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Bioinformatics and Genomics
  1. December 12, 2019: Minor Corrections: The Funding statement is incomplete. “Francesca Belardinilli is the recipient of a fellowship from Fondazione Umberto Veronesi.” should be added to the Funding statement.

    The complete funding declaration should be written as: "This work was supported by the Italian Ministry of Education, Universities and Research—Dipartimenti di Eccellenza—L. 232/2016; Associazione Italiana per la Ricerca sul Cancro (AIRC) grant IG17734, Italian Ministry of University and Research, PRIN projects, and Istituto Pasteur-Fondazione Cenci Bolognetti (to Giuseppe Giannini); Francesca Fabretti is the recipient of a fellowship of the PhD Programme in Tecnologie Biomediche in Medicina Clinica, University La Sapienza; Francesca Belardinilli is the recipient of a fellowship from Fondazione Umberto Veronesi. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

Background

Hereditary breast and ovarian cancer syndrome, caused by germline pathogenic mutations in the BRCA1 (MIM#113705) or BRCA2 (MIM#600185) genes, is characterized by an increased risk for breast, ovarian, pancreatic and other cancers (Palma et al., 2006). It has been recently estimated that the cumulative risks of breast cancer to age 80 years was 72% for BRCA1 and 69% for BRCA2 carriers (Kuchenbaecker et al., 2017). Differences in mutation type and site may at least partially impact on cancer risk definition (Rebbeck et al., 2015; Coppa et al., 2018; Rebbeck et al., 2018). BRCA1 and BRCA2 gene mutations are typically found in 25–30% of the breast cancer families subjected to genetic testing (Giannini et al., 2006; Economopoulou, Dimitriadis & Psyrri, 2015). The relatively low rate of success in finding relevant pathogenic mutations in this settings is likely due to the contribution of other moderate-to-high penetrance breast cancer susceptibility genes (i.e., PALB2, ATM, CHK2) (Economopoulou, Dimitriadis & Psyrri, 2015; Coppa et al., 2018), or to the influence of low penetrance and risk-modifying alleles (Couch et al., 2012; Ottini et al., 2013; Kuchenbaecker et al., 2014; Peterlongo et al., 2015), all of which needs to be taken into account for a more appropriate assessment of individual cancer risk. For quite some time, the use of classical qualitative PCR-based techniques incapable of detecting large genomic rearrangements (LGRs) also contributed to failures in the identification of BRCA mutation carriers. Interestingly, the prevalence of BRCA1/BRCA2 LGRs varies greatly among different populations ranging from 0 to 27% of mutation positive families in Iranian/French, Canadian, Dutch, Spanish, German, French and South Africa populations (Gad et al., 2002; Hogervorst et al., 2003; Hartmann et al., 2004; Pietschmann et al., 2005; Moisan et al., 2006; La Hoya de et al., 2006; Sluiter & Van Rensburg, 2011). Relevant differences in the frequency of BRCA1 LGRs have also been reported within the Italian population (Montagna et al., 2003; Buffone et al., 2007). In general, BRCA2 LGRs are less frequent (Woodward et al., 2005; Agata et al., 2005; Buffone et al., 2007), probably due to the lower density of Alu sequences compared to BRCA1, which are involved in the genesis of LGRs (Smith et al., 1996). Multiplex ligation-dependent probe amplification (MLPA) is the most commonly used technique for the detection of large deletions/duplications in BRCA1/2 genes.

The recent advances in sequencing technologies have increased the speed and efficiency of DNA testing and the emergence of benchtop next-generation sequencing (NGS) instruments are becoming the standard in molecular genetic diagnosis (Feliubadalo et al., 2013; Trujillano et al., 2015). NGS is capable of sensitive detection of sequence variants, but may also be used for detection of LGRs by the evaluation of Copy Number Variations (CNVs) (Tarabeux et al., 2014; Enyedi et al., 2016; Schenkel et al., 2016; Schmidt et al., 2017). The CNVs assessment is mainly performed using the sequencing read depth (RD) assessment approach, whose assumption is that the RD signal is proportional to the number of copies of chromosomal segments present in that specimen (Tan et al., 2014). The ability to detect CNVs from NGS multigene panel largely, but not uniquely, depends on the library preparation, and target enrichment approaches based on hybridization and capture seem to have better performances compared to amplicon-based methods. In general, NGS data are not routinely used for CNVs detection in clinical settings for BRCA mutation screenings, due to concerns related to library preparation protocols, normalization procedures and employed software (Feliubadalo et al., 2013; Wallace, 2016). Recently, we adopted the NGS Ion AmpliSeq™ BRCA1 and BRCA2 Panel to perform routine BRCA1/2 mutation screening on the Ion PGM platform (Nicolussi et al., 2019). Here, we aimed at establishing whether sequencing data generated by this approach could be processed by a computational algorithm to efficiently predict the presence of LGRs, based on the dosage quotient (DQ) calculation and the Ion Reporter (IR) analysis.

Methods

Patients and DNA

Families putatively affected by hereditary breast/ovarian cancer syndrome were recruited at the Hereditary Tumors section of Policlinico Umberto I, University La Sapienza, between July 2015 and September 2017 and selected as previously described (Capalbo et al., 2006b; Capalbo et al., 2006a; Coppa et al., 2014). Comprehensive pre-test counseling was offered to all probands and their family members and informed consent was obtained. For each study participant, samples of blood or DNA from peripheral blood leukocytes were collected. DNA from blood samples was extracted and quantified as described by Nicolussi et al. (2019). All investigations were approved by Ethics Committee of the University of Roma “La Sapienza” (Prot.: 88/18; RIF.CE:4903, 31-01-2018) and conducted according to the principles outlined in the declaration of Helsinki.

A retrospective group of 3 DNA samples, previously found positive for BRCA1 LGRs by MLPA was used as a training set (TS). LGRs in the TS were as follows: sample BR59, BRCA1 exon 23–24 deletion (c.5407-?*(1_?)del); sample BR328, BRCA1 exon 18-19 deletion (c.5075-?_5193+?del)(Buffone et al., 2007) and sample BR409, NBR2 exon1 and BRCA1 exon 1-2 deletion (NBR2del EX1_BRCA1 delEX1-2) (Coppa et al., 2018) (Table 1).

Table 1:
LGRs in TS and VS.
Sample Id Genomic variant Exon deletion Ref
TS BR59 c.5407-?_*(1_?)del exon 23-24 del Buffone et al. (2007)
BR328 c.5075-?_5193+?del exon 18-19 del Buffone et al. (2007)
BR409 NBR2delEX1_BRCA1delEX1-2 exon 1 NBR2 del Coppa et al. (2018)
exon 1-2 BRCA1 del
VS BR963 NG_005905.2: g.163181_169408del6228 exon 21-22 del /
BR1154 NG_005905.2: g.160396_164568del4173 exon 20 del /
BR1379 NG_005905.2:g.145185_151339del6155 exon 16-17 del /
DOI: 10.7717/peerj.7972/table-1

For NGS-based LGR analysis, a consecutive group of 127 NGS/MPLA negative samples have been used to create a baseline and a prospective consecutive cohort of 85 uncharacterized probands, validation set (VS), was studied.

Ion torrent PGM sequencing

The target regions in the BRCA1 and BRCA2 genes were amplified using the Ion AmpliSeq™ BRCA1 and BRCA2 Panel (Life Technologies) according to the manufacturers’ procedures and processed as previously described (Belardinilli et al., 2015; Nicolussi et al., 2019).

Sanger sequencing

All clinical samples were sequenced for the entire coding regions by Sanger sequencing, using an ABI PRISM DyeDeoxy Terminator Cycle Sequencing Kit and an ABI 3100 Genetic Analyzer (Applied Biosystems, Warrington, UK). Reference sequence for BRCA1 was Genbank, NM_007294.3, and reference sequence for BRCA2 was Genebank, NM_000059.3.

MLPA analyses

MLPA methodology (Schouten et al., 2002) was performed, according to the manufacturer’s instructions (MRC–Holland, Amsterdam, the Netherlands), to identify BRCA1/2 genomic rearrangements. For the statistical analysis we transferred the size and the peak areas of each sample to an Excel file. The peak areas of the expected MLPA products were evaluated by comparison with a normal control and by cumulative comparison of all samples within the same experiment (Buffone et al., 2007; Coppa et al., 2018).

NGS-based LGRs analysis

LGRs in BRCA1 gene were studied by two distinct approaches: the manual calculation of the DQ and the IR platform. In the manual approach, DQ for each sample was calculated as follows: amplicon read count normalized on the BRCA1 and BRCA2 total reads/average of normalized amplicon read counts obtained from all samples. Specifically, we referred to DQA when amplicon counts were normalized vs. the coverage data of all samples run on the same single chip, and to DQB when amplicon counts were normalized vs. coverage data obtained from a baseline built from 127 LGRs negative samples. In addition, DQB has been alternatively obtained either considering together all amplicons of the Ion AmpliSeq™ BRCA1 and BRCA2 Panel (DQB1) or by separately considering the three different pools of amplicons (DQB2). DQ value higher than mean plus two standard deviations (SD) was considered indicative of a duplication; DQ value lower than mean minus 2 SD was considered indicative of a deletion. Particular attention has been also payed to reduction of multiple consecutive amplicons, even when they failed to trespass the above defined thresholds.

In the IR approach, we create a user-defined CNV detection workflow by a tunable Ion Reporter™ Software algorithm based on Hidden Markov Model (HMM), that utilize normalized read coverage across amplicons to predict the copy number or ploidy (https://assets.thermofisher.com/TFS-Assets/LSG/brochures/CNV-Detection-by-Ion.pdf). The data coverage of 20 mutation-negative patients has been used as CNV baseline to analyze the samples of both TS and VS. We detected no BRCA2 LGR in both the TS and VS. Thus, our analysis is necessarily limited to BRCA1 LGRs.

DNA breakpoint analysis

Newly discovered BRCA1 large deletions were validated by characterization of the genomic breakpoints. Long-range PCR was performed according to the manufacturer’s instructions using the kit Platinum Taq DNA polymerases High Fidelity (Thermo Fisher) with the primers sitting on closer undeleted exons as described in Table S2. PCR products were purified with ExoSAP-IT (USB Corp., Cleveland, USA) according to the manufacturer’s instructions and sequenced using the ABI PRISM DyeDeoxy Terminator Cycle Sequencing Kit and an ABI 3100 Genetic Analyzer (Applied Biosystems, Warrington, UK). Reference sequences for BRCA1 and BRCA2 are in GenBank; NM_007294.3 and NM_000059.3, respectively.

Statistical analysis

Validation metrics were defined as: Accuracy = (TP + TN)/(TP + FP + TN + FN); Sensitivity = TP/(TP + FN); Specificity = TN/(TN + FP); Negative Predictive Value = TN/(TN + FN), where TP = true positives, TN = true negatives, FP = false positives, FN = false negatives. The confidence intervals (CIs) were calculated by the method of Wilson (1927).

DQ analyses for TS samples.

Figure 1: DQ analyses for TS samples.

(A) For each sample, every peak represents the ratio of the amplicon read count normalized on BRCA1/BRCA2 total reads and the average of normalized amplicon read counts from all samples on a single chip (DQA). (B) For each sample, every peak represents the ratio of the amplicon read count normalized on BRCA1/BRCA2 total reads and the average of the coverage data of a baseline built from 127 LGRs negative samples (DQB1). The threshold = mean ± 2 SD. Value > mean ± 2 SD is indicative of a duplication; Value < mean ± 2 SD is indicative of a deletion. * indicated the amplicons included in the region involved in the rearrangement as confirmed by MLPA analysis.

Results

NGS-dependent LGR analyses

To establish whether the data obtained by NGS via Ion AmpliSeq™ BRCA1 and BRCA2 Panel were suitable to identify copy number alterations in BRCA1, we used data from three samples (TS), already characterized in our laboratory for the presence of BRCA1 LGRs by MLPA (Table 1). The sequencing data of the TS were analyzed by a locally devised algorithm for the calculation of the DQ and by our custom modified IR analysis, as described in materials and methods. The intrarun DQ calculation (DQA), which includes normalization based on the coverage data of the samples sequenced in the same chip, was always included to monitor the variability eventually due to different batches of reagents or to time-related variables. In general, however, we thought we could get improved resolution and reduced numbers of CNV false calls by normalizing the coverage data of all amplicons of each sample vs. those obtained from a reference set of 127 MLPA negative samples selected on the basis of their quality and uniformity of the coverage (DQB analysis). This baseline has been used to perform two DQB calculations, considering either all amplicons contained in the Ion AmpliSeq™ BRCA1 and BRCA2 Panel (DQB1) or dividing them into the three subsets identified by the amplification primer pools (DQB2).

As shown in Fig. 1A, the DQA plot of the TS samples revealed the presence of peaks below the thresholds, in samples BR328 and BR409 (corresponding to deletions of BRCA1 exons 18-19 and 1-2, respectively, in agreement with MLPA results). The DNA quality of BR59 sample was rather low, as evidenced by the many peaks out of the threshold. Nevertheless, the DQB1 analysis evidenced values below the threshold for 3 consecutive amplicons (AMPL223551867, 223530147 and 223954665), identifying BRCA1 exon 23-24 deletion (Fig. 1B), already discovered by MLPA analysis. Although they fail to trespass the threshold, the same consecutive amplicons showed strongly reduced values also at DQA evaluation (Fig. 1A). Hence, the careful examination of the two DQ calculations allowed us to identify all three BRCA1 LGRs in the TS. Also, the analysis performed by IR software detected the presence of CNV (CNV = 1) in the proper regions in all three TS samples (Table 2). On this basis, we extended DQA, DQB and IR analysis to a group of 85 consecutive samples (VS) negative for BRCA1/2 pathogenic variants at NGS analysis and compared it with MLPA results. Overall, DQA and DQB analysis resulted in detection of positive calls in 33/85 (39%) samples, while IR analysis detected CNVs in 29/85 (34%) (Table 3). Interestingly, DQ and IR evaluation only shared 12 positive results, with exon calls being not coincident in seven of them and with a rather precise, although imperfect, indication of the exons involved in the remaining five (Table S1). MLPA confirmed BRCA1 LGRs in 3/85 samples (Fig. 2): BR963 and BR1379, belonging to the small group of five DQ/IR double positive samples, and BR1154 resulted DQ positive-IR negative. Therefore, DQ calculation resulted 100% sensitive and displayed a 100% NPV (95% CI [87.6–99.9]) (Table 3) in our VS, values not reached by IR analysis, which failed in the identification of BR1154 (Table 2). Within DQ analysis, the correct calls were more clearly defined by the DQB2 calculation (Figs. 3A3C). The appropriateness of the deletions calls of DQ, IR and MLPA evaluations were confirmed by the molecular characterization of the breakpoints, as described below.

Table 2:
CNVs prediction by IR software algorithm in TS and VS.
The confidence score is the probability that the number of copies of the region of interest is different from 2, which is the normal value, while the precision score indicates how much the algorithm is certain of the accuracy of the number of copies estimated by the analysis.
Sample ID Locus Type Genes Location Length Copy number CytoBand CNV confidence CNV precision
TS BR59 chr17:41197602 CNV BRCA1 exon 23-24 2.138 kb 1 17q21.31 (41197602–41199740) × 1 5.66 5.66
BR328 chr17:41215277 CNV BRCA1 exon 18-19 749 kb 1 17q21.31 (41215277–41216026) × 1 13.05 13.05
BR409 chr17:41275973 CNV BRCA1 exon 2 275 kb 1 17q21.31 (41275973-41276248) × 1 1.14 1.14
VS BR963 chr17:41201074 CNV BRCA1 exon 21-22 2.18 kb 1 17q21.31 (41201074–41203254) × 1 9.14 9.14
BR1379 chr17:41215855 CNV BRCA1 exon 16-18 7.44kb 1 17q21.31 (41215855–41223295) × 1 5.11 5.11
DOI: 10.7717/peerj.7972/table-2

Characterization of LGRs

Identification of the breakpoints characterizing the LGRs is important for several reasons, including the possibility to develop diagnostic assays for segregation analyses in relatives. For different reasons DQ, IR and MLPA analyses are not able to provide such detailed molecular characterization of LGR. To define the breakpoints of the newly identified BRCA1 LGRs, PCR amplification of genomic DNA from the three samples and direct sequencing were performed.

As shown in Fig. 4A, PCR amplification of genomic DNA from the BR963 patient resulted in an aberrant fragment of approximately 1,353 bp, whose direct sequencing confirmed loss of BRCA1 exons 21 and 22, possibly originating from an erroneous homologous recombination process between an AluSq2 (Alu family, SINE class; chr17:41206762-41207066) and an AluSz (Alu family, SINE class; chr17:41200521-41200834) motifs. The rearrangement involved a perfectly repeated stretch of 24 bases and resulted in the deletion of 6228 nucleotides encompassing part of IVS20, exons 21–22 and IVS22 (Figs. 4B and 4C). The BR963 proband was affected with breast cancer at age 40 and belonged to HBC family. Segregation analysis demonstrated that the mutation came from the maternal lineage (Fig. 5A). PCR amplification of genomic DNA from BR1154 patient resulted in an aberrant fragment of approximately 872 bp (also present in her mother, sample BR1148), whose direct sequencing confirmed loss of BRCA1 exons 20, possibly originating from an erroneous homologous recombination process between an AluY (Alu family, SINE class; chr17:41205398-41205698) and an AluY (Alu family, SINE class; chr17:41205398-41205698) motifs. The rearrangement involved a perfectly repeated stretch of 11 bases and resulted in the deletion of 4173 nucleotides encompassing part of IVS19, exon 20 and IVS20 (Figs. 4D4F). The BR1154 proband was affected with ovarian cancer at age 52 and belonged to a HBOC family (Fig. 5B). The segregation analysis demonstrated that the mutation originating from the maternal lineage segregated in three individuals (Fig. 5B). Finally, PCR amplification of genomic DNA from BR1379 patient, resulted in an aberrant fragment of approximately 2,027 bp, whose direct sequencing confirmed loss of BRCA1 exons 16 and 17, possibly originated from an erroneous homologous recombination process between an AluSp (Alu family, SINE class; chr17:41224585-41224884) and an AluSg (Alu family, SINE class; chr17:41218424-41218724) motif. The rearrangement involved a perfectly repeated stretch of 16 bases and resulted in the deletion of 6155 nucleotides encompassing part of IVS15, exons 16-17 and IVS17 (Figs. 4G4I). The BR1379 proband was affected with bilateral breast cancer at age 42 and 58 and belonged to a family with colon cancer and hepatomas cases (Fig. 5C).

Table 3:
Performance of NGS-dependent LGRs analysis.
Tot MLPA Results
LGR No LGR
DQ LGR 33 3 30 64.7% accuracy (95% CI [50.6–76.7])
100% sensitivity (95% CI [22.8–98.4])
No LGR 52 0 52 63.4% specificity (95% CI [49–75.8])
100% NPV (95% CI [87.6–99.9])
IR LGR 29 2 27 67.1% accuracy (95% CI [52.9–78.7])
66.7% sensitivity (95% CI [8.9–98.8])
No LGR 56 1 55 67.1% specificity (95% CI [52.7–78.9])
98.2% NPV (95% CI [85.6–99.9])
DOI: 10.7717/peerj.7972/table-3

Notes:

Validation metrics were defined as: Accuracy = (TP + TN)/(TP + FP + TN + FN); Sensitivity = TP/(TP + FN); Specificity = TN/(TN + FP); Negative Predictive Value = TN/(TN + FN), where TP, true positives; TN, true negatives; FP, false positives; FN, false negatives.

In conclusion, our results in the VS allow us to propose an operative algorithm which uses DQ calculation and IR analysis to select samples to be subjected to MLPA analysis, as indicated in Fig. 6. Indeed, all DQ positive samples should be subjected to MLPA, while DQ and IR double positive samples, sharing calls in the same regions, could be directly subjected to second level confirmation assay or directly to breakpoint characterization. In principle, all DQ negative samples (52 sample out of 85 in our VS) could be considered negative for LGRs, thus completing the analysis at this step.

BRCA1 MLPA electropherogram showing aberrant profiles in BR963, BR1154, and BR1379 patients.

Figure 2: BRCA1 MLPA electropherogram showing aberrant profiles in BR963, BR1154, and BR1379 patients.

(A) Wild-type sample (WT). Black arrows indicate the deletion of (B) BRCA1 exons 21-22 (BR963), (C) BRCA1 exon 20 (BR1154), (D) BRCA1 exons 16-17 (BR1379).

Discussion

A complete clinical level analysis of BRCA1 and BRCA2 in hereditary breast/ovarian cancer includes the study of LGRs. Many methods have been used to identify LGRs, such as fluorescent in-situ hybridization (FISH) and microarrays (Xia et al., 2018), Southern blot, long-range PCR, quantitative multiplex PCR of short fragments (QMPSF) (Ewald et al., 2009), semiquantitative multiplex PCR, real-time PCR, restriction analysis and sequencing (Armour et al., 2002). All these methods are limited by their low throughput, time consuming, large amounts of high molecular weight DNA request and several false negative results (Ewald et al., 2009). More recently a multiplex PCR-based method that allows the determination of copy number status of multiple loci in a single assay, has been developed by Multiplicom (http://www.multiplicom.com) and described as a valid method (Concolino et al., 2014). However, the MLPA represents the most widely used approach to scan for LGRs in BRCA1/2 genes (Ruiz de Garibay et al., 2012). The simultaneous detection of mutations and copy number alterations is an attractive and useful prospect for clinical settings. In the last years the NGS-based approaches for genetic testing offered a powerful alternative for BRCA1/2 mutation detection. However, the specificity of this approach is still considered not completely satisfactory for a correct LGRs detection. One of the most relevant aspects concerns the library preparation method, with the amplicon-based approach having a lower specificity compared to target enrichment approaches (Apessos et al., 2018). Here we reported the definition of an operative algorithm to use amplicon-based Ion-PGM/Ampliseq BRCA1/BRCA2 sequencing data to efficiently predict the occurrence of BRCA1 LGRs. By comparison of the results obtained with DQ and IR analyses, we demonstrate that DQ had 100% sensitivity and 100% NPV, at variance with IR analysis, which failed in the identification of a BRCA1 exon 20 deletion. This result is consistent with one known limitation of the IR software, able to detect CNVs only if the region of interest is covered by more than one amplicon (https://assets.thermofisher.com/TFS-Assets/LSG/brochures/CNV-Detection-by-Ion.pdf). Indeed, BRCA1 exon 20, deleted in BR1154 sample, is covered by only one amplicon in the Ion AmpliSeq™ BRCA1 and BRCA2 Panel, making IR incapable of calling this CNV.

DQ analyses for the representative samples for VS.

Figure 3: DQ analyses for the representative samples for VS.

(A) and (B) for each sample, every peak represents the ratio of the amplicon read count normalized on BRCA1/BRCA2 total reads and the average of normalized amplicon read counts from all samples on a single chip (DQA). (C) for each sample, every peak represents the ratio of the amplicon read count normalized on BRCA1/BRCA2 total reads and the average of the coverage data of a baseline built from 127 LGRs negative samples considering separately the amplicon pools (DQB2, pool 2). The threshold = mean ± 2 SD. Value > mean ± 2 SD is indicative of a duplication; Value < mean ± 2 SD is indicative of a deletion. * indicated the amplicons included in the region involved in the rearrangement as confirmed by MLPA analysis.
Characterization of BRCA1 LGRs.

Figure 4: Characterization of BRCA1 LGRs.

(A) Gel image of PCR products. PCR amplification of the genomic region spanning the BRCA1 rearrangement resulted in a fragment of approximately 1,353 bp present only in the proband BR963. (B) and (C) schematic representation and electropherogram showing the BRCA1 exons 21 and 22 deletion. The variant arose from an erroneous homologous recombination process between an AluSq2 (Alu family, SINE class; chr17:41206762-41207066) and an AluSz (Alu family, SINE class; chr17:41200521-41200834) motif, and it involved a perfectly repeated stretch of 24 bp. (D) Gel image of PCR products. PCR amplification of the genomic region spanning the BRCA1 rearrangement resulted in a fragment of approximately 872 bp present in the proband BR1154 and in her mother BR1148. (E) and (F) schematic representation and electropherogram showing the BRCA1 exon 20 deletion. The variant arose from an erroneous homologous recombination process between two AluY motif at chr17:41205398-41205698 and chr17:41205398-41205698, respectively, and it involved a perfectly repeated stretch of 11 bp. (G) Gel image of PCR products. PCR amplification of the genomic region spanning the BRCA1 rearrangement resulted in a fragment of approximately 2027 bp present only in the proband BR1379. (H) and (I) schematic representation and electropherogram showing the BRCA1 exons 16 and 17 deletion. The variant arose from an erroneous homologous recombination process between an AluSp motif (Alu family, SINE class; chr17:41224585-41224884) and an AluSg (Alu family, SINE class; chr17:41218424-41218724) motif, and it involved a perfectly repeated stretch of 16 bp. MK, marker; NT, no template; CTR healthy individual DNA.
Pedigree of the HBC or HBOC family carriers of BRCA1 novel LGRs.

Figure 5: Pedigree of the HBC or HBOC family carriers of BRCA1 novel LGRs.

(A) Exons 21–22 deletion (BR963). (B) Exon 20 deletion (BR1154). (C) Exons 16–17 deletion (BR1379). Probands are indicated with an arrow. Cancer type and age at diagnosis are reported and described as: BC, breast cancer; Pan, pancreas; Leu, leukemia; Lung; bil BC, bilateral breast cancer; OC, ovarian cancer; Hep, hepatoma; CC, colon cancer.

Of course, a major caveat deals with the limited specificity and accuracy of our approach, which could not overcome the limitations also reported by other groups (Feliubadalo et al., 2013; Pilato et al., 2016). Thus, although our operative algorithm cannot fully substitute for MLPA analysis, and if our data will be confirmed in larger data sets, we suggest that combined DQ and IR analyses could be used for selecting samples to be subjected to MLPA analysis following the flow chart depicted in Fig. 4, with significant savings in time and money.

Another important contribution of this paper is the molecular characterization of the three novel BRCA1 rearrangements up to providing their unique breakpoint coordinates. Deletion of exons 21 and 22 causing damage to the C-terminal BRCT domain of the BRCA1 protein has been reported and characterized in Czech (Vasickova et al., 2007; Ticha et al., 2010) and Malay population (Hasmad et al., 2015), but with different breakpoints. BRCA1 exon 20 deletion has been described in Italian and Greek population (Montagna et al., 2003; Belogianni et al., 2004; Armaou et al., 2007) but all different from each other and from our own, with respect to their breakpoints. The BRCA1 exons 16-17 deletion, responsible of BRCA1 loss of function (Carvalho et al., 2009), has been reported in Latin America/Caribbean population, but the breakpoints were not provided by the authors (Judkins et al., 2012). Similar to many other cases (Mazoyer, 2005; Buffone et al., 2007; Ewald et al., 2009), all three novel rearrangements described here, are likely to be due to an erroneous homologous recombination event between perfectly matching Alu repeats.

Operative algorithm to select samples for MLPA analysis.

Figure 6: Operative algorithm to select samples for MLPA analysis.

Conclusion

In conclusion, here we described a simple approach that require the use of a basic statistical package such as Microsoft Excel, to predict the occurrence of LGRs by the analysis of NGS data designed for Ion AmpliSeq™ BRCA1 and BRCA2 Panel/IT-PGM platform, applicable to all NGS platforms in use to reduce the number of samples to be subjected to MLPA analysis. We also characterized for the first time the breakpoints of three novel BRCA1 LGRs.

Supplemental Information

IR/DQ common calls

In the table, 1 indicates the results of DQA analyses and 2 indicates the results of DQB analyses. In bold, the IR/DQ common calls with exon calls coincident (xlsx 13 kb).

DOI: 10.7717/peerj.7972/supp-1

Primers for LGRs breakpoints characterization

F: forward strand; R: reverse strand.

DOI: 10.7717/peerj.7972/supp-2

Raw data DQB1 analysis training set

DOI: 10.7717/peerj.7972/supp-3

Raw data DQB2 analysis Validation Set

DOI: 10.7717/peerj.7972/supp-4

Raw data NGS- LGRs analysis in the Validation Set

DOI: 10.7717/peerj.7972/supp-5

Raw data for DQA analysis of BR59

DOI: 10.7717/peerj.7972/supp-6

Raw data for DQA analysis of BR328 and BR409

DOI: 10.7717/peerj.7972/supp-7

Raw data for DQA analysis of BR1379

DOI: 10.7717/peerj.7972/supp-8

Raw data for DQA analysis for BR963 and BR1154

DOI: 10.7717/peerj.7972/supp-9