among breast and ovarian cancer patients in different populations, which varies from 2 to 10% depending on the race of the patients and the sample size [11,12]. This result allows one to use the NGS data of sample cohorts that were not selected for CNV presence in the genome.

Although the sample cohort used for evaluating BRACNAC is one of the largest described in the literature (e.g., panelcn.MOPS was evaluated on 180 samples) [6,13], we could not estimate a fair frequency of CNVs in our cohort due to the non-random selection of samples to confirm the CNV status by MLPA. We plan to perform such an evaluation in the future.

Another promising direction for further research is the comparison of existing programs for identifying somatic CNAs. However, the absence of a gold standard complicates such a study. For example, MLPA is highly sensitive to tissue fixation conditions [14] as well as ddPCR, but this sensitivity is less pronounced for the latter due to the absence of the ligation step. Therefore, ddPCR is a potential method of choice that could be applied to blood and FFPE DNA samples [15,16].

4. Materials and Methods

4.1. Algorithm

BRACNAC is an open-source and freely available Python tool that includes several parts for effectively calling CNVs and CNAs in the BRCA1 and BRCA2 genes (Figure 5). It uses the target region coordinate file and TSV table with samples’ coverage depth values for the target regions. The coverage values for each target region can be mean, median, minimal, or maximal coverage values per all nucleotides of the region, but they should be uniform across all the samples analyzed. In the case of amplicon-based targeted NGS panels, such regions can be represented by the genomic coordinates of the amplified regions. From the input table, BRACNAC creates a NumPy array that is normalized by the following types: (1) the median coverage values of one sample for the target regions; (2) the median normalized coverage value for each target region among all samples; (3) the median normalized coverage value for each primer pool (if it is known or applicable, e.g., for an amplicon-based targeted NGS panel); (4) the median normalized coverage for each sample cluster (optional, applicable, e.g., when some sample libraries are prepared separately from the rest). To obtain target region copy numbers, BRACNAC multiplies all values by 2.

After the normalization steps, BRACNAC searches for exons potentially deleted and/or amplified: (1) it combines the values less than 2.0 and greater than 2.0 to two distinct sets; (2) it joins the neighboring value ranges if the region between them has low coverage or has a value less than 2.0 (for deletions) or greater than 2.0 (for amplifications); (3) it filters out the value ranges where all the exons are affected only partially (this filtration is performed because almost all BRCA1/2 LRs are associated with Alu-repeats in their introns); (4) it filters out the deletions for which the number of normalized values of at least of 1.3 (default value, but can be changed by the “del1” parameter) is less than one; (5) it filters out the deletions for which the number of values higher than 1.7 (“del2” parameter) is greater than one; (6) it filters out the deletions for which the number of values of no more than 1.7 (“del2” parameter) is less than half of the length of the region; (7) it filters out the amplifications for which the number of normalized values of at least 2.7 (default value which can be changed by the “dupl1” parameter) is less than one; (8) it filters out the amplifications for which the number of values less than 2.4 (“dupl2” parameter) is greater than one; (9) it filters out the amplifications for which the number of values of at least 2.4 (“dupl2” parameter) is less than half of the length of the region; (10) for the left deletions and amplifications, it calculates the score and p-values and filters them out by these values.

• The deletion score is calculated as follows:

$${score}_{del}=\frac{k_1\times {\sum }_{i=1}^N\left(4-a_i\right)-4\times k_2-4\times k_3}{{1.3}^{(\frac{D}{L}+k_4\times 2+\frac{1}{E}+Mdist)}}$$

(1)

where k₁ is 1 if all considered exons are affected completely or 0.5 if any exon is involved into CNVs/CNAs only partially; a_i indicates normalized target region coverage values that are no more than “del2”; N is the number of such values; k₂ is 1 if the first target region value is no greater than “del2”—otherwise it is 0.5; k₃ is 1 if the last target region value is no greater than ”del2”—otherwise it is 0.5; D is the number of target region values which are greater than ”del1”; L is the number of target region values that are considered as potential deletions; k₄ has a value of 1 if the exon deletion in question is covered by only one target region (except for the second exon, which is frequently deleted in BRCA1 with promoter regions)—otherwise, k₄ has a value of 0; E is the number of exons affected; Mdist is the median for the values of the differences between neighboring normalized coverage values.

• The amplification score is calculated as follows:

$${score}_{amp}=\frac{k_1\times {\sum }_{i=1}^N{a}_i-4\times k_2-4\times k_3}{{1.3}^{(\frac{I}{L}+k_4\times 2+\frac{1}{E}+Mdist)}}$$

(2)

where k₁ is 1 if all considered exons are affected completely, or 0.5 if any exon is affected only partially; a_i indicates normalized target region coverage values that are at least “dupl2”; N is the number of such values; k₂ is 1 if the first target region value is at least “dupl2”—otherwise, it is 0.5; k₃ is 1 if the last target region value is at least “dupl2”—otherwise, it is 0.5; I is the number of target region values less than “dupl1”; L is the number of target region values that are considered for the amplification; k₄ has a value of 1 if the exon amplification in question is covered by only one target region, otherwise k₄ has a value of 0; E is the number of exons affected; Mdist is the median for the values of the differences between neighboring normalized coverage values.

For LRs with a score higher than the threshold (by default, 9.9 and 2 for the first and second p-value evaluation steps, respectively), the p-value is calculated by the following bootstrap analysis. When using only the normalized data of samples and target regions covered enough, BRACNAC shuffles the data array randomly by column for each sample and then by rows. The shuffle number of the sample columns is equal to 1000 (by default and can be changed), the shuffle number of the LR target regions (by rows) is equal to the number of samples without potential LRs from the first step (when we use hard CNV and CNA filters, with a minimal score of 9.9 and a maximal p-value of 0.01). After each shuffling step, BRACNAC calculates the score similarly as described above but normalizes it only by the length:

$$score=\frac{{\sum }_{i=1}^N{a}_i}{{1.3}^{\frac{M}{L}}}$$

(3)

where a_i is a normalized value (if we consider amplifications) or 4.0 minus the normalized value (if we consider deletions); M is the total number of normalized values shuffled more than “dupl1” (for amplifications) or less than “del1” (for deletions); L is the number of target regions affected by the LR considered. Such a two-step p-value evaluation helps us avoid the influence of obviously positive samples on the subsequently calculated p-value.

BRACNAC can be applied in command line or graphical user interface (GUI) versions and uses the following Python modules: argparse (to read input arguments), NumPy (for arrays) [17], xlsxwriter (to write outputs into XLS tables), and scipy (for statistics) [18]. The BRACNAC tool is freely available at https://github.com/aakechin/bracnac/ (accessed on 18 November 2023).

4.2. Datasets

To compare the BRACNAC performance with those of panelcn.MOPS [6] and MLPA, we used BRCA1/2-targeted NGS data previously obtained from 211 ovarian cancer and 2 prostate cancer patient leukocyte DNA samples with an in-house (nine runs) panel and the GeneRead BRCA panel v2 (Qiagen, one run), covering whole-coding regions of the BRCA1 and BRCA2 genes [19]. Informed consent was obtained from all patients who participated.

Ten runs were performed using the MiSeq and MiniSeq platforms (Illumina) involving 730 DNA samples from leukocytes and FFPE, from which 213 samples were also selected for MLPA. For these samples, MLPA was performed with the BRCA1 MRC Holland assay (lot P002-BRCA1-D1-1114) following the manufacturer’s instructions. For 34 of these samples, the BRCA2 gene (lot P045-BRCA2/CHEK2-B3-0714) was also analyzed. Two or four results were compared for each patient to perform ROC analysis, i.e., BRCA1 and/or BRCA2 deletion and/or amplification. For each result compared, the match was considered an event with the same CNV type (deletion or amplification) and target (BRCA1 or BRCA2).

BRACNAC was also tested on targeted NGS of other research groups (NCBI SRA Project PRJNA493651; European Genome-Phenome Archive EGAD00001003400, which was used for panelcn.MOPS) [6,20] and WES (PRJNA388048) data. The first dataset was obtained while discovering germline variants in 192 breast cancer patients using the AmpliSeq BRCA1 and BRCA2 panel (Thermo Fisher Scientific) and the Ion Proton platform. The second one was used for testing the panelcn.MOPS program and includes 99 samples tested with the BRCA1/2 MLPA assay and sequenced with the TruSight^TM Cancer (TSC) panel (Illumina). The WES dataset were generated for 60 FFPE DNA samples containing germline BRCA1/2 mutations, from breast and ovarian cancer patients. However, there was no detailed information about the library preparation protocol or the NGS platform used. To identify short and point mutations, we applied a BRCA analyzer [19]. The WGS data were not used because we could not find such data for enough samples in one NGS run.

4.3. Statistics

All plots and statistical tests were performed with Python modules scipy [18], scikit-learn [20], and matplotlib [21]. For ROC analysis, “roc_auc_score” and “roc_curve” functions of the scikit-learn Python module were used.

5. Conclusions

In this study we presented a newly developed bioinformatics tool for detecting CNVs and CNAs in the BRCA1 and BRCA2 genes using in NGS data. This tool is specifically tailored for data obtained with various library preparation kits and sequencing platforms. Our findings also highlighted the general limitations of such tools, including the minimal number of samples per NGS run and the minimal percent of CNV-/CNA-negative samples. We hope our results will be valuable for researchers and clinicians working in cancer genetics and will contribute to improving the accuracy and sensitivity of CNV and CNA detection in NGS data.