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Genetic variants of prospectively demonstrated phenocopies in BRCA1/2 kindreds



In kindreds carrying path_BRCA1/2 variants, some women in these families will develop cancer despite testing negative for the family’s pathogenic variant. These families may have additional genetic variants, which not only may increase the susceptibility of the families’ path_BRCA1/2, but also be capable of causing cancer in the absence of the path_BRCA1/2 variants. We aimed to identify novel genetic variants in prospectively detected breast cancer (BC) or gynecological cancer cases tested negative for their families’ pathogenic BRCA1/2 variant (path_BRCA1 or path_BRCA2).


Women with BC or gynecological cancer who had tested negative for path_BRCA1 or path_BRCA2 variants were included. Forty-four cancer susceptibility genes were screened for genetic variation through a targeted amplicon-based sequencing assay. Protein- and RNA splicing-dedicated in silico analyses were performed for all variants of unknown significance (VUS). Variants predicted as the ones most likely affecting pre-mRNA splicing were experimentally analyzed in a minigene assay.


We identified 48 women who were tested negative for their family’s path_BRCA1 (n = 13) or path_BRCA2 (n = 35) variants. Pathogenic variants in the ATM, BRCA2, MSH6 and MUTYH genes were found in 10% (5/48) of the cases, of whom 15% (2/13) were from path_BRCA1 and 9% (3/35) from path_BRCA2 families. Out of the 26 unique VUS, 3 (12%) were predicted to affect RNA splicing (APC c.721G > A, MAP3K1 c.764A > G and MSH2 c.815C > T). However, by using a minigene, assay we here show that APC c.721G > A does not cause a splicing defect, similarly to what has been recently reported for the MAP3K1 c.764A > G. The MSH2 c.815C > T was previously described as causing partial exon skipping and it was identified in this work together with the path_BRCA2 c.9382C > T (p.R3128X).


All women in breast or breast/ovarian cancer kindreds would benefit from being offered genetic testing irrespective of which causative genetic variants have been demonstrated in their relatives.

Peer Review reports


Breast cancer (BC) is one of the most common human malignancies, accounting for 22% of all cancers in women worldwide [1]. A significant proportion of BC cases can be explained by hereditary predisposition and approximately 30% of this hereditary cancer risk is explained by the currently known high-penetrance susceptibility genes [2,3,4,5]. Notably, carriers of pathogenic BRCA1 or BRCA2 variants (path_BRCA1 or path_BRCA2) have an increased risk of developing BC (average lifetime risk of 35–85%) and ovarian cancer (average lifetime risk 11–39%). Further, carriers of pathogenic variants of ATM, CHEK2, PALB2, NBS1 and RAD50 have been found to confer two- to five-fold increased risk for developing BC [1, 6]. It is also known that pathogenic variants in TP53, PTEN, STK11 and CDH1, resulting in Li-Fraumeni syndrome, Cowden syndrome, Peutz–Jeghers syndrome and hereditary diffuse gastric cancer, respectively, are associated with a high lifetime risk (> 40%) of BC. Moreover, pathogenic variants in RAD51 paralogs, i.e., RAD51C, confer an increased risk of ovarian cancer [7]. The frequency of pathogenic variants in BC-associated genes varies significantly among different populations, as exemplified by the frequently studied founder pathogenic variant c.1100delC in CHEK2 [6].

The identification of path_BRCA1 or path_BRCA2 in an affected BC individual enables access to evidence-based screening for family members, and thus facilitates the implementation of appropriate cancer prevention in these families [1, 5, 6]. However, some women in families with an identified pathogenic variant will develop cancer despite testing negative for the family’s pathogenic variant, often denoted as phenocopies [8]. In BC kindreds having a demonstrated path_BRCA2 variant, the number of phenocopies is reportedly more frequent than expected by chance [8,9,10]. It has been proposed that these families may have additional genetic variants, which not only may increase the susceptibility of the families’ path_BRCA1/2, but also be capable of causing cancer in the absence of the path_BRCA1/2 demonstrated in the families [5,6,7].

The current practice of genetic counselling for women who do not carry the path_BRCA1/2 variants of their relatives is challenging since their recognition is crucial for application of proper diagnostic and therapeutic approaches in these families. To discover additional inherited disease-causing variants in path_BRCA1/2 kindreds, we examined all prospectively detected BC or gynecological cancer cases in these kindreds by next-generation sequencing (NGS) using a panel of 44 cancer susceptibility genes. All detected variants were analyzed by RNA splicing- and protein-dedicated in silico methods. Variants predicted as the most likely to affect splicing were experimentally analyzed by using a cell-based minigene splicing assay.


Study population

For more than 20 years, we (the Hereditary Cancer Biobank from the Norwegian Radium Hospital, Norway; and the Department of Genomic Medicine from the University of Manchester, United Kingdom) have ascertained BC and breast/ovarian cancer kindreds by family history. The sisters and daughters of cancer patients were initially subjected to follow-up by annual mammography and gynecological examinations as appropriate at that time, and later they were all subjected to genetic testing [11].

Both collaborating outpatient genetic centers identified 48 women with prospective detected BC or gynecological cancer at follow-up, who were tested negative for their respective families’ path_BRCA1/2 variants. Clinical data were obtained from pathology reports and clinical files.

Ethical approval for the prospective study was granted from the Norwegian Data Inspectorate and Ethical Review Board (ref 2015/2382). All examined patients had signed an informed consent for their participation in the study.

Targeted sequencing

Genomic DNA was isolated from peripheral blood samples and targeted sequencing was carried out using a TrueSeq amplicon based assay v.1.5 on a MiSeq apparatus, as previously described [12]. The 44-gene panel used in this work includes genes associated with cancer predisposition as described in a prior study [12].

Sequencing data analysis

Paired-end sequence reads were aligned to the human reference genome (build GRCh37) using the BWA-mem algorithm (v.0.7.8-r55) [13]. The initial sequence alignments were converted to BAM format and subsequently sorted and indexed with SAMtools (v.1.1) [13]. Genotyping of single nucleotide variants (SNV) and short indels was performed by GATK’s HaplotypeCaller. Filtering of raw genotype calls and assessment of callable regions/loci were done according to GATK’s best practice procedures, as described more detail previously [12].

Variants were annotated using ANNOVAR (version November 2015) [14] and were queried against a range of variant databases and protein resources (v29, December 2015), as previously described [12].

Validation by cycling temperature capillary electrophoresis

The pathogenic variants identified in this study were validated by cycling temperature capillary electrophoresis. The method is based on allele separation by cooperative melting equilibrium while cycling the temperature surrounding capillaries [15]. This approach has previously been described and extensively used to detect somatic mutations and single nucleotide polymorphisms (SNPs) [16,17,18,19]. The amplicon design was performed by the variant melting profile tool ( [20]. Primer sequences, PCR reaction conditions and electrophoresis settings are described in Additional file 1.

Genetic variants nomenclature and classification

The nomenclature guidelines of the Human Genome Variation Society (HGVS) were used to describe the detected genetic variants [21]. The recurrence of the identified variants was established by interrogating six databases (in their latest releases as of November 2016): Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA), Breast Cancer Information Core Database (BIC), the International Society of Gastrointestinal Hereditary Tumors (InSiGHT) Database, the Leiden Open Variation Database (LOVD), ClinVar, and the Human Gene Mutation Database (HGMD).

Novel variants were considered pathogenic if either one of the following criteria was met: a) introduced a premature stop codon in the protein sequence (nonsense or frameshift); b) occurred at positions + 1/+ 2 or − 1/− 2 of donor or acceptor splice sites, respectively; and c) represented whole-exon deletions or duplications.

In silico analyses of VUS

Two types of bioinformatics methods were used to predict the impact of selected variants on RNA splicing. First, we used MaxEntScan (MES) and SSF-like (SSFL) to predict variant-induced alterations in 3′ and 5′ splice site strength, as described by Houdayer et al. [22], except that here both algorithms were interrogated by using the integrated software tool Alamut Batch version 1.5, (Interactive Biosoftware, For prediction of variant-induced impact on exonic splicing regulatory elements (ESR), we resorted to ΔtESRseq- [23], ΔHZei- [24], and SPANR-based [25] as described by Soukarieh et al. [26]. Score differences (Δ) between variant and wild-type (WT) cases were taken as proxies for assessing the probability of a splicing defect. More precisely, we considered that a variant mapping at a splice site was susceptible of negatively impacting exon inclusion if ΔMES≥15% and ΔSSFL≥5% [22], whereas an exonic variant located outside the splice sites was considered as a probable inducer of exon skipping if negative Δ scores (below the thresholds described below) were provided by all the 3 ESR-dedicated in silico tools. We chose the following thresholds: <− 0.5 for ΔtESRseq-, <− 10 for ΔHZei-, and < − 0.2 for SPANR-based scores. In addition, we evaluated the possibility of variant-induced de novo splice sites by taking into consideration local changes in MES and SSFL scores. In this case, we considered that variants located outside the splice sites were susceptible of creating a competing splice site if local MES scores were equal to or greater than those of the corresponding reference splice site for the same exon.

In silico protein impact predictions of VUS were performed with FATHMM ( (v2.3), PolyPhen2-HVAR (v 2.2.2), MutationTaster (data release Nov 2015), MutationAssessor (release 3), SIFT (Jan 2015) and PROVEAN (v1.1 Jan 2015) using dbNSFP v3.4.

Cell-based minigene splicing assays

In order to determine the impact of the APC c.721G > A on RNA splicing, we performed functional assays based on the comparative analysis of the splicing pattern of WT and mutant reporter minigenes [27], as follows. First, the genomic region containing APC exon 7 and at least 150 nucleotides of the flanking introns (c.646–169 to c.729 + 247) were amplified by PCR using patient #12470 DNA as template and primers indicated in Additional file 2. Next, the PCR-amplified fragments were inserted into a previously linearized pCAS2 vector [26] to generate the pCAS2-APC exon 7 WT and c.721G > A minigenes. All constructs were sequenced to ensure that no unwanted mutations had been introduced into the inserted fragments during PCR or cloning. Then, WT and mutant minigenes were transfected in parallel into HeLa cells grown in 12-well plates (at ~ 70% confluence) using the FuGENE 6 transfection reagent (Roche Applied Science). Twenty-four hours later, total RNA was extracted using the NucleoSpin RNA II kit (Macherey Nagel) and, the minigene transcripts were analyzed by semi-quantitative RT-PCR using the OneStep RT-PCR kit (QIAGEN), as previously described [26]. The sequences of the RT-PCR primers are shown in Additional file 2. Then, RT-PCR products were separated by electrophoresis on 2.5% agarose gel containing EtBr and visualized by exposure to UV light under saturating conditions using the Gel Doc XR image acquisition system (Bio-Rad), followed by gel-purification and Sanger sequencing for proper identification of the minigenes’ transcripts. Finally, splicing events were quantitated by performing equivalent fluorescent RT-PCR reactions followed by capillary electrophoresis on an automated sequencer (Applied Biosystems), and computational analysis by using the GeneMapper v5.0 software (Applied Biosystems).


Family history and clinical characteristics

In total, we identified 48 cases, of whom 18 BC or gynecological cancer patients who did not carry their respective families’ path_BRCA1 or path_BRCA2 variants (n = 13 and n = 5, respectively) came from the Hereditary Cancer Biobank from the Norwegian Radium Hospital, while the Department of Genomic Medicine from the University of Manchester identified a total of 30 BC patients, all non-carriers of the family’s path_BRCA2 variants (Fig. 1). The median age at first cancer diagnosis was 53.5 years (range 31–79 years). The incidence was higher for BC (92%), followed by ovarian cancer (4%) and endometrial and cervical cancer (2% each) (Table 1).

Fig. 1
figure 1

Flow chart showing the study population selection from the Hereditary Cancer Biobank from the Norwegian Radium Hospital, Norway. It contains ascertained BC and breast/ovarian cancer kindreds by family history that were all subjected to genetic testing. The identification of phenocopies involved 48 women with prospective detected BC or gynecological cancer at follow-up, who were tested negative for their respective families’ path_BRCA1/2 variants. Among these cases, 13 were identified in non-carriers of the family’s path_BRCA1 variant and in 35 non-carriers of the family’s path_BRCA2 variant (n = 30 from the Department of Genomic Medicine from the University of Manchester). Pathogenic variants were identified in 5/48 (10%) BC or gynecological cancer cases

Table 1 Summary of the 48 prospective BC or gynecological cancer patients included in the study

Germline findings

In the 48 cases, we identified five (10%) to carry pathogenic variants in ATM (c.468G > A, p.Trp156Ter and c.9139C > T, p.Arg3047Ter), BRCA2 (c.9382C > T, p.Arg3128Ter), MSH6 (c.2864delC, p.Thr955fs) and MUTYH (c.1178G > A, p.Gly393Asp). Among these five cases, 2/13 were identified in non-carriers of the family’s path_BRCA1 variant and in 3/35 non-carriers of the family’s path_BRCA2 variant (Fig. 1). Disease type, familial path_BRCA1/2 and pathogenic variants found in this study are shown in detail in Table 1.

Interestingly, one case with a familial path_BRCA2 (c.6591_6592delTG) was found to carry another pathogenic variant in the same gene (BRCA2 c.9382C > T, p.Arg3128Ter), which causes a premature stop in the codon 3128 and is known to be a high risk pathogenic variant (Table 1).

The pathogenic variants in BC-related genes (2 in ATM and 1 in BRCA2) were found in 3 women with BC or ovarian cancer, while the MSH6 and the heterozygous MUTYH p.Gly393Asp pathogenic variant was found in a woman with endometrial cancer at 57 years and BC diagnosis at 56 years, respectively (Table 1).

Validation of the cancer gene panel output

The presence of the five pathogenic variants detected by targeted NGS was confirmed by cycling temperature capillary electrophoresis, showing 100% correspondence between both methods.

Variants of unknown significance (VUS) and predicted protein alterations

In total, we found 26 unique VUS in 30 out of 48 patients (63%). Common polymorphisms (with an allele frequency ≥ 1% in the general population according to the ExAC database) and benign variants classified according to either ClinVar or the American College of Medical Genetics and Genomics (ACMG) guidelines were excluded from further analyses [41, 58].

The VUS were detected in 17 genes, namely: AXIN2, RAD51B (in 4 patients each), MAP3K1 (in 3 patients), APC, ATM, MSH2, NBN, POLE (in 2 patients each), BRCA1, CDH1, CDX2, DVL2, MRE11A, MUTYH, NOTCH3, PTEN and RAD51D (in 1 patient each) (Table 2). The minor allele frequencies (MAF) of these variants in public databases were very low or no frequency data have been reported (Table 2).

Table 2 RNA splicing- dedicated in silico analyses for the VUS identified in our study

The VUS were furthermore analyzed by using 6 in silico protein prediction tools with different underlying algorithms (Fig. 2). The MRE11A c.1139G > A and the MUTYH c.881G > A variants were suggested to have a potentially damaging effect on protein level by all six predictions programs. For the variants in the MSH2, NBN, POLE and BRCA1 genes (MSH2 c.815C > T, NBN c.283G > A, POLE c.2459 T > C and BRCA1 c.1927A > G, five out of six predictions suggested a potentially damaging effect (Fig. 2).

Fig. 2
figure 2

Protein-related in silico data obtained for the VUS identified in the study

Discrepancies in protein-related predictions were even more pronounced for the variants in APC, AXIN2, RAD51B, DVL2, RAD51D, CDH1 and MSH2 c.2164G > A. In contrast, none of the six prediction tools showed deleterious effects for the detected variants in the AXIN2, ATM, RAD51B and MAP3K1 genes (AXIN2 c.2272G > A, ATM c.2689 T > A, RAD51B c.539A > G and c.1063G > A and MAP3K1 c.764A > G) (Fig. 2).

Splicing-dedicated in silico analysis and minigene splicing assays

Out of the 26 unique VUS, two (APC c.721G > A and MAP3K1 c.764A > G) were bioinformatically predicted as the most likely to affect RNA splicing, either by potentially creating a new splice site or by altering putative exonic splicing regulatory elements, respectively (Table 2). Given that RNA data was not available for APC c.721G > A, we set out to experimentally evaluate the impact on RNA splicing produced by this variant, by performing a cell-based minigene splicing assay. As shown in Fig. 3, we observed that c.721G > A did not affect the splicing pattern of APC exon 7 in our system. These results are reminiscent of those recently obtained for MAP3K1 c.764A > G by using a similar splicing assay, in which the variant did not cause an alteration in the minigene’s splicing pattern (Dominguez-Valentin et al. under submission). It would be important in both cases to validate the minigene results by analyzing RNA from the variant carriers/patients as compared to those from healthy controls. However, we do not have such material in our biobank.

Fig. 3
figure 3

Analysis of the impact on RNA splicing of APC c.721G > A by using a cell-based minigene splicing assay. a Structure of pCAS2-APC.ex7 minigene used in the assay. The bent arrow indicates the CMV promoter, boxes represent exons, lines in between indicate introns, and arrows below the exons represent primers used in RT-PCR reactions. The WT and c.721G > A minigenes were generated by inserting a genomic fragment containing the exon of interest and flanking intronic sequences into the intron of pCAS2, as described under Materials and Methods. b Analysis of the splicing pattern of pCAS2-APC.ex7 WT and c.721G > A minigenes. The two constructs were introduced into HeLa cells and the minigenes’ transcripts were analyzed by RT-PCR 24 h post-transfection. The image shows the results of a representative experiment in which the RT-PCR products were separated on a 2.5% agarose gel stained with EtBr and visualized by exposure to ultraviolet light. M, 100 bp DNA ladder (New England Biolabs). c Quantification of splicing events observed in the minigene splicing assay. The relative levels of exon inclusion indicated under the gel are based on RT-PCR experiments equivalent to those shown in B but performed with a fluorescent forward primer and then separated on an automated sequencer under denaturing conditions. Quantification results were obtained by using the GeneMapper v5.0 software (Applied Biosystems) and correspond to the average of two independent fluorescent-RT-PCR experiments. d Representative fluorescent RT-PCR experiment. The panel shows superposed peaks corresponding to the WT and mutant products (in blue and red, respectively), as indicated

To our knowledge, the only other VUS from our list for which RNA data is available is MSH2 c.815C > T (p.Ala272Val). Previous results from different minigene assays revealed that, albeit located outside the splice sites, MSH2 c.815C > T induces partial skipping of exon 5 [28]. These results agree, at least in part, with those obtained by analyzing RNA from a LS patient carrying this same variant [29]. Indeed, the latter study revealed aberrantly spliced MSH2 transcripts associated with the presence of c.815C > T, but where the severity of the splicing defect was not addressed at the time. Of note, here we identified MSH2 c.815C > T together with another VUS (DVL2 c.596 T > C) and a path_BRCA2 c.9382C > T (different from the familial path_BRCA2) in a patient diagnosed with ductal carcinoma at 44 years of age (Patient 1,100,948) (Table 1).


Among prospectively detected BC or gynecological cancer phenocopies in the path_BRCA1/2 families, we found that 4/48 have pathogenic variants in high-penetrance cancer genes: two BC- and one CRC-associated gene (ATM, BRCA2 and MSH6, respectively). Our findings are in line with a previous study, which detected a likely pathogenic variant in a gene other than BRCA1/2 in a BC patient, i.e. MSH6 c.3848_3862del (p.(Ile1283_Tyr1287del) [30]. In addition, we found the MUTYH c.1178G > A (p.Gly393Asp) variant in a BC case, which is one of the most common path_MUTYH variants. Pathogenic MUTYH variants may cause a recessively inherited colon cancer syndrome. Whether or not individuals who are heterozygous for MUTYH mutations may be at risk for cancer is debated [31]. Among the five cases found to carry pathogenic variants, 2/13 were identified from families with path_BRCA1 and 3/35 with path_BRCA2 variants.

Our results are in concordance with the recently published NGS panel studies, which have demonstrated that besides high-risk genes, like BRCA1/2 and MMR genes, other genes may also contribute to familial cancer predisposition, thus providing a broader picture on the genetic heterogeneity of cancer syndromes [25, 32, 33]. In this regard, a molecular diagnosis yield of approximately 9% to identify a pathogenic or likely pathogenic variant in BC has been reported, and with yields of 13% in ovarian and 15% in colon/stomach cancer cases [25]. On the other hand, family history is currently used to identify high risk patients. However, the use of family history fails to identify women without close female relatives who are carriers of pathogenic variants [9].

Despite the potential of NGS to identify genetic causes among families that tested negative for pathogenic variants in high-risk genes using traditional methods [25, 32, 33], a high number of VUS are also detected and constitute a major challenge in oncogenetics [34]. In this study, we subjected 26 VUS to RNA splicing and protein in silico evaluations, and the bioinformatics predictions indicated that two VUS (APC c.721G > A and MAP3K1 c.764A > G) were likely to affect RNA splicing. Our results from minigene splicing assays suggest, however, that this is not the case. Complementary analysis of patients’ RNA will be important to verify the impact on splicing of these variants in vivo. Of note, none of the six protein in silico prediction tools showed a deleterious effect for the MAP3K1 c.764A > G missense variant and inconsistences were found for the APC c.721G > A variant.

Bioinformatics prediction tools are widely used to aid the biological and clinical interpretation of sequence variants, although it is well recognized that they have their limitations. Co-segregation studies for further evaluation will be key for understanding whether some of the VUS detected in this work may have a causal effect. Some of the VUS may in the future be reclassified as deleterious or benign, but in the meantime, they cannot be used to make clinical decisions [30].

A polygenic model involving a combination of multiple genomic risk factors, including the effect of low- or moderate- penetrance susceptibility alleles may explain the increased BC risk in women who tested negative for family’s path_BRCA1/2 variants [5]. In addition, heterozygous whole gene deletions (WGD) and intragenic microdeletions have been reported to account for a significant proportion of pathogenic variants underlying cancer predisposition syndromes, although WGD were not a common mechanism in any of the three high-risk BC genes, BRCA1, BRCA2 and TP53 [35].

The clinical utility of gene panels such as the one used in this study is not yet fully established and the appropriate routes for clinical deployment of such tests remain under discussion [36]. So far, the large patient datasets generated by NGS panels may be used to explore the specific penetrance of the genes included in these panels, and to assess the performance and implications of the use of NGS in clinical diagnostics [34].


In kindreds carrying path_BRCA1/2 variants, testing only for the already known path_BRCA1/2 variants in the family may not be sufficient to exclude increased risk neither for BC nor for ovarian cancer or other cancers in the healthy female relatives. Our findings suggest that all women in BC or breast/ovarian cancer kindreds would benefit from being offered genetic testing irrespective of which causative genetic variants have been demonstrated in their relatives. In addition, we found a number of VUS in genes other than BRCA1/2 i.e. AXIN2, APC, DVL2, MAP3K1, RAD51B, NBN, POLE, CDH1, CDX2, MRE11A, MUTYH, NOTCH3, PTEN and RAD51D. All these may be suspected of being associated with cancer in the families studied and may be considered as candidates for being included in future gene panel testing to better understand why some families present aggregation of cancer cases.



American College of Medical Genetics and Genomics


Breast cancer


Breast Cancer Information Core Database


Colorectal cancer


Evidence-based Network for the Interpretation of Germline Mutant Alleles


Exonic splicing regulatory elements


Human Gene Mutation Database


International Society of Gastrointestinal Hereditary Tumors Database


Leiden Open Variation Database


Lynch syndrome


Minor allele frequency




Next generation sequencing

path_BRCA1/2 :

Pathogenic (disease-causing) variant of the BRCA1 or the BRCA2 genes


Single nucleotide polymorphisms


Single-nucleotide variants




Variants of unknown significance


Whole gene deletions


Wild type


  1. Mavaddat N, Peock S, Frost D, Ellis S, Platte R, Fineberg E, Evans DG, Izatt L, Eeles RA, Adlard J, et al. Cancer risks for BRCA1 and BRCA2 mutation carriers: results from prospective analysis of EMBRACE. J Natl Cancer Inst. 2013;105(11):812–22.

    Article  CAS  PubMed  Google Scholar 

  2. King MC, Marks JH, Mandell JB, Grp NYBCS. Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2. Science. 2003;302(5645):643–6.

    Article  CAS  PubMed  Google Scholar 

  3. Fackenthal JD, Olopade OI. Breast cancer risk associated with BRCA1 and BRCA2 in diverse populations. Nat Rev Cancer. 2007;7(12):937–48.

    Article  CAS  PubMed  Google Scholar 

  4. Antoniou A, Pharoah PD, Narod S, Risch HA, Eyfjord JE, Hopper JL, Loman N, Olsson H, Johannsson O, Borg A, et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies. Am J Hum Genet. 2003;72(5):1117–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Obermeier K, Sachsenweger J, Friedl TW, Pospiech H, Winqvist R, Wiesmuller L. Heterozygous PALB2 c.1592delT mutation channels DNA double-strand break repair into error-prone pathways in breast cancer patients. Oncogene. 2016;35(29):3796–806.

    Article  CAS  PubMed  Google Scholar 

  6. Aloraifi F, McCartan D, McDevitt T, Green AJ, Bracken A, Geraghty J. Protein-truncating variants in moderate-risk breast cancer susceptibility genes: a meta-analysis of high-risk case-control screening studies. Cancer Genet. 2015;208(9):455–63.

    Article  CAS  PubMed  Google Scholar 

  7. Harismendy O, Schwab RB, Alakus H, Yost SE, Matsui H, Hasteh F, Wallace AM, Park HL, Madlensky L, Parker B, et al. Evaluation of ultra-deep targeted sequencing for personalized breast cancer care. Breast Cancer Res. 2013;15(6):R115.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Evans DGR, Ingham SL, Buchan I, Woodward ER, Byers H, Howell A, Maher ER, Newman WG, Lalloo F. Increased rate of Phenocopies in all age groups in BRCA1/BRCA2 mutation kindred, but increased prospective breast cancer risk is confined to BRCA2 mutation carriers. Cancer Epidem Biomar. 2013;22(12):2269–76.

    Article  CAS  Google Scholar 

  9. Moller P, Hagen AI, Apold J, Maehle L, Clark N, Fiane B, Lovslett K, Hovig E, Vabo A. Genetic epidemiology of BRCA mutations--family history detects less than 50% of the mutation carriers. Eur J Cancer. 2007;43(11):1713–7.

    Article  PubMed  Google Scholar 

  10. Moller P, Stormorken A, Holmen MM, Hagen AI, Vabo A, Maehle L. The clinical utility of genetic testing in breast cancer kindreds: a prospective study in families without a demonstrable BRCA mutation. Breast Cancer Res Treat. 2014;144(3):607–14.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Moller P, Evans G, Haites N, Vasen H, Reis MM, Anderson E, Apold J, Hodgson S, Eccles D, Olsson H, et al. Guidelines for follow-up of women at high risk for inherited breast cancer: consensus statement from the biomed 2 demonstration Programme on inherited breast cancer. Dis Markers. 1999;15(1–3):207–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Dominguez-Valentin M, Nakken S, Tubeuf H, Vodak D, Ekstrom PO, Nissen AM, Morak M, Holinski-Feder E, Martins A, Moller P, et al. Potentially pathogenic germline CHEK2 c.319+2T>A among multiple early-onset cancer families. Fam Cancer. 2017. [Epub ahead of print].

  13. Li L, Chen HC, Liu LX. Sequence alignment algorithm in similarity measurement. Int Forum Info Technol Appl Proc. 2009;1:453–456.

  14. Borras E, Pineda M, Blanco I, Jewett EM, Wang F, Teule A, Caldes T, Urioste M, Martinez-Bouzas C, Brunet J, et al. MLH1 founder mutations with moderate penetrance in Spanish lynch syndrome families. Cancer Res. 2010;70(19):7379–91.

    Article  CAS  PubMed  Google Scholar 

  15. Ekstrom PO, Warren DJ, Thilly WG. Separation principles of cycling temperature capillary electrophoresis. Electrophoresis. 2012;33(7):1162–8.

    Article  CAS  PubMed  Google Scholar 

  16. Hinselwood DC, Abrahamsen TW, Ekstrom PO. BRAF mutation detection and identification by cycling temperature capillary electrophoresis. Electrophoresis. 2005;26(13):2553–61.

    Article  CAS  PubMed  Google Scholar 

  17. Ekstrom PO, Khrapko K, Li-Sucholeiki XC, Hunter IW, Thilly WG. Analysis of mutational spectra by denaturing capillary electrophoresis. Nat Protoc. 2008;3(7):1153–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Houdayer C, Caux-Moncoutier V, Krieger S, Barrois M, Bonnet F, Bourdon V, Bronner M, Buisson M, Coulet F, Gaildrat P, et al. Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico/in vitro studies on BRCA1 and BRCA2 variants. Hum Mutat. 2012;33(8):1228–38.

    Article  CAS  PubMed  Google Scholar 

  19. Palles C, Cazier JB, Howarth KM, Domingo E, Jones AM, Broderick P, Kemp Z, Spain SL, Guarino E, Salguero I, et al. Germline mutations affecting the proofreading domains of POLE and POLD1 predispose to colorectal adenomas and carcinomas. Nat Genet. 2013;45(2):136–44.

    Article  CAS  PubMed  Google Scholar 

  20. Ekstrom PO, Nakken S, Johansen M, Hovig E. Automated amplicon design suitable for analysis of DNA variants by melting techniques. BMC Res Notes. 2015;8:667.

    Article  PubMed  PubMed Central  Google Scholar 

  21. den Dunnen JT, Antonarakis SE. Mutation nomenclature extensions and suggestions to describe complex mutations: a discussion. Hum Mutat. 2000;15(1):7–12.

    Article  CAS  PubMed  Google Scholar 

  22. Antoniou AC, Kuchenbaecker KB, Soucy P, Beesley J, Chen XQ, McGuffog L, Lee A, Barrowdale D, Healey S, Sinilnikova OM, et al. Common variants at 12p11, 12q24, 9p21, 9q31.2 and in ZNF365 are associated with breast cancer risk for BRCA1 and/or BRCA2 mutation carriers. Breast Cancer Research. 2012;14(1):1–18.

  23. Di Giacomo D, Gaildrat P, Abuli A, Abdat J, Frebourg T, Tosi M, Martins A. Functional analysis of a large set of BRCA2 exon 7 variants highlights the predictive value of hexamer scores in detecting alterations of exonic splicing regulatory elements. Hum Mutat. 2013;34(11):1547–57.

    Article  CAS  PubMed  Google Scholar 

  24. Erkelenz S, Hillebrand F, Widera M, Theiss S, Fayyaz A, Degrandi D, Pfeffer K, Schaal H. Balanced splicing at the tat-specific HIV-1 3'ss A3 is critical for HIV-1 replication. Retrovirology. 2015;12:29.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Susswein LR, Marshall ML, Nusbaum R, Vogel Postula KJ, Weissman SM, Yackowski L, Vaccari EM, Bissonnette J, Booker JK, Cremona ML, et al. Pathogenic and likely pathogenic variant prevalence among the first 10,000 patients referred for next-generation cancer panel testing. Genet Med. 2016;18(8):823–32.

    Article  CAS  PubMed  Google Scholar 

  26. Soukarieh O, Gaildrat P, Hamieh M, Drouet A, Baert-Desurmont S, Frebourg T, Tosi M, Martins A. Exonic Splicing Mutations Are More Prevalent than Currently Estimated and Can Be Predicted by Using In Silico Tools. Plos Genet. 2016;12(1):1–26.

    Article  Google Scholar 

  27. Gaildrat P, Killian A, Martins A, Tournier I, Frebourg T, Tosi M. Use of splicing reporter minigene assay to evaluate the effect on splicing of unclassified genetic variants. Methods Mol Biol. 2010;653:249–57.

    Article  CAS  PubMed  Google Scholar 

  28. Tournier I, Vezain M, Martins A, Charbonnier F, Baert-Desurmont S, Olschwang S, Wang Q, Buisine MP, Soret J, Tazi J, et al. A large fraction of unclassified variants of the mismatch repair genes MLH1 and MSH2 is associated with splicing defects. Hum Mutat. 2008;29(12):1412–24.

    Article  CAS  PubMed  Google Scholar 

  29. Sjursen W, Haukanes BI, Grindedal EM, Aarset H, Stormorken A, Engebretsen LF, Jonsrud C, Bjornevoll I, Andresen PA, Ariansen S, et al. Current clinical criteria for lynch syndrome are not sensitive enough to identify MSH6 mutation carriers. J Med Genet. 2010;47(9):579–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Pinto P, Paulo P, Santos C, Rocha P, Pinto C, Veiga I, Pinheiro M, Peixoto A, Teixeira MR. Implementation of next-generation sequencing for molecular diagnosis of hereditary breast and ovarian cancer highlights its genetic heterogeneity. Breast Cancer Res Treat. 2016;159(2):245–56.

    Article  CAS  PubMed  Google Scholar 

  31. Hegde M, Ferber M, Mao R, Samowitz W, Ganguly A. Working Group of the American College of medical G, genomics laboratory quality assurance C: ACMG technical standards and guidelines for genetic testing for inherited colorectal cancer (lynch syndrome, familial adenomatous polyposis, and MYH-associated polyposis). Genet Med. 2014;16(1):101–16.

    Article  CAS  PubMed  Google Scholar 

  32. Tung N, Lin NU, Kidd J, Allen BA, Singh N, Wenstrup RJ, Hartman AR, Winer EP, Garber JE. Frequency of Germline mutations in 25 cancer susceptibility genes in a sequential series of patients with breast cancer. J Clin Oncol. 2016;34(13):1460–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Castera L, Krieger S, Rousselin A, Legros A, Baumann JJ, Bruet O, Brault B, Fouillet R, Goardon N, Letac O, et al. Next-generation sequencing for the diagnosis of hereditary breast and ovarian cancer using genomic capture targeting multiple candidate genes. Eur J Hum Genet. 2014;22(11):1305–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kamps R, Brandao RD, Bosch BJ, Paulussen AD, Xanthoulea S, Blok MJ, Romano A. Next-Generation Sequencing in Oncology: Genetic Diagnosis, Risk Prediction and Cancer Classification. Int J Mol Sci. 2017;18(2):1–57.

    Article  Google Scholar 

  35. Smith MJ, Urquhart JE, Harkness EF, Miles EK, Bowers NL, Byers HJ, Bulman M, Gokhale C, Wallace AJ, Newman WG, et al. The contribution of whole gene deletions and large rearrangements to the mutation Spectrum in inherited tumor predisposing syndromes. Hum Mutat. 2015;

  36. Lincoln SE, Kobayashi Y, Anderson MJ, Yang S, Desmond AJ, Mills MA, Nilsen GB, Jacobs KB, Monzon FA, Kurian AW, et al. A systematic comparison of traditional and multigene panel testing for hereditary breast and ovarian cancer genes in more than 1000 patients. J Mol Diagn. 2015;17(5):533–44.

    Article  PubMed  Google Scholar 

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We thank the included families for their contribution to this study.


This work was supported by the Radium Hospital Foundation (Oslo, Norway), Helse Sør-Øst (Norway), the French Association Recherche contre le Cancer (ARC), the Groupement des Entreprises Françaises dans la Lutte contre le Cancer (Gefluc), the Association Nationale de la Recherche et de la Technologie (ANRT, CIFRE PhD fellowship to H.T.) and by the OpenHealth Institute.

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All data generated or analyzed during this study are included in the manuscript.

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Authors and Affiliations



All authors have taken part in the different steps of the study: MDV, DGRE, PM and EH designed the study, AM, HT performed in silico splicing predictions and the minigene assays, POE performed validation experiments, MM, AN and EHF performed in silico protein predictions, SN, DV performed the sequence analysis. MDV drafted the manuscript and all have read, revised and approved the manuscript.

Corresponding author

Correspondence to Mev Dominguez-Valentin.

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Ethics approval and consent to participate

Ethical approval for the prospective study was granted from the Norwegian Data Inspectorate and Ethical Review Board (ref 2015/2382). All examined patients had signed an informed consent for their participation in the study.

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Not Applicable.

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The authors declare that they have no competing interests.

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Additional files

Additional file 1:

The concentration in a 10 ml PCR was 1xThermopol Reaction Buffer with 2 mM MgS04, 0.3 μM “reverse” primers, 0.15 μM “forward” primer, 0.1 μM, 6-Carboxyfluorescein-GC clamp primer, 600 μM dNTP, 100 μg Bovine Serum Albumine (Sigma-Aldrich, Oslo, Norway) and 0.75 U Taq DNA polymerase. Plates were sealed with two strips of electrical tape (Clas Ohlson, Oslo, Norway). The temperature cycling was repeated 35 times; 94 °C for 30 s, annealing temperature held for 30 s and extension at 72 °C for 60 s (Eppendorf Mastercycler ep gradient S (Eppendorf, Hamburg, Germany)). Table S1. primers used to amplify PCR product to be analysed by cycling temperature capillary electrophoresis. (DOCX 16 kb)

Additional file 2:

Primers used in the pCAS2 minigene splicing assay. (DOCX 14 kb)

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Dominguez-Valentin, M., Evans, D.G.R., Nakken, S. et al. Genetic variants of prospectively demonstrated phenocopies in BRCA1/2 kindreds. Hered Cancer Clin Pract 16, 4 (2018).

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