Data may be interpreted or applied differently resulting in discordant results.

"Although all testing laboratories use guidelines from the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP) as the foundation for variant classification, there are still differences in how laboratories interpret and ultimately classify some variants."

Mundt E, et al. Complexities of variant classification in clinical hereditary cancer genetic testing. JCO 2017; 35:3796-99.

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In silico algorithms do not provide reliable evidence regarding the clinical significance of missense variants in genes associated with hereditary cancer.

"In a clinical laboratory, samples are tested due to clinical suspicion of hereditary cancer risk. Accurate variant classification using multiple lines of evidence is vital for appropriate clinical management based on NCCN guidelines. In this setting, incorrect variant classification based on in silico tools comes with more immediate consequences to the patient."

Kerr ID, et al. Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes. J Community Genet 2017. doi:10.1007/s12687-016-0289-x.

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Clinical History Weighting Algorithm aids in the reclassification of variants.

"The need for improved variant classification tools has become more urgent as hereditary cancer genetic testing is increasingly performed with large panels of genes, or even entire exomes, rather than smaller subsets of genes associated with individual conditions like Lynch syndrome (LS). The use of panels including genes for multiple hereditary cancer syndromes has already demonstrated that pathogenic mutations in LS-associated genes are frequently identified in individuals who might not have been ascertained for LS testing based on their personal and family histories of colorectal cancer and endometrial cancer. While this validates the benefits of a broader pan-cancer panel approach to testing, it is inevitable that analysis of more genes leads to the identification of more VUSs [variants of uncertain significance]."

Morris B, et al. Classification of genetic variants in genes associated with Lynch syndrome using a clinical history weighting algorithm. BMC Genetics (2016) 17:99.

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Clinical data for many variants contradicts expected pathogenic classification.

"[O]ur laboratory has identified a subset of such variants in hereditary cancer genes for which compelling contradictory evidence emerged after the initial evaluation following the first observation of the variant. Three representative examples of variants in BRCA1, BRCA2 and MSH2 that are predicted to disrupt splicing, prematurely truncate the protein, or remove the start codon were evaluated for pathogenicity by analyzing clinical data with multiple classification algorithms. Available clinical data for all three variants contradicts the expected pathogenic classification. These variants illustrate potential pitfalls associated with standard approaches to variant classification as well as the challenges associated with monitoring data, updating classifications, and reporting potentially contradictory interpretations to the clinicians responsible for translating test outcomes to appropriate clinical action."

Rosenthal ET, et al. Exceptions to the rule: Case studies in the prediction of pathogenicity for genetic variants in hereditary cancer genes. Clinical Genetics 2015; 88(6): 533-41.

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Myriad identifies approximately 75 new variants every day, including 5-10 new BRCA1 and BRCA2 variants.

"Although the VUS rate for Myriad’s myRisk® panel was 41.7% in 2014, Myriad’s experience and investment in variant classification allowed for the development and improvement of multiple classification tools, leading to a substantial reduction in the VUS rate by 2016. The VUS rate is currently 28.6%. Although new variants are seen daily in genes on the myRisk® panel, it is expected that the VUS rate will continue to decrease with the current tools now utilized for variant classification at Myriad."

Mundt E and Chen D. Lowering the rate of variants of uncertain significance on Myriad’s myRisk® hereditary cancer panel. White paper June 2016.

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Interpretation of genetic data is challenging.

"Because the primary aim of clinical testing is to provide results to inform medical management, a variant classification program that offers timely, accurate, confident and cost-effective interpretation of variants should be an integral component of the laboratory process. Here we describe the components of our laboratory's current variant classification program (VCP), based on 20 years of experience and over one million samples tested, using the BRCA1/2 genes as a model. Our VCP has lowered the percentage of tests in which one or more BRCA1/2 variants of uncertain significance (VUSs) are detected to 2.1% in the absence of a pathogenic mutation, demonstrating how the coordinated application of resources toward classification and reclassification significantly impacts the clinical utility of testing."

Eggington JM, et al. A comprehensive laboratory-based program for classification of variants of uncertain significance in hereditary cancer genes. Clinical Genetics 2014; 86(3):229-37.

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Accurately classifying variants as pathogenic or benign is critical for improved patient management.

"The history weighting algorithm [at Myriad] is a powerful tool that accurately assigns actionable clinical classifications to variants of uncertain clinical significance. While being developed for reclassification of BRCA1 and BRCA2 variants, the history weighting algorithm is expected to be applicable to other cancer- and non-cancer-related genes."

Pruss D, et al. Development and validation of a new algorithm for the reclassification of genetic variants identified in the BRCA1 and BRCA2 genes. Breast Cancer Res Treat 2014; 147:119–32.

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Functionality of curated missense mutations in tumor suppressor genes dependent upon user knowledge and process when using existing algorithms SIFT, Align-GVGD, PolyPhen-2 and Xvar.

"Accurately predicting the impact of missense mutations on protein function depends on the algorithm used, the type of sequence alignment provided, and on the number of sequences in the alignment. In addition to problems of interpretation there are technical difficulties as well. In our experience, when simply submitting a list of missense mutations to an algorithm the user must be able to: (1) manipulate the input format specified by each algorithm, (2) build an optimal protein sequence alignment, if required, (3) be knowledgeable of Unix system commands, (4) interpret server error messages, and (5) transform the output to a working format for further studies."

Hicks S, et al. Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed. Human Mutation 2011, 32(6): 661–8.

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