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DETERMINING THE CLINICAL SIGNIFICANCE OF BRCA1 AND BRCA2 INTRONIC AND EXONIC SPLICING VARIANTS
Karla R. Bowles, Nanda Singh, Ulrike P. Kappes, Erin Mundt, Bryan Warf, Brian Morris, Dmitry Pruss, Benjamin B. Roa, Richard Wenstrup
Myriad Genetic Laboratories, Inc., Salt Lake City, UT, USA
BACKGROUND �Nucleotide changes which are predicted by in silico splicing analysis to affect mRNA splicing are sometimes identified during diagnostic sequencing analysis of the BRCA1 and BRCA2 genes.
� In silico splicing analysis tools should be used with caution as their use can result in incorrect variant interpretation, potentially leading to inappropriate medical management decisions.
�We describe the algorithms used by our laboratory to determine possible pathogenicity of intronic and exonic variants predicted by in silico analysis to result in abnormal mRNA splicing.
METHODS �Clinical germline testing for BRCA1 and BRCA2 sequencing mutations was performed on extracted patient genomic DNA, after informed consent was obtained.
� Sanger or next generation sequencing analyses of BRCA1 and BRCA2 identified nucleotide changes predicted by in silico splicing analysis to result in abnormal mRNA splicing.
�Using our variant classification and reclassification processes, which include multiple methodologies for variant evaluation and interpretation (Figure 1, Table 1), we further investigated the pathogenicity of these putative splicing variants.
RESULTS � INTRONIC VARIANTS: Sequencing analysis of >1 million patients identified ~1500 unique intronic variants in BRCA1 and BRCA2.
� In silico splice site analysis accurately identified pathogenic mutations lying within the consensus splice junctions at the +1/+2 or -1/-2 intronic positions as pathogenic splicing mutations (Tables 2-3, Figure 2).
� In silico splice site analysis of 456 benign intronic variants indicated that ~4.6% of these variants may negatively affect normal mRNA splicing, resulting in a 25%-75% estimated decrease in wild type donor or acceptor strength (Figure 3). However, analysis of some of these variants, such as BRCA1 c.81-13C>A and BRCA2 c.9501+3A>T, using other classification methodologies confirms their benign classifications.
� EXONIC VARIANTS:
� In silico splice site analysis accurately identified pathogenic mutations lying at the consensus last nucleotide of certain exons, such as BRCA1 c.4185G>A and BRCA2 c.9117G>A, as pathogenic splicing mutations.
� In silico splice site analysis of 3380 benign exonic variants indicated that ~4% of these variants have the potential to result in the creation of or significant strengthening of (> 0.10 absolute score increase with a final score >0.50) an alternative donor or alternative acceptor. However, variants such as BRCA1 c.3699A>G (p.Lys1233Lys) and BRCA2 c.9876G>A (p.Pro3292Pro) have proven to be benign using other classification evidence.
Figure 2: Raw history weighting algorithm graphs illustrating classification calls for select BRCA1 and BRCA2 variants. Deleterious (red) and benign (green) control distributions with corresponding variant-specific history weighting scores (blue) are indicated for each variant. The Log History Weighting Score is plotted on the x-axis and the Number of Control Variants is plotted on the y-axis.
BRCA1 c.4185G>A
0
5000
10000
15000
20000
-15 -10 -5 0 5 10
BRCA2 c.9117G>A
0
5000
10000
15000
-40 -20 0 20 40
BRCA1 c.3699A>G (p.Lys1233Lys)
0
5000
10000
15000
20000
-30 -20 -10 0 10 20
BRCA2 c.9876G>A (p.Pro3292Pro)
0
5000
10000
15000
-40 -20 0 20 40
0
5000
10000
15000
20000
25000
-100 -50 0 50
BRCA1 c.81-13C>A
0
5000
10000
15000
20000
25000
-100 -50 0 50
BRCA1 c.135-1G>T
0
5000
10000
15000
20000
-40 -20 0 20 40
BRCA2 c.9501+3A>T
-2000
3000
8000
13000
18000
-40 -20 0 20 40
BRCA2 c.7977-1G>C
CONCLUSIONS �We have developed and implemented a robust classification and reclassification program for variants predicted by in silico analysis to result in abnormal mRNA splicing.
� In silico mRNA splicing analysis may indicate that particular variants negatively affect mRNA splicing. However, many of these predictions are either inaccurate or splicing effects are small and do not result in significant increases in cancer risk.
� In silico predictors should be used with caution and results rigorously verified by other independent methods before being used in the clinical setting in order to ensure correct test result interpretation and appropriate clinical management.
Figure 1. Basic algorithm used to classify potential mRNA splicing
variants
Novel Intronic or Exonic Variant Which May Affect Splicing Identified in Clinical Patient
Evaluate Data
Determine variant frequencies in cases and controls Perform in silico splice site analysis Review literature
Review other available data
Classify Variant
PathogenicBenign
Send Patient Report
Likely PathogenicLikely Benign
Variant of Uncertain Significance (VUS)
Variant Reclassification Program
Automated StatisticalAnalysis
Additional probandidentified
Automated History
Weighting analysis
Automated Mutation
Co-occurrence (MCO) analysis
Data compiled
Statistical thresholdreached - Evidence
for pathogenic or benign
Statistical threshold not reached -
Hold formore probands
Family Analysis
Offer free testing toappropriate family
members
Run lab-developedsegregation
algorithm
in trans co-occurrence
identified - Evidencefor benign
Segregation datasignificant in multiple
families - Evidence for pathogenic
or benign
Segregation datainsignificant -
Gather more family members
• Identify newrelevant literature
• Performed weekly
Monitor Literature
Monitor variantfrequencies in
cases and controls
Variant reclassified and amended reports issued
Evidence for
Pathogenic• Found at
higherfrequency incancer casesthan in controls
Evidence for
Benign• Found in >2%
of controls• Found at equal
frequencies in cancer cases and controls
Uninformative• Found in <2%
of controls
Evidence for
Pathogenic• Predicted to
disrupt splicingwith definitivebiochemicalconfirmation
Evidence for
Benign• Not predicted
to disrupt splicing
Uninformative• Predicted to
disrupt splicingwithoutdefinitive biochemicalconfirmation
Evidence for
Pathogenic• Segregates
with cancer inmultiple families
• Biochemicalconfirmationof splicingdefect
• Biochemicalconfirmation of complete transcript loss
Evidence for
Benign• in trans with
pathogenicmutation
• Does not segregate withcancer in multiple families
• Biochemicalconfirmation of normaltranscription
Uninformative• Segregation
not concordantbetween families
• Biochemical analysis showspartial effecton splicing
• Biochemicalanalysis showspartial effecton transcription
Table 2. BDGP Splice Site Analysis Results
Gene Variant Wild Type Score Variant Score
BRCA1 c.135-1G>T0.97
(Acceptor)<0.10
(Acceptor)
BRCA1 c.81-13C>A0.52
(Acceptor)0.25
(Acceptor)
BRCA1c.3699A>G
(p.Lys1233Lys) <0.10
(Alternate Donor)0.98
(Alternate Donor)
BRCA1 c.4185G>A0.95
(Donor)0.38
(Donor)
BRCA2 c.7977-1G>C0.98
(Acceptor)<0.10
(Acceptor)
BRCA2 c.9501+3A>T0.99
(Donor)0.41
(Donor)
BRCA2 c.9117G>A0.57
(Donor)<0.10
(Donor)
BRCA2c.9876G>A
(p.Pro3292Pro)
0.10 (Alternate Acceptor)
0.85 (Alternate Acceptor)
Table 3. Variants determined by in silico splicing analysis (BDGP) to possibly result in abnormal splicing
Gene VariantTranscript Analysis
# Myriad Probands
# Probands with a Pathogenic
Mutation (in trans)
History Weighting
Algorithm Call
Mutation Co-occurrence Algorithm Call
Final Interpretation
BRCA1 c.135-1G>TAbnormal
splicing1 1640
(0)Pathogenic No Call Pathogenic
BRCA1 c.81-13C>A No Data 16811 (1)
Benign Benign Benign
BRCA1 c.3699A>G (p.Lys1233Lys) No Data 473 (1)
Benign No Call Benign
BRCA1 c.4185G>AAbnormal Splicing2 11
0 (0)
No Call No Call Pathogenic
BRCA2 c.7977-1G>CAbnormal Splicing3 118
0 (0)
Pathogenic No Call Pathogenic
BRCA2 c.9501+3A>T No Data 31618 (5)
Benign Benign Benign
BRCA2 c.9117G>AAbnormal Splicing4 150
0 (0)
Pathogenic No Call Pathogenic
BRCA2 c.9876G>A (p.Pro3292Pro) No Data 13611 (1)
Benign Benign Benign
1. Tesoriero AA, et al. Hum Mutat 26:495, 2005. 2. Claes K, et al. Genes, Chromosomes & Cancer 37:314-320, 2003. 3. Myriad Genetic Laboratories, Inc. Internal data. 4. Peelen T et al. British J Cancer 82:151-6, 2000; Bonatti F et al. Cancer Genet Cytogenet 170:93-101, 2006.
Table 1. Additional descriptions of select variant analysis methods
Method Description/ Rationale for Use
In silico splice site prediction
Multiple splice site analysis programs, which estimate the impact of a particular variant on mRNA splicing, are available for public use. Myriad uses both publicly available and internally developed programs. BDGP results are provided for the variants discussed.1
History Weighting analysis
This statistical technique, developed and validated by Myriad, is based upon the premise that disease associated mutations will be observed more often in individuals at high risk for carrying a mutation, as determined by the severity of personal and family history, but the observation of benign variants should be independent of personal and family history.2
Mutation co-occurrence analysis
This statistical technique, developed and validated by Myriad is based on the observation that the primary genetic cause of disease in a family is usually attributable to a single pathogenic mutation. Therefore, variants found to co-occur with a pathogenic mutation in the same individual are less likely to be pathogenic themselves.3
In trans co-occurrence and homozygosity analyses
Homozygous or compound heterozygous BRCA1 and BRCA2 pathogenic mutations are generally presumed to be embryonically lethal (BRCA1/2) or to result in severe phenotypes such as Fanconi anemia (BRCA2), although some exceptions have been identified. Therefore, a homozygous observation of a variant or, an in trans co-occurrence of a particular variant with a pathogenic mutation, provides evidence that a variant may be benign.3
1. Berkeley Drosophila Genome Project (BDGP). www.fruitfly.org/seq_tools/splice.html2. Pruss D et al. Breast Cancer Res Treat, Epub ahead of print, 2014.3. Eggington JM, et al. Clin Genet. 2014 Sept;86(3):229-37. PubMed PMID: 24304220.
Figure 2: Raw history weighting algorithm graphs illustrating classification calls for select BRCA1 and BRCA2 variants. Deleterious (red) and benign (green) control distributions with corresponding variant-specific history weighting scores (blue) are indicated for each variant. The Log History Weighting Score is plotted on the x-axis and the Number of Control Variants is plotted on the y-axis.
Figure 3: A) In silico splice site analysis of 456 benign intronic variants indicated that ~4.6% of these variants may negatively affect normal mRNA splicing, resulting in a 25%-75% estimated decrease in wild type donor or acceptor strength. B) In silico splice site analysis of 3380 benign exonic variants indicated that ~4% of these variants have the potential to result in the creation of or significant strengthening of (> 0.10 absolute score increase with a final score >0.50) an alternative donor or alternative acceptor.
< 0.1 absolute increase in alternative donor/acceptor score or not predicted to affect splicing
> 0.1 absolute increase in alternative donor/acceptor score, final score <0.25
> 0.1 absolute increase in alternative donor/acceptor score, final score 0.25 to 0.49
> 0.1 absolute increase in alternative donor/acceptor score, final score 0.50 to 0.74
> 0.1 absolute increase with alternative donor/acceptor score, final score > 0.75
No effect or positive effect on donor/acceptor
Negative effect < 25%
Negative effect 25% to 49.9%
Negative effect 50% to 74.9%
42392.8%
122.6%
163.5%
51.1%
299988.7%
1374.1%
752.2%
732.2%96
2.8%
A B
Presented at NSGC - September 18, 2014