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Genetic variants of prospectively demonstrated phenocopies in BRCA1/2 kindreds

  • Mev Dominguez-Valentin1Email authorView ORCID ID profile,
  • D. Gareth R. Evans2, 3,
  • Sigve Nakken1,
  • Hélène Tubeuf4, 5,
  • Daniel Vodak1,
  • Per Olaf Ekstrøm1,
  • Anke M. Nissen6, 7,
  • Monika Morak6, 7,
  • Elke Holinski-Feder6, 7,
  • Alexandra Martins4,
  • Pål Møller1, 8, 9 and
  • Eivind Hovig1, 10, 11
Hereditary Cancer in Clinical Practice201816:4

https://doi.org/10.1186/s13053-018-0086-0

Received: 2 October 2017

Accepted: 10 January 2018

Published: 15 January 2018

Abstract

Background

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).

Methods

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.

Results

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).

Conclusion

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.

Keywords

BRCA1 BRCA2 Breast cancerGene panel testingRNA splicing

Background

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 [25]. 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 [810]. 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 [57].

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.

Methods

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) [1619]. The amplicon design was performed by the variant melting profile tool (https://hyperbrowser.uio.no/hb/?tool_id=hb_variant_melting_profiles/) [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, http://www.interactive-biosoftware.com). 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 (http://fathmm.biocompute.org.uk) (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).

Results

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).
Figure 1
Fig. 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

Patient_ID

Institution

Familial path_BRCA1 or path_BRCA2 variantFamilial path_BRCA1 or path_BRCA2 variant

ICD9 diagnosis (age)

Pathogenic variant identified in the current study

17,161

HCBNRH

BRCA2 c.5217_5223delTTTAAGT (p.Tyr1739Terfs)BRCA2 c.5217_5223delTTTAAGT (p.Tyr1739Terfs)

OC (67)

ATM c.468G > A (p.Trp156Ter)*ATM c.468G > A (p.Trp156Ter)*

6475

HCBNRH

BRCA1 c.1011dupA (p.Val340Glyfs)BRCA1 c.1011dupA (p.Val340Glyfs)

BC (52)

ATM c.9139C > T (p.Arg3047Ter)ATM c.9139C > T (p.Arg3047Ter)

13,141

HCBNRH

BRCA1 c.1072delC (p.Leu358Cysfs)BRCA1 c.1072delC (p.Leu358Cysfs)

EC (57)

MSH6 c.2864delC (p.Thr955fs)*MSH6 c.2864delC (p.Thr955fs)*

1873

HCBNRH

BRCA1 c.1556delA (p.Lys519Argfs)BRCA1 c.1556delA (p.Lys519Argfs)

MTHM (56), BC (70)

Not

5378

HCBNRH

BRCA1 c.697_698delGT (p.Val233Asnfs)BRCA1 c.697_698delGT (p.Val233Asnfs)

BC (52)

Not

5180

HCBNRH

BRCA1 c.5194-2A > CBRCA1 c.5194-2A > C

BC (39)

Not

22

HCBNRH

BRCA2 c.3847_3848delGT (p.Val1283Lysfs)BRCA2 c.3847_3848delGT (p.Val1283Lysfs)

BC (63)

Not

243

HCBNRH

BRCA2 c.3847_3848delGT (p.Val1283Lysfs)BRCA2 c.3847_3848delGT (p.Val1283Lysfs)

CVC (41)

Not

5348

HCBNRH

BRCA1 c.1556delA (p.Lys519Argfs)BRCA1 c.1556delA (p.Lys519Argfs)

BC (68)

Not

6031

HCBNRH

BRCA1 c.1556delA (p.Lys519Argfs)BRCA1 c.1556delA (p.Lys519Argfs)

BC (66)

Not

6032

HCBNRH

BRCA1 c.3228_3229delAG (p.Gly1077Alafs)BRCA1 c.3228_3229delAG (p.Gly1077Alafs)

OC (55)

Not

6207

HCBNRH

BRCA1 c.697_698delGT (p.Val233Asnfs)BRCA1 c.697_698delGT (p.Val233Asnfs)

BC (47)

Not

8085

HCBNRH

BRCA1 c.3228_3229delAG (p.Gly1077Alafs)BRCA1 c.3228_3229delAG (p.Gly1077Alafs)

BC (55), CC (66)

Not

11,717

HCBNRH

BRCA1 c.1556delA (p.Lys519Argfs)BRCA1 c.1556delA (p.Lys519Argfs)

BC(42,57)

Not

12,470

HCBNRH

BRCA1 c.3178G > T (p.Glu1060Ter)

BC (39)

Not

13,023

HCBNRH

BRCA2 c.5217_5223delTTTAAGT (p.Tyr1739Terfs)

BC (59)

Not

15,529

HCBNRH

BRCA2 c.4821_4823delTGAins

BC (48)

Not

22,325

HCBNRH

BRCA1 c.5047G > T (p.Glu1683Ter)

BC (45)

Not

1,100,948

UM

BRCA2 c.6591_6592delTG (p.Glu2198Asnfs)

BC (44)

BRCA2 c.9382C > T (p.Arg3128Ter)

12,010,643

UM

BRCA2 c.7360delA (p.Ile2454Phefs)

BC (56)

MUTYH c.1178G > A (p.Gly393Asp)

75,443

UM

BRCA2 c.5909C > A (p.Ser1970Ter)

BC (55)

Not

88,295

UM

BRCA2 c.7977-1G > C

BC (44)

Not

64,949

UM

BRCA2 c.5909C > A (p.Ser1970Ter)

BC (55)

Not

67,723

UM

BRCA2 c.4866delA p.(Arg1622Serfs*14)

BC (46)

Not

84,510

UM

BRCA2 c.5946delT (p.Ser1982Argfs)

BC (67)

Not

13,007,862

UM

BRCA2 c.5909C > A (p.Ser1970Ter)

BC (31)

Not

9,009,462

UM

BRCA2 c.6535_6536insA (p.Val2179Aspfs)

BC (67)

Not

900,178

UM

BRCA2 c.1889delC (p.Thr630Asnfs)

BC (49,77)

Not

10,005,829

UM

BRCA2 c.9541_9554del p.(Met318CysfsTer13)

BC (38)

Not

10,007,016

UM

BRCA2 c.632-1G > A

BC (51)

Not

10,003,959

UM

BRCA2 c.6275_6276delTT (p.Leu2092Profs)

BC (55)

Not

12,852

UM

BRCA2 c.1929delG (p.Arg645Glufs)

BC (56)

Not

12,001,161

UM

BRCA2 c.7958 T > C (p.Leu2653Pro)

BC (67)

Not

13,017,067

UM

BRCA2 c.755_758delACAG (p.Asp252Valfs)

BC (74)

Not

688

UM

BRCA2 c.1929delG (p.Arg645Glufs)

BC (32)

Not

40,540

UM

BRCA2 c.8535_8538delAGAG p.(Glu2846LysfsTer16)

BC (69)

Not

9,001,644

UM

BRCA2 c.4965C > G (p.Tyr1655Ter)

BC (39, 45)

Not

89,205

UM

BRCA2 c.5946delT (p.Ser1982Argfs)

BC (77)

Not

10,002,068

UM

BRCA2 del exons 14–16

BC (37)

Not

10,004,590

UM

BRCA2 c.2672dupT

BC (67,67)

Not

40,286

UM

BRCA2 c.7069_7070delCT p.(Leu2357ValfsTer2)

BC (36,53)

Not

76,618

UM

BRCA2 c.4478_4481delAAAG (p.Glu1493Valfs)

BC (51)

Not

12,015,576

UM

BRCA2 c.9382C > T (p.Arg3128Ter)

BC (45)

Not

61,420

UM

BRCA2 c.5350_5351delAA p.(Asn1784HisfsTer2)

BC (59)

Not

960,579

UM

BRCA2 c.2808_2811del4 (p.Ala938Profs)

BC (39)

Not

14,965

UM

BRCA2 c.5682C > G p.(Tyr1894Ter)

BC (59)

Not

20,468

UM

BRCA2 c.6275_6276delTT (p.Leu2092Profs)

BC (38)

Not

56,193

UM

BRCA2 c.7884dupA (p.Trp2629Metfs)

BC (79)

Not

HCBNRH Hereditary Cancer Biobank from the Norwegian Radium Hospital (Norway), UM University of Manchester (United Kingdom), ICD9 diagnosis International Classification of Diseases, 9th Revision, OC Ovary cancer, BC Breast cancer, EC Endometrial cancer, MTHM Malignant neoplasm of thymus, heart, and mediastinum, CC Colon cancer, CVC Cervical cancer, *Considered pathogenic based in its nature (nonsense and frameshift), VUS Variants of unknown significance, NM for ATM NM_000051, BRCA1 NM_007294.3, BRCA2 NM_000059.3, MSH6 NM_001281492, MUTYH NM_012222

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

Patient ID

Genomic position (GRCh37)

Gene

Exon

Nucleotide change (cNomen)

Predicted protein change (pNomen)

dbSNPrsID

Non-Finnish European population frequency*

Reference splice site-dedicated analyses

Cryptic splice site-dedicated analyses

ESR-dedicated analyses

Nearest reference

MES scores

SSFL scores

Potential local splice effect

Local MES scores

∆tESRseq

∆Hzei

ΔΨ

Distance

Type

WT

Var

VAR vs WT

WT

Var

VAR vs WT

WT

Var

(nt)

(3′ or 5’ss)

∆ (%)

∆ (%)

688

chr_16_68835593_G_A

CDH1

3

c.184G > A

p.Gly62Ser

587,781,898

5.99e-05

21

3’

8.17477

8.17477

0

86.5179

86.5179

0

   

− 1.44947

10.35

−1.24

chr2_47703664_G_A

MSH2

13

c.2164G > A

p.Val722Ile

587,781,996

8.99e-05

−47

5’

10.8583

10.8583

0

100

100

0

   

0.59756

10.51

−0.01

chr_8_90983475_C_A

NBN

6

c.628G > T

p.Val210Phe

61,754,796

0.0008158

44

3’

6.19815

6.19815

0

86.8244

86.8244

0

   

− 0.782222

−46.21

− 0.15

1873

chr_5_56155672_A_G

MAP3K1

3

c.764A > G

p.Asn255Ser

56,069,227

0.0269

−71

5’

7.52484

7.52484

0

78.4708

78.4708

0

New Acceptor Site?

8.8

−1.18661

6.7

−0.04

5378

chr 12_133244944_G_A

POLE

19

c.2171C > T

p.Ala724Val

61,734,163

0.00030

−3

5’

9.89081

8.73118

−11.7

86.6769

82.5488

−4.8

New Donor Site?

6.3

−2.14822

−32.05

−0.16

6031

chr17_41245621_T_C

BRCA1

10

c.1927A > G

p.Ser643Gly

80,357,105

NA

1257

3’

8.86265

8.86265

0

87.3058

87.3058

0

   

1.44078

58.08

0.02

 

AXIN2

10

c.2272G > A

p.Ala758Thr

145,007,501

0.0039861

35

3’

6.34671

6.34671

0

86.1925

86.1925

0

   

−0.942617

0.12

−0.09

chr5_112102960_C_T

APC

4

c.295C > T

p.Arg99Trp

139,196,838

0.0006444

75

3’

7.49577

7.49577

0

84.8039

84.8039

0

   

−2.2189

−14.34

− 0.08

12,470

 

AXIN2

10

c.2272G > A

p.Ala758Thr

145,007,501

0.0039861

35

3’

6.34671

6.34671

0

86.1925

86.1925

0

   

−0.942617

0.12

− 0.09

chr5_112128218_G_A

APC

7

c.721G > A

p.Glu241Lys

777,603,154

0.0001818

−9

5’

7.15277

7.15277

 

87.0697

87.0697

0

   

−1.51981

−49.76

−0.42

12,852

chr_14_69061228_G_A

RAD51B RAD51B

11

c.1063G > A

p.Ala355Thr

61,758,785

0.0071658

27

3’

11.8

11.8

0

80.2

80.2

0

−1.24035

−50.64

88,295

chr10_89690828_G_A

PTEN PTEN

4

c.235G > A

p.Ala79Thr

202,004,587

0.0001678

−19

5’

9.6515

9.6515

0

86.8647

86.8647

0

   

−1.39321

10.77

0.6

900,178

chr11_94197365_C_T

MRE11A MRE11A

11

c.1139G > A

p.Arg380His

587,781,646

4.5e-05

41

3’

8.9941

8.9941

0

95.7456

95.7456

0

   

−1.57887

−48.78

−0.03

960,579

chr_5_56177843_C_G

MAP3K1

14

c.2816C > G

p.Ser939Cys

45,556,841

0.0221

447

3’

12.0063

12.0063

0

100

100

0

−0.486881

−16.1

0

1,000,459

chr13_28537449_ACTT_A

CDX2

3

c.742_744del

p.Lys248delAAG

553,066,746

0.0001682

55

3’

11.7045

11.7045

0

87.4307

87.4307

0

   

−2.46964

−100.08

1,100,948

chr_17_7133187_A_G

DVL2 DVL2

5

c.596 T > C

p.Met199Thr

372,715,697

6.01e-05

−61

5’

6.34467

6.34467

0

80.4452

80.4452

0

   

0.0509416

−1.77

0.54

chr2_47641430_C_T

MSH2

5

c.815C > T

p.Ala272Val

34,136,999

0.0003755

23

3’

10.3527

10.3527

0

84.3224

84.3224

0

   

−2.17832

−46.5

−0.03

10,002,068

chr_17_63526198_C_T

AXIN2

11

c.2428G > A

p.Asp810Asn

140,344,858

1.5e-05

23

3’

11.6727

11.6727

0

87.3948

87.3948

0

   

−1.22987

−14.33

10,005,829

chr_14_69061228_G_A

RAD51B

11

c.1063G > A

p.Ala355Thr

61,758,785

0.0071658

27

3’

11.8

11.8

0

80.2

80.2

0

−1.24035

−50.64

chr8_90993640_C_T

NBN

3

c.283G > A

p.Asp95Asn

61,753,720

0.0030459

−38

5’

10.7663

10.7663

0

94.6711

94.6711

0

   

0.318238

24.6

0.03

chr_11_108155132_G_A

ATM

26

c.3925G > A

p.Ala1309Thr

149,711,770

0.0009147

  

9.98517

9.98517

0

84.8076

84.8076

0

   

0.676556

32.96

0.04

12,001,161

chr_14_68353893_A_G

RAD51B

7

c.728A > G

p.Lys243Arg

34,594,234

0.010682

−29

5’

9.09184

9.09184

0

78.9497

78.9497

0

Cryptic 5’ss activation?

0.9

7.9

−1.48785

−40.54

−0.19

12,015,576

chr19_15291551_C_G

NOTCH3

19

c.3083G > C

p.Trp1028Ser

rs146829488

na

−60

5’

11.1124

11.1124

0

82.5954

82.5954

0

   

0.300115

−6.4

0.1

11,717

chr1_45797881_C_T

MUTYH

10

c.881G > A

p.Cys294Tyr

rs879254257

na

−44

5’

6.31089

6.31089

0

72.818

72.818

0

   

1.09496

−7.06

0.04

17,161

chr_11_108139187_T_A

ATM

18

c.2689 T > A

p.Phe897Ile

147,122,522

4.5e-05

51

3’

9.8979

9.8979

0

93.4253

93.4253

0

   

0.554269

87.95

0.01

22

chr_12_133241897_A_G

POLE

21

c.2459 T > C

p.Met820Thr

767,460,640

0

−10

5’

6.58677

6.58677

0

77.9039

77.9039

0

   

1.28743

−2.13

0.06

chr_14_68352672_A_G

RAD51B

6

c.539A > G

p.Tyr180Cys

28,910,275

0.0045906

−34

5’

9.54919

9.54919

0

83.7411

83.7411

0

   

0.881539

7.23

−0.19

chr_5_56155672_A_G

MAP3K1

3

c.764A > G

p.Asn255Ser

56,069,227

0.0269

−71

5’

7.52484

7.52484

0

78.4708

78.4708

0

New Acceptor Site?

8.8

−1.18661

6.7

−0.04

6207

chr_17_63530163_C_T

AXIN2

10

c.2272G > A

p.Ala758Thr

145,007,501

0.0039861

35

3’

6.34671

6.34671

0

86.1925

86.1925

0

   

−0.942617

0.12

−0.09

6475

chr_17_33433488_G_A

RAD51D

6

c.493C > T

p.Arg165Trp

544,654,228

6.94e-05

13

3’

8.20686

8.20686

0

85.1161

85.1161

0

   

−2.55724

−22.32

1.42

na not available; *Non-Finnish European population based on ExAC database; NM for APC: NM_000038; ATM: NM_000051; AXIN2: NM_004655; BRCA1: NM_007300; CDH1: NM_004360; CDX2: NM_001265; DVL2: NM_004422; MAP3K1: NM_005921; MSH2: NM_000251; MRE11A: NM_005591; MUTYH: NM_012222; NBN: NM_002485; NOTCH3: NM_000435; POLE: NM_006231; PTEN: NM_000314; RAD51B: NM_133509; RAD51D: NM_002878. In order to predict their biological impact, RNA splicing-dedicated bioinformatics analyses were performed as described under Materials and Methods. Results shown in bold were considered as predictive of a potential variant-induced negative biological effect. MES MaxEntScan, SSFL Splice Site Finder-Like, nt Nucleotide, 3′ or 5’ss 3′ splice site or 5′ splice site, ESR Exonic splicing regulators

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).
Figure 2
Fig. 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.
Figure 3
Fig. 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).

Discussion

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].

Conclusions

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.

Abbreviations

ACMG: 

American College of Medical Genetics and Genomics

BC: 

Breast cancer

BIC: 

Breast Cancer Information Core Database

CRC: 

Colorectal cancer

ENIGMA: 

Evidence-based Network for the Interpretation of Germline Mutant Alleles

ESR: 

Exonic splicing regulatory elements

HGMD: 

Human Gene Mutation Database

InSiGHT: 

International Society of Gastrointestinal Hereditary Tumors Database

LOVD: 

Leiden Open Variation Database

LS: 

Lynch syndrome

MAF: 

Minor allele frequency

MES: 

MaxEntScan

NGS: 

Next generation sequencing

path_BRCA1/2

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

SNPs: 

Single nucleotide polymorphisms

SNV: 

Single-nucleotide variants

SSFL: 

SSF-like

VUS: 

Variants of unknown significance

WGD: 

Whole gene deletions

WT: 

Wild type

Declarations

Acknowledgements

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

Funding

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.

Availability of data and materials

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

Authors’ contributions

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.

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.

Consent for publication

Not Applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
(2)
Department of Genetic Medicine, The University of Manchester, Manchester Academic Health Science Centre, St. Mary’s Hospital, Manchester, UK
(3)
Genesis Prevention Centre, University Hospital of South Manchester, Wythenshawe, UK
(4)
Inserm-U1245, UNIROUEN, Normandie Univ, Normandy Centre for Genomic and Personalized Medicine, Rouen, France
(5)
Interactive Biosoftware, Rouen, France
(6)
Medizinische Klinik und Poliklinik IV, Campus Innenstadt, Klinikum der Universität München, Munich, Germany
(7)
MGZ—Medizinisch Genetisches Zentrum, Munich, Germany
(8)
Department of Human Medicine, Universität Witten/Herdecke, Witten, Germany
(9)
Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
(10)
Department of Informatics, University of Oslo, Oslo, Norway
(11)
Institute of Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway

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