Development and validation of a new algorithm for the reclassification ...

Breast Cancer Res Treat (2014) 147:119–132 DOI 10.1007/s10549-014-3065-9

EPIDEMIOLOGY

Development and validation of a new algorithm for the reclassification of genetic variants identified in the BRCA1 and BRCA2 genes Dmitry Pruss • Brian Morris • Elisha Hughes • Julie M. Eggington • Lisa Esterling • Brandon S. Robinson • Aric van Kan • Priscilla H. Fernandes • Benjamin B. Roa • Alexander Gutin • Richard J. Wenstrup • Karla R. Bowles

Received: 14 March 2014 / Accepted: 15 July 2014 / Published online: 2 August 2014  Springer Science+Business Media New York 2014

Abstract BRCA1 and BRCA2 sequencing analysis detects variants of uncertain clinical significance in approximately 2 % of patients undergoing clinical diagnostic testing in our laboratory. The reclassification of these variants into either a pathogenic or benign clinical interpretation is critical for improved patient management. We developed a statistical variant reclassification tool based on the premise that probands with disease-causing mutations are expected to have more severe personal and family histories than those having benign variants. The algorithm was validated using simulated variants based on approximately 145,000 probands, as well as 286 BRCA1 and 303 BRCA2 true variants. Positive and negative predictive values of C99 % were obtained for each gene. Although the history weighting algorithm was not designed to detect alleles of lower penetrance, analysis of the hypomorphic mutations c.5096G[A (p.Arg1699Gln; BRCA1) and c.7878G[C (p.Trp2626Cys; BRCA2) indicated that the history weighting algorithm is able to identify some lower penetrance alleles. The history weighting algorithm is a powerful tool that accurately assigns actionable clinical classifications to variants of uncertain clinical significance. While being developed for Electronic supplementary material The online version of this article (doi:10.1007/s10549-014-3065-9) contains supplementary material, which is available to authorized users. D. Pruss  B. Morris  E. Hughes  A. Gutin Myriad Genetics, Inc., Salt Lake City, UT, USA J. M. Eggington  L. Esterling  B. S. Robinson  A. van Kan  P. H. Fernandes  B. B. Roa  R. J. Wenstrup  K. R. Bowles (&) Myriad Genetic Laboratories, Inc., 320 Wakara Way, Salt Lake City 84108, UT, USA e-mail: [email protected]

reclassification of BRCA1 and BRCA2 variants, the history weighting algorithm is expected to be applicable to other cancer- and non-cancer-related genes. Keywords BRCA1  BRCA2  Breast cancer  Ovarian cancer  Variant classification

Introduction Approximately 7 % of breast cancers and 10 % of ovarian cancers result from single-gene mutations [1]. The majority of these mutations have been identified in the BRCA1 and BRCA2 genes [2–4]. Identification of mutation carriers facilitates early detection and more effective clinical strategies to reduce risk. DNA sequencing and large rearrangement analyses detect BRCA1 and BRCA2 DNA changes. Standard variant classification guidelines recommend a multiple tier classification system [5]. The identification of a Pathogenic or Likely Pathogenic mutation is generally cause to clinically manage a patient as having a high cancer risk [6], although some exceptions occur. Genetic analyses sometimes identify variants of uncertain clinical significance (VUS). Clinical management of individuals carrying a VUS should be based upon personal and family history and not the presence of the variant itself, but variants often increase anxiety and uncertainty. Thus, efforts are made to reclassify these VUS. Multiple approaches have been taken to reclassification. Segregation analysis can be limited due to small pedigrees, incomplete penetrance, and a significant phenocopy rate. Structural and functional analyses are also limited, as the functional roles of BRCA1 and BRCA2 have not been fully elucidated. The evaluation of species conservation may provide some clues as to the impact of a VUS, but commonly

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utilized computer programs, such as SIFT and PolyPhen-2 [7–9], have high error rates. As BRCA1/BRCA2 clinical genetic testing has become standard of care for individuals with high-risk personal and family histories, large genetic datasets have been developed allowing for the development of novel statistical algorithms for the reclassification of VUS [10]. Utilizing a dataset of [550,000 genetically tested probands, we developed and validated a personal/family history weighting algorithm to aid in the reclassification of BRCA1/ BRCA2 VUS.

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Patient ascertainment After informed consent, probands underwent comprehensive genetic testing for mutations in the BRCA1 and BRCA2 genes. Testing included germline DNA sequencing of the entire BRCA1 and BRCA2 coding regions (with or without additional large rearrangement analyses). Patient samples were collected by healthcare providers who supplied the following information: the proband’s age, ethnicity, personal cancer history including cancer type(s) and age(s) at diagnosis (if applicable), and family history of cancer, including a list of affected relatives, cancer type(s) and age(s) at diagnosis.

Methods Construction of conditional probability tables Previously, Easton et al. [10] used logistic regression to estimate the probability of carrying a pathogenic mutation, conditional on personal and family history for each proband. These probabilities were used to calculate a Bayesian likelihood ratio of the posterior odds to the prior odds of the proband carrying a pathogenic mutation. Assuming independence, these Bayesian likelihood ratios were multiplied over probands with the same VUS to obtain a likelihood ratio for the VUS, which was evaluated as odds for or against the VUS being pathogenic. The history weighting score is a similar likelihood except that probabilities are estimated from observed proportions in a clinical population rather than logistic regression, and the history weighting score is not simply evaluated as odds for or against the VUS being pathogenic. Rather, the history weighting algorithm conducts two hypothesis tests with each history weighting score. One hypothesis test compares the history weighting score of the VUS against an empirical cumulative distribution function (ECDF) constructed from history weighting scores of probands with known pathogenic mutations. The second test compares the history weighting score of the VUS against the ECDF of history weighting scores from probands known to have neither pathogenic variants nor variants of unknown significance. The history weighting algorithm will make a pathogenic call if we can reject, at the 0.005 significance level according to the ECDF, the null hypothesis that the variant is benign and if we fail to reject, at the 0.05 significance level, the null hypothesis that the variant is pathogenic. Each hypothesis test implicitly assumes that pathogenic mutations within the same gene confer identical risk. As the history weighting algorithm was not designed to detect lower penetrance pathogenic alleles, mutations determined by other classification methodologies to represent significantly lower penetrance alleles, in comparison with other BRCA1 and BRCA2 pathogenic mutations, were excluded from all testing and validation data sets, unless otherwise noted.

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Probands’ personal and family histories were analyzed for cancers associated with mutations in BRCA1 and/or BRCA2 (Supplemental Table 1) [11–19]. Other cancer types were scored as unaffected. Familial weighting and proband-specific conditional probability tables were constructed independently for the BRCA1 and BRCA2 genes based upon empirical analysis of the proband dataset. Familial weighting tables For each affected family member, a numeric severity weight (Wrel) was calculated as the proportion of pathogenic mutation carriers observed among the members of our clinical population who reported one or more relatives with the same cancer type and similar age at diagnosis as the affected family member in question. Ages were specified in yearly intervals and a minimum of 100 probands was required to calculate Wrel for a specific relative’s age. If 100 probands could not be identified, the age window was expanded in 1 year increments up to, and including, ±5 years until at least 100 probands were assessed. If 100 probands could not be identified, the age/cancer type combination was eliminated from the weighting table. Unaffected relatives were not scored. Proband conditional probability tables Tables estimating the probability of the proband carrying a BRCA1 or BRCA2 pathogenic mutation were constructed for each gene based on the proportion (Ppro) of pathogenic mutation carriers observed among the members of our clinical population sharing the proband’s cancer type, age at diagnosis or current age if unaffected, and the rounded to the nearest integer weighted sum of the Wrel values of relatives X wi  Wreli i

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with the weight of wi = 2.0 for first-degree family members and the weight of wi = 1.0 for second-degree relatives. History weighting score calculation and hypothesis testing For each proband, a likelihood ratio (LR = ((1 Ppro) * Pave)/(Ppro * (1 - Pave))) was calculated as the ratio of the posterior odds to the prior odds of the proband not carrying a pathogenic mutation. If a proband or relative had multiple cancer types, the highest applicable Wrel and/ or Ppro values were used in the calculation. Pave was the average Ppro value for all probands (with and without pathogenic mutations) tested for the specified gene. Assuming independence of individuals with the same VUS, the likelihood ratios of individuals were cumulatively multiplied to obtain a likelihood ratio for the variant, hereafter referred to as the history weighting score (HWS = (LR1)(LR2),…,(LRn)) of the variant. Probands for which personal and family histories were not provided or who also carried a pathogenic mutation or an additional VUS within BRCA1 or BRCA2 were excluded. Known family members of a proband were also excluded. Only the most recent 100 probands/variant were analyzed. Control probands Variant-specific history weighting scores were compared to history weighting scores derived from pathogenic and benign control probands. Pathogenic (positive) controls were selected from individuals carrying a pathogenic sequencing mutation within the gene of interest. Benign (negative) controls were selected from individuals carrying either benign or no BRCA1/BRCA2 variants. For both positive and negative control probands, a minimum of 100 matched unique control individuals was selected for each carrier proband. Controls were matched to probands by ethnicity and time of testing. If the proband specified one ancestry, all controls were selected from within that ancestry. If the proband specified two ancestries, at least 50 controls were selected from each ancestry and so on. The eligible control pool for each proband was initially limited to controls tested within ±180 days of the proband’s test date. If the minimum number of controls for each ancestry was not identified within the ±180-day window, ethnically ‘‘unmatched’’ controls from within the same time window were randomly selected until 100 controls had been identified. Ethnically unmatched controls were preferred over the expansion of the ±180-day window, as preliminary analyses determined that time of testing ascertainment

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biases were generally greater than ethnicity biases (data not shown). If a main ancestry was not specified for the proband, then all ethnically unmatched controls within the time window were used. If the ±180-day window did not identify 100 unique controls, the window was expanded by another ±180 days iteratively until 100 unique controls were identified. Composite control variants 100,000 pathogenic and 100,000 benign random composite control variants were constructed for history weighting score comparison. Each composite variant was composed of the same number of control probands (see above) as the variant under investigation. For example, if the investigational variant had been observed in 20 eligible probands, then 100,000 pathogenic and 100,000 benign composite variants, each with 20 randomly selected pathogenic or benign control probands (matched to their respective investigational probands; see above), were constructed. In most cases, due to limited control availability, the same control was used for the construction of more than one composite variant. History weighting scores were calculated for each composite control variant and the ECDFs of the pathogenic and benign composite variant history weighting scores were plotted on a graph to conduct hypothesis tests for and against causality of the variant under investigation (Fig. 1). Determination of proband minimums and construction of simulated variants A minimum number of qualifying carrier probands was required before a variant became eligible for analysis. Proband minimums were dependent on the gene and variant classification call made by the history weighting analysis tool. Minimums were established using four two-fold cross-validations. In each cross-validation, the proband dataset was divided randomly into two equal halves of *200,000. Conditional probability tables were constructed using Half A. Simulated variants were constructed from Half B probands (and vice versa; Supplemental Table 2). It should be noted that simulated variants used for testing differ from composite control variants used for ECDF construction in that probands are selected randomly (as follows) for simulated variants, whereas probands selected for composite control variants are matched to variant-specific probands based on ethnicity and time of testing (see above). For pathogenic simulated variants composed of n individuals, n variants were chosen uniformly at random from true pathogenic variants within the gene of interest to allow for representation of common and rare pathogenic variants. One individual was selected uniformly at random

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Benign

Weak Benign

Weak Pathogenic

30000 Pathogenic Controls

Number of Control Variants

Fig. 1 Illustration of a history weighting algorithm graph. Pathogenic and benign positive control distributions, percentile threshold lines utilized by the history weighting algorithm for variant classification calls, and corresponding variant classification categories (top) are indicated

Not Callable

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Pathogenic

122

Pathogenic 95th Percentile

25000

Pathogenic 99.95 Percentile Benign Controls

20000

Benign 5th Percentile Benign 0.05 Percentile

15000

10000

5000

0 -100

-50

0

50

100

Log History Weighting Score

from each chosen variant. These n individuals were treated as carriers of the simulated variant to be classified. Each proband after the first proband selected had a 0.05 probability of being randomly duplicated as an additional proband to simulate unknown proband relatedness. Simulated variants with n probands were constructed separately for each gene and classification starting with n = 1 and increasing in increments of 1. A minimum of 10,000 simulated variants was constructed for each n. Benign simulated variants were similarly constructed. Positive (PPV) and negative (NPV) predictive values were generated for each n and variant classification combination as follows: PPV ffi

Classification Call

History weighting score compared to pathogenic composite controls

History weighting score compared to benign composite controls

Pathogenic

\95th percentile

\0.05 percentile

Weak pathogenic

C95th and B99.95 Percentiles

\0.05 percentile

Not callable

[99.95 percentile

\0.05 percentile

Weak benign

[99.95 percentile

C0.05 and B5th percentiles

Benign

[99.95 percentile

[5th percentile

Indeterminate

All other variants

TP ; TP þ FP

  TP=Npþ  Prevalence     ¼ TP= FP=  Prevalence þ Npþ Np  ð1  PrevalenceÞ NPV ffi

Table 1 Classification thresholds utilized by the history weighting analysis tool

TN ; TN þ FN

¼



 TN=Nn  ð1  PrevalenceÞ   TN= FN= Nn   ð1  PrevalenceÞ þ Nnþ  Prevalence 

where TP is true positives, FP is false positives, TN is true negatives, FN is false negatives, Np? is the total number of tests involving pathogenic mutations with sufficient probands for a pathogenic call, Np- is the total number of tests involving benign variants with sufficient probands for

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a false pathogenic call, Nn- is the total number of tests involving benign variants with sufficient probands for a benign call, and Nn? is the total number of tests involving pathogenic mutations with sufficient probands for a false benign call. Pathogenic mutation prevalence estimates of 13.8 and 6.7 % (data not shown) were used for BRCA1 and BRCA2, respectively. Classification thresholds were predefined, with only ‘‘Pathogenic’’ and ‘‘Benign’’ calls being counted toward PPV and NPV (Table 1). Other calls were categorized as ‘‘Indeterminate’’. Proband minimums resulting in PPVs of [0.9975 and NPVs of [0.999 were selected for implementation of the algorithm. The NPV threshold was set higher than that for PPV to account for uncertainty in estimation of pathogenic mutation prevalence per gene.

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Validation with simulated variants *145,000 probands clinically tested between September 2011 and December 2012 were selected for analysis. 50,500 simulated benign variants and 25,500 simulated pathogenic mutations per gene were constructed. Each variant was initially analyzed using the minimum number of eligible probands required. If the history weighting tool was unable to call a variant as ‘‘Pathogenic’’ or ‘‘Benign,’’ the number of probands was increased by at least 20 %, and the history weighting score was recalculated. This process was repeated until the variant was classified as ‘‘Pathogenic,’’ ‘‘Benign’’ or ‘‘Not Callable.’’

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minimum proband numbers. Four two-fold cross-validations demonstrated that PPVs and NPVs were strongly dependent on the number of analyzed probands (Fig. 2). Greater proband numbers were required to achieve target PPVs and NPVs for BRCA2 variants in comparison with BRCA1 variants due to the lower penetrance of pathogenic BRCA2 mutations (Supplemental Table 3). Proband minimums were tested through analysis of 599 BRCA1 and 646 BRCA2 true variants with clinical classifications previously determined by other methodologies. One hundred trials were performed for each eligible variant. PPV and NPV were C0.999 (Table 2). Probands were selected from the same data set from which simulated variants used for algorithm development were constructed.

Testing and validation with true variants Validation of the history weighting algorithm For testing, BRCA1 and BRCA2 true variants previously classified using other methodologies were analyzed [20]. For each variant, the minimum number of probands was selected uniformly at random and an algorithm call was made. If the variant could be classified as ‘‘Benign’’ or ‘‘Pathogenic,’’ the call was scored. Otherwise, the number of probands was increased by an additional 20 % and another call attempted. Probands were added in this manner until either a definitive call was made or no more probands were available. One hundred trials were performed for each variant analyzed. For validation, variants previously classified using other methodologies and with C6 or C10 (BRCA1 or BRCA2, respectively) proband observations between September 2011 and December 2012 were analyzed. All probands were independent of algorithm development and testing. History weighting analysis of representative BRCA1 and BRCA2 variants BRCA1 and BRCA2 probability tables were constructed as described above using the proband set of *400,000 individuals. For variants with \100 probands, all eligible probands were analyzed. For variants with [100 eligible probands, only the most recent 100 probands were analyzed. Proband minimums established through development and validation were utilized.

Results Determination of proband minimums The history weighting algorithm for variant classification was developed using empirical analysis of a dataset consisting of *400,000 probands undergoing BRCA1 and BRCA2 sequencing analyses. A novel and critical component of the algorithm is the establishment of required

In order to assess the performance of the algorithm using a dataset that was independent of development and testing, *145,000 probands identified through clinical testing from September 2011 through December 2012 were used for analysis. 25,500 simulated pathogenic mutations and 50,500 simulated benign variants were constructed. NPV and PPV for both BRCA1 and BRCA2 were [0.999 and 0.993, respectively (Table 3). Additional validation was performed using variants from this proband set that had C6 or C10 (BRCA1 or BRCA2, respectively) eligible probands identified. BRCA1 (n = 286) and BRCA2 (n = 303) true variants, with all probands not previously used for algorithm development or testing, were analyzed. One hundred trials were performed for each variant. PPV and NPV were C0.998 and 0.999, respectively (Table 4). History weighting analysis of representative BRCA1 and BRCA2 variants The history weighing algorithm correctly classified representative BRCA1 and BRCA2 variants, which had been previously classified as pathogenic mutations or benign variants based on other methodologies (Table 5; Fig. 3) [21–31]. An additional two mutations, p.Arg1699Gln (BRCA1) and p.Trp2626Cys (BRCA2), which most likely represent hypomorphic alleles based on segregation and/or functional analyses, were classified as ‘‘Not Callable’’ by the history weighting algorithm, consistent with their previous hypomorphic classifications.

Discussion Based on the analysis of *550,000 probands (including both development and validation datasets), we have

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Fig. 2 Results from four two-fold cross-validations for PPV (a), and NPV (b). Cross-validations were conducted separately for BRCA1 (left panels) and BRCA2 (right panels) over a range of proband minimums

Table 2 Testing results with true variants (100 trials per variant) Gene

History weighting classification

BRCA1

Pathogenic

True classification

# true variant trials

PPV

Pathogenic

13,533

0.9997

Np? = 14,200 Benign

1

Np- = 18,900 BRCA2

Pathogenic

Pathogenic

5,453

1.0000

Np? = 6,200 Benign

0

Np- = 16,800 Gene

History weighting classification

BRCA1

Benign

True classification Pathogenic

# true variant trials 176

NPV 0.9988

Nn? = 28,500 Benign

26,859

Nn- = 31,400 BRCA2

Benign

Pathogenic

228

Nn? = 24,200 Benign Nn- = 40,400

123

32,786

0.9992

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125

Table 3 Validation results of BRCA1 and BRCA2 simulated variants independent of algorithm development and testing Gene

History weighting classification

True classification

# simulated variants

PPV

BRCA1

Pathogenic

Pathogenic

24,173

0.9939

Np? = 25,500 Benign

47

Np- = 50,500 BRCA2

Pathogenic

Pathogenic

23,284

0.9931

Np? = 25,500 Benign

23

Np- = 50,500 Gene

History weighting classification

True classification

BRCA1

Benign

Pathogenic

# simulated variants 117

NPV 0.9992

Nn? = 25,500 Benign

47,945

Nn- = 50,500 BRCA2

Benign

Pathogenic

117

0.9997

Nn? = 25,500 Benign

48,499

Nn- = 50,500

Table 4 Validation results of BRCA1 and BRCA2 true variants independent of algorithm development and testing (100 trials per variant) Gene

History weighting classification

BRCA1

Pathogenic

True classification

# true variant trials

PPV

Pathogenic

3,562

0.9994

Np? = 3,700 Benign

1

Np- = 11,100 BRCA2

Pathogenic

Pathogenic

432

0.9985

Np? = 500 Benign

1

Np- = 11,000 Gene

History weighting classification

BRCA1

Benign

True classification Pathogenic

# true variant trials 11

NPV 0.9998

Nn? = 10,000 Benign

13,597

Nn- = 18,600 BRCA2

Benign

Pathogenic

9

0.9999

Nn? = 7,000 Benign

15,213

Nn- = 23,300

developed and validated a history weighting algorithm, which can be used to reclassify BRCA1 and BRCA2 VUS into more definitive clinical classifications. This algorithm is based on the premise that pathogenic mutations will occur more often in high-risk individuals, as determined by personal and family history, while the occurrence of benign

variants will be independent of personal and family history. The premise underlying the history weighting algorithm was previously postulated by Easton et al, who developed a logistic regression model [10]. This model was limited in granularity and power by the patient population, 61,270 individuals, available for its development. The larger

123

123

c.5165C[T (p.Ser1722Phe) [28]

c.5177_5180del (p.Arg1726Lysfs*3)

c.5194-2A[G

BRCA1

BRCA1

BRCA1

c.5096G[A (p.Arg1699Gln) [22–24, 28, 34]

c.5123C[A (p.Ala1708Glu) [28, 44, 45]

c.4987-20A[G

BRCA1

BRCA1

c.4689C[G (p.Tyr1563*) c.4955T[C (p.Met1652Thr) [28]

BRCA1 BRCA1

BRCA1

c.4357?1G[A

c.4675?1G[A [43]

c.4327C[T (p.Arg1443*)

BRCA1

BRCA1

c.4113G[A (p.Gly1371Gly)

BRCA1

BRCA1

c.4065_4068del (p.Asn1355Lysfs*10)

c.3418A[G (p.Ser1140Gly) [41]

BRCA1

c.4039A[G (p.Arg1347Gly) [42]

c.2814A[G (p.Pro938Pro)

BRCA1

BRCA1

c.2733A[G (p.Gly911Gly)

BRCA1

BRCA1

c.2681_2682del (p.Lys894Thrfs*8)

BRCA1

c.3598C[T (p.Gln1200*)

c.1971A[G (p.Gln657Gln)

BRCA1

c.3756_3759del (p.Ser1253Argfs*10)

c.1065G[A (p.Lys355Lys) c.1175_1214del (p.Leu392Glnfs*5)

BRCA1 BRCA1

BRCA1

c.591C[T (p.Cys197Cys)

BRCA1

BRCA1

c.301?8T[C

c.427G[T (p.Glu143*)

c.301?7G[A

BRCA1

BRCA1

c.212?1G[A [40]

BRCA1

BRCA1

c.181T[G (p.Cys61Gly) [26]

c.191G[A (p.Cys64Tyr) [38, 39]

c.131G[T (p.Cys44Phe) [27, 37]

BRCA1

BRCA1

c.81-14C[T

BRCA1

BRCA1

Variant

Gene

Pathogenic

Pathogenic

Pathogenic

Pathogenic

Likely Pathogenic (Hypomorphic)

Benign

Pathogenic Benign

Pathogenic

Pathogenic

Pathogenic

Benign

Pathogenic

Benign

Pathogenic

Pathogenic

Benign

Benign

Benign

Pathogenic

Benign

Benign Pathogenic

Benign

Pathogenic

Benign

Benign

Pathogenic

Pathogenic

Pathogenic

Pathogenic

Benign

Prior classification

33

100

44

100

100

100

100 65

35

100

100

100

100

100

100

100

100

100

100

100

100

100 100

100

100

100

100

35

100

100

42

100

# probands analyzed

-6.89

-21.82

-9.7

-17.11

-22.27

-9.65

-17.3 -14.99

-8.61

-33.53

-13.6

-11.02

-13.99

-1.92

-12.44

-28.41

-7.32

-8.83

-4.67

-14.69

-7.81

-16.93 -19.9

-4.36

-20.37

-17.2

-14.07

-7.62

-30.71

-11.31

-8.31

-6.38

2.68

-1.21

-12.18

-3.63

-7.6

-12.28

0

-7.94 -7.23

-3.08

-23.18

-4.12

-1.72

-4.35

6.64

-2.92

-18.41

2.32

0.36

4.27

-5.19

2.03

-7.22 -10.43

4.65

-11.1

-7.92

-4.58

-2.05

-21.23

-1.78

-2.18

0.98

17.44

1.8

19.21

20.58

22.17

20.08 8.46

1.02

11.38

21.75

21.62

21.8

25.85

22.46

13.18

22.65

22.85

27.7

21.9

22.51

23.27 19.07

24.94

18.93

18.98

22.29

-0.05

13.82

25.91

3.15

24.52

5.36

24.18

6.68

25.56

27.51

28.15

26.66 13.91

5.29

18.31

27.95

27.97

28.17

31.4

28.75

20.24

28.86

28.75

33.17

28.25

28.46

30.06 25.7

31

25.57

25.41

28.65

4.39

20.66

32.29

7.73

30.61

5.00 percentile

0.05 percentile

95.0 percentile

99.95 percentile

Benign control history weighting score

Pathogenic control history weighting score

Table 5 History weighting analysis results for selected BRCA1 and BRCA2 variants previously classified by other methodologies

-11.95

-45.4

-14.4

-30.71

9.19

37.2

-26.07 21.16

-16.96

-62.73

-24.3

36.61

-25.94

33.6

-27.92

-40.09

34.57

34.09

41.03

-23.82

34.13

33.41 -24.44

36.1

-39.39

29.06

30.07

-17.61

-56.56

-14.04

-13.64

36.02

Variant history Weighting score

Pathogenic

Pathogenic

Pathogenic

Pathogenic

Not Callable

Benign

Pathogenic Benign

Pathogenic

Pathogenic

Pathogenic

Benign

Pathogenic

Benign

Pathogenic

Pathogenic

Benign

Benign

Benign

Pathogenic

Benign

Benign Pathogenic

Benign

Pathogenic

Benign

Benign

Pathogenic

Pathogenic

Pathogenic

Pathogenic

Benign

History weighting call

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c.5503C[T (p.Arg1835*)

BRCA1

c.6275_6276del (p.Leu2092Profs*7)

c.7232A[C (p.Lys2411Thr) [36]

c.7242A[T (p.Ser2414Ser)

BRCA2

BRCA2

BRCA2

c.7878G[C (p.Trp2626Cys) [21, 36]

c.7977-1G[C

c.8167G[C (p.Asp2723His) [29, 36]

c.8243G[A (p.Gly2748Asp) [30]

c.8297del (p.Thr2766Asnfs*11)

c.8377G[A (p.Gly2793Arg) [36]

BRCA2

BRCA2

BRCA2

BRCA2

BRCA2

BRCA2

c.7626G[A (p.Thr2542Thr)

c.6100C[T (p.Arg2034Cys) [48]

BRCA2

BRCA2

c.6037A[T (p.Lys2013*)

BRCA2

c.7558C[T (p.Arg2520*)

c.3922G[T (p.Glu1308*) c.4965C[G (p.Tyr1655*)

BRCA2 BRCA2

c.7618-1G[A

c.3264T[C (p.Pro1088Pro)

BRCA2

BRCA2

c.2883G[A (p.Gln961Gln)

BRCA2

BRCA2

c.1938C[T (p.Ser646Ser)

c.2808_2811del (p.Ala938Profs*21)

BRCA2

BRCA2

c.1832C[A (p.Ser611*)

c.1929del (p.Arg645Glufs*15)

BRCA2

c.1813dupA (p.Ile605Asnfs*11)

BRCA2

BRCA2

c.517-19C[T

c.631?2T[G

BRCA2

BRCA2

c.5576C[G (p.Pro1859Arg) [28]

c.5467?1G[A [46]

BRCA1

c.125A[G (p.Tyr42Cys) [29, 47]

c.5406?8T[C

BRCA1

BRCA1

c.5277?1G[A c.5324T[G (p.Met1775Arg) [27]

BRCA1 BRCA1

BRCA2

Variant

Gene

Table 5 continued

Pathogenic

Pathogenic

Pathogenic

Pathogenic

Pathogenic

Likely Pathogenic (Hypomorphic)

Benign

Pathogenic

Pathogenic

Benign

Benign

Pathogenic

Benign

Pathogenic

Pathogenic Pathogenic

Benign

Benign

Pathogenic

Benign

Pathogenic

Pathogenic

Pathogenic

Pathogenic

Benign

Benign

Benign

Pathogenic

Pathogenic

Benign

Pathogenic Pathogenic

Prior classification

79

100

58

100

98

82

100

100

100

77

87

100

100

87

100 100

100

100

100

100

100

95

100

100

100

100

97

100

54

100

100 100

# probands analyzed

-4.54

-3.19

-4.41

-2.71

-11.75

-7.56

7.45

-3.43

-0.43

-11.64

-5.38

-0.03

6.1

-7.76

2.75 -0.7

9.54

5.78

2.55

5.85

-2.41

-8.7

-0.55

-3.19

8.83

5.8

-26.42

-16.55

-17.49

-8.16

-22.6 -24.78

1.13

3.15

0.16

3.56

-5.51

-1.65

13.73

2.85

5.87

-6.22

0.34

6.24

12.37

-1.84

8.77 5.54

15.87

11.99

8.68

12.17

3.7

-2.67

5.52

3.18

14.87

12.15

-16.58

-6.77

-10.04

1.65

-13.21 -15.17

3.9

10.01

0.75

10.59

2.65

3.52

13.4

9.33

12.05

-0.84

5.33

12.5

16.19

3.3

12.86 12.08

14.29

16.05

12.83

17.39

10.35

4.58

12.31

9.72

14.12

16.05

14.59

20.5

2.05

22.17

17.76 16.20

8.52

15.18

5.09

15.65

8.2

8.79

18.56

14.87

16.99

4.30

10.33

17.87

21.13

8.61

18 17.16

19.31

21.12

18.05

22.16

15.78

9.88

17.44

15

19.1

21.03

21.29

26.82

7.42

28.28

24.56 22.92

5.00 percentile

0.05 percentile

95.0 percentile

99.95 percentile

Benign control history weighting score

Pathogenic control history weighting score

-9.68

-23.84

-11.02

-7.07

-25.26

2.22

27.44

-17.54

-3.06

8.76

16.2

-6.31

21.59

-10.84

-7.76 -3.13

22.4

26.38

-3.99

26.31

-15.93

-14.65

-15.15

-13.19

22.41

30.03

31.17

-25.56

-26.29

31.09

-43.26 -43.23

Variant history Weighting score

Pathogenic

Pathogenic

Pathogenic

Pathogenic

Pathogenic

Not Callable

Benign

Pathogenic

Pathogenic

Benign

Benign

Pathogenic

Benign

Pathogenic

Pathogenic Pathogenic

Benign

Benign

Pathogenic

Benign

Pathogenic

Pathogenic

Pathogenic

Pathogenic

Benign

Benign

Benign

Pathogenic

Pathogenic

Benign

Pathogenic Pathogenic

History weighting call

Breast Cancer Res Treat (2014) 147:119–132 127

123

123

c.9285C[G (p.Asp3095Glu) [30, 36, 50]

c.9292T[C (p.Tyr3098His) [36]

c.9371A[T (p.Asn3124Ile) [36, 50]

c.9501?9A[C

c.9502-12T[G

c.9649-20C[T

c.9976A[T (p.Lys3326*) [25]

c.10110G[A (p.Arg3370Arg)

BRCA2

BRCA2

BRCA2

BRCA2

BRCA2

BRCA2

BRCA2

c.9154C[T (p.Arg3052Trp) [30, 36]

BRCA2

BRCA2

c.8917C[T (p.Arg2973Cys) [36]

c.9004G[A (p.Glu3002Lys) [36, 50]

BRCA2

c.8851G[A (p.Ala2951Thr) [31]

BRCA2

BRCA2

c.8537_8538del (p.Glu2846Glyfs*22)

c.8567A[C (p.Glu2856Ala) [36]

BRCA2

c.8487?1G[A [49] c.8487?8G[A

BRCA2 BRCA2

BRCA2

Variant

Gene

Table 5 continued

Benign

Benign

Benign

Benign

Benign

Pathogenic

Benign

Pathogenic

Pathogenic

Pathogenic

Benign

Benign

Benign

Pathogenic

Pathogenic Benign

Prior classification

100

100

100

100

100

100

100

72

78

100

100

100

100

100

61 66

# probands analyzed

5.36

2.63

1.47

-0.64

-2.31

-8.88

3.65

-7.36

-7.1

-8.27

-3.29

3.28

5.2

-2.59

-5.51 -4.6

11.82

8.81

7.56

5.5

3.92

-2.83

9.8

-2.08

-1.38

-1.98

2.85

9.22

11.54

3.68

-0.59 0.73

16.03

13.48

12.13

12.13

10.79

5.36

14.54

0.93

2.11

5.68

10.1

15.19

15.83

10.43

0.54 0.86

21.05

19.21

17.35

17.24

16.17

10.78

19.78

5.64

7.27

11.24

15.31

20.20

20.82

15.83

4.94 5.52

5.00 percentile

0.05 percentile

95.0 percentile

99.95 percentile

Benign control history weighting score

Pathogenic control history weighting score

28.91

26.82

22.69

18.87

23.01

-13.55

30.22

-8.29

-17.4

-10.48

23.63

24.72

26.9

-14.97

-12.59 8.37

Variant history Weighting score

Benign

Benign

Benign

Benign

Benign

Pathogenic

Benign

Pathogenic

Pathogenic

Pathogenic

Benign

Benign

Benign

Pathogenic

Pathogenic Benign

History weighting call

128 Breast Cancer Res Treat (2014) 147:119–132

Breast Cancer Res Treat (2014) 147:119–132

sample size used for the development of the history weighting algorithm allowed for probabilities to be estimated from observed proportions within a clinical population, analysis of a broader range of ethnicities, and cancer types/combinations, as well as definition of age ranges by individual years. In addition, the prior logistic regression model was not corrected for changes in ascertainment bias over time. We have observed that year of testing was significantly associated with reporting of cancer history information. This may be due, in part, to issues such as types of providers ordering testing or personal and family history requirements for insurance coverage. The history weighting algorithm corrects for this association by

129

selecting controls from within the same testing time period as the proband. Another key difference between the history weighting algorithm and the logistic regression model is the establishment of minimum proband numbers required for variant-specific analysis. Proband minimums were significantly higher for BRCA2 than BRCA1 due to the lower penetrance of BRCA2 mutations. This lower penetrance causes personal and family history severities of pathogenic BRCA2 mutations to more closely resemble those of benign controls when compared to BRCA1 pathogenic mutations. Proband minimums for both genes were also required to be higher for pathogenic calls in comparison with benign calls

Fig. 3 History weighting algorithm graphs illustrating algorithm calls generated for select BRCA1 and BRCA2 variants. Pathogenic (red) and benign (green) control distributions with corresponding percentile threshold lines and the variant-specific history weighting scores 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

123

130

due to the high a priori likelihood that an observed VUS was actually benign. Once proband minimums were established using simulated variants, testing against a dataset of true variants was performed. One hundred trials were run for each variant. The PPV and NPV were [0.999. Validation of the algorithm by testing BRCA1 and BRCA2 simulated and true variants with all tested probands not represented in previous datasets, and thus independent of algorithm development and testing, also resulted in high PPV and NPV, indicating that classification calls made by the algorithm are highly predictive of clinical effect. This model assumes that pathogenic mutations within the same gene confer identical risk. However, some alleles may have significantly lower penetrance in comparison with other pathogenic mutations. Although not designed to detect these lower penetrance alleles, the algorithm is able to distinguish some lower penetrance alleles from other more typical pathogenic mutations. The BRCA1 and BRCA2 suspected pathogenic mutations p.Arg1699Gln and p.Trp2626Cys, respectively, have segregation and/or functional evidence indicating that these mutations represent lower penetrance hypomorphic alleles [12, 21–24, 32– 34]. Both mutations were classified as ‘‘Not Callable’’ by the history weighting algorithm indicating that they are distinct from both the pathogenic and benign control variant populations (Fig. 3). p.Trp2626Cys was previously classified as a pathogenic mutation based on multifactorial and functional analyses [10, 35, 36]. The history weighting algorithm call is consistent with this classification, in that it indicates that p.Trp2626Cys personal and family history severity is distinctly higher than that of the benign control variant population. However, the penetrance may be lower than that of a typical pathogenic BRCA2 mutation. Thus, the algorithm adds further refinement to the pathogenicity of p.Trp2626Cys. There is insufficient data to determine the minimum penetrance level that is distinguishable from a ‘‘Benign’’ call by the algorithm. In addition, some variants have the potential to be scored as ‘‘Not Callable’’ due to insufficient data rather than lower penetrance. All reclassification methods have weaknesses and the potential to result in erroneous classifications if utilized without consideration of all available evidence. Analysis of the collective data is required for the correct classification of p.Lys3326* (BRCA2), which is located near the c-terminus of the protein. While most nonsense mutations are interpreted as ‘‘Pathogenic’’ according to standard variant classification guidelines, empirical evidence indicates that this variant is benign [25]. p.Lys3326* does not segregate with cancer, and has been identified at a significant frequency in normal controls. History weighting analysis of p.Lys3326* provides additional support for the benign classification of this variant.

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Breast Cancer Res Treat (2014) 147:119–132

The BRCA1/BRCA2 history weighting algorithm is a powerful tool allowing for the reclassification of hundreds of variants and better identification and clinical management of at-risk patients. The use of this algorithm and other reclassification tools has resulted in a decline in VUS rates within our laboratory to approximately 2 %. Since algorithm implementation, our laboratory has reclassified [450 variants from either ‘‘VUS’’ into a more definitive clinical category or from ‘‘Likely Benign’’ to ‘‘Benign’’ or from ‘‘Likely Pathogenic’’ to ‘‘Pathogenic.’’ The algorithm not only has the capability of distinguishing high-risk variants from those conveying no significant cancer risk, but in some cases, also has the ability to identify alleles that may be associated with significant, albeit lower, cancer risks. While the history weighting algorithm was developed for analysis of the BRCA1 and BRCA2 genes, the underlying premise is expected to be applicable to other cancer-associated genes as well as other dominant, non-cancer-related diseases. Acknowledgments We would like to thank Kirstin Roundy for assistance with manuscript editing and submission. Conflict of interest All authors are employees of Myriad Genetics, Inc. and Myriad Genetic Laboratories, Inc. and receive salaries and stock options as compensation.

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