Recently, we demonstrated that the qualitative American College of Medical Genetics and Genomics/Association for Medical Pathology (ACMG/AMP) guidelines for evaluation of Mendelian disease gene ...variants are fundamentally compatible with a quantitative Bayesian formulation. Here, we show that the underlying ACMG/AMP “strength of evidence categories” can be ed into a point system. These points are proportional to Log(odds), are additive, and produce a system that recapitulates the Bayesian formulation of the ACMG/AMP guidelines. The strengths of this system are its simplicity and that the connection between point values and odds of pathogenicity allows empirical calibration of the strength of evidence for individual data types. Weaknesses include that a narrow range of prior probabilities is locked in and that the Bayesian nature of the system is inapparent. We conclude that a points‐based system has the practical attribute of user‐friendliness and can be useful so long as the underlying Bayesian principles are acknowledged.
Building from our Bayesian formulation of the American College of Medical Genetics and Genomics/Association for Medical Pathology (ACMG/AMP) sequence variant classification guidelines, we have now derived a point system for variant classification. Two key features are (1) the points are proportional to Log(odds), and (2) the classification thresholds are derived from the probabilistic thresholds of the parent ACMG/AMP guidelines. We conclude that a points‐based system has the practical attribute of user‐friendliness and can be useful so long as the underlying Bayesian principles are acknowledged.
We evaluated the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) variant pathogenicity guidelines for internal consistency and compatibility with ...Bayesian statistical reasoning.
The ACMG/AMP criteria were translated into a naive Bayesian classifier, assuming four levels of evidence and exponentially scaled odds of pathogenicity. We tested this framework with a range of prior probabilities and odds of pathogenicity.
We modeled the ACMG/AMP guidelines using biologically plausible assumptions. Most ACMG/AMP combining criteria were compatible. One ACMG/AMP likely pathogenic combination was mathematically equivalent to pathogenic and one ACMG/AMP pathogenic combination was actually likely pathogenic. We modeled combinations that include evidence for and against pathogenicity, showing that our approach scored some combinations as pathogenic or likely pathogenic that ACMG/AMP would designate as variant of uncertain significance (VUS).
By transforming the ACMG/AMP guidelines into a Bayesian framework, we provide a mathematical foundation for what was a qualitative heuristic. Only 2 of the 18 existing ACMG/AMP evidence combinations were mathematically inconsistent with the overall framework. Mixed combinations of pathogenic and benign evidence could yield a likely pathogenic, likely benign, or VUS result. This quantitative framework validates the approach adopted by the ACMG/AMP, provides opportunities to further refine evidence categories and combining rules, and supports efforts to automate components of variant pathogenicity assessments.
The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) clinical variant interpretation guidelines established criteria for different types of evidence. ...This includes the strong evidence codes PS3 and BS3 for "well-established" functional assays demonstrating a variant has abnormal or normal gene/protein function, respectively. However, they did not provide detailed guidance on how functional evidence should be evaluated, and differences in the application of the PS3/BS3 codes are a contributor to variant interpretation discordance between laboratories. This recommendation seeks to provide a more structured approach to the assessment of functional assays for variant interpretation and guidance on the use of various levels of strength based on assay validation.
The Clinical Genome Resource (ClinGen) Sequence Variant Interpretation (SVI) Working Group used curated functional evidence from ClinGen Variant Curation Expert Panel-developed rule specifications and expert opinions to refine the PS3/BS3 criteria over multiple in-person and virtual meetings. We estimated the odds of pathogenicity for assays using various numbers of variant controls to determine the minimum controls required to reach moderate level evidence. Feedback from the ClinGen Steering Committee and outside experts were incorporated into the recommendations at multiple stages of development.
The SVI Working Group developed recommendations for evaluators regarding the assessment of the clinical validity of functional data and a four-step provisional framework to determine the appropriate strength of evidence that can be applied in clinical variant interpretation. These steps are as follows: (1) define the disease mechanism, (2) evaluate the applicability of general classes of assays used in the field, (3) evaluate the validity of specific instances of assays, and (4) apply evidence to individual variant interpretation. We found that a minimum of 11 total pathogenic and benign variant controls are required to reach moderate-level evidence in the absence of rigorous statistical analysis.
The recommendations and approach to functional evidence evaluation described here should help clarify the clinical variant interpretation process for functional assays. Further, we hope that these recommendations will help develop productive partnerships with basic scientists who have developed functional assays that are useful for interrogating the function of a variety of genes.
Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational ...predictors as “supporting” level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (supporting, moderate, strong, very strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tool’s scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying moderate and several reached strong evidence levels. One tool reached very strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.
We developed an approach to calibrate computational predictors to the American College of Medical Genetics and Genomics and Association for Molecular Pathology guidelines for clinical variant classification. We observed that predictors can provide much stronger evidence for variant pathogenicity/benignity than previously thought and propose updated recommendations for their clinical use.
An international group of cancer geneticists review the level of evidence for the association of gene variants with the risk of breast cancer. It is difficult to draw firm conclusions from the data ...because of ascertainment bias and the lack of data from large populations.
Advances in sequencing technology have made multigene testing, or “panel testing,” a practical option when looking for genetic variants that may be associated with a risk of breast cancer. In June 2013, the U.S. Supreme Court
1
invalidated specific claims made by Myriad Genetics with respect to the patenting of the genomic DNA sequence of
BRCA1
and
BRCA2
. Other companies immediately began to offer panel tests for breast cancer genes that included
BRCA1
and
BRCA2
. The subsequent flourishing of gene-panel testing services (Table 1, and Table S1 in the Supplementary Appendix, available with the full text of this article at . . .
Genetic testing of cancer susceptibility genes is now widely applied in clinical practice to predict risk of developing cancer. In general, sequence-based testing of germline DNA is used to determine ...whether an individual carries a change that is clearly likely to disrupt normal gene function. Genetic testing may detect changes that are clearly pathogenic, clearly neutral, or variants of unclear clinical significance. Such variants present a considerable challenge to the diagnostic laboratory and the receiving clinician in terms of interpretation and clear presentation of the implications of the result to the patient. There does not appear to be a consistent approach to interpreting and reporting the clinical significance of variants either among genes or among laboratories. The potential for confusion among clinicians and patients is considerable and misinterpretation may lead to inappropriate clinical consequences. In this article we review the current state of sequence-based genetic testing, describe other standardized reporting systems used in oncology, and propose a standardized classification system for application to sequence-based results for cancer predisposition genes. We suggest a system of five classes of variants based on the degree of likelihood of pathogenicity. Each class is associated with specific recommendations for clinical management of at-risk relatives that will depend on the syndrome. We propose that panels of experts on each cancer predisposition syndrome facilitate the classification scheme and designate appropriate surveillance and cancer management guidelines. The international adoption of a standardized reporting system should improve the clinical utility of sequence-based genetic tests to predict cancer risk. Hum Mutat 29(11), 1282-1291, 2008.
While mobile elements are largely inactive in healthy somatic tissues, increased activity has been found in cancer tissues, with significant variation among different cancer types. In addition to ...insertion events, mobile elements have also been found to mediate many structural variation events in the genome. Here, to better understand the timing and impact of mobile element insertions and associated structural variants in cancer, we examined their activity in longitudinal samples of four metastatic breast cancer patients. We identified 11 mobile element insertions or associated structural variants and found that the majority of these occurred early in tumor progression. Most of the variants impact intergenic regions; however, we identified a translocation interrupting MAP2K4 involving Alu elements and a deletion in YTHDF2 involving mobile elements that likely inactivate reported tumor suppressor genes. The high variant allele fraction of the translocation, the loss of the other copy of MAP2K4, the recurrent loss-of-function mutations found in this gene in other cancers, and the important function of MAP2K4 indicate that this translocation is potentially a driver mutation. Overall, using a unique longitudinal dataset, we find that most variants are likely passenger mutations in the four patients we examined, but some variants impact tumor progression.
The tumor suppressor gene TP53 is frequently mutated in human cancers. More than 75% of all mutations are missense substitutions that have been extensively analyzed in various yeast and human cell ...assays. The International Agency for Research on Cancer (IARC) TP53 database (www‐p53.iarc.fr) compiles all genetic variations that have been reported in TP53. Here, we present recent database developments that include new annotations on the functional properties of mutant proteins, and we perform a systematic analysis of the database to determine the functional properties that contribute to the occurrence of mutational “hotspots” in different cancer types and to the phenotype of tumors. This analysis showed that loss of transactivation capacity is a key factor for the selection of missense mutations, and that difference in mutation frequencies is closely related to nucleotide substitution rates along TP53 coding sequence. An interesting new finding is that in patients with an inherited missense mutation, the age at onset of tumors was related to the functional severity of the mutation, mutations with total loss of transactivation activity being associated with earlier cancer onset compared to mutations that retain partial transactivation capacity. Furthermore, 80% of the most common mutants show a capacity to exert dominant‐negative effect (DNE) over wild‐type p53, compared to only 45% of the less frequent mutants studied, suggesting that DNE may play a role in shaping mutation patterns. These results provide new insights into the factors that shape mutation patterns and influence mutation phenotype, which may have clinical interest. Hum Mutat 28(6), 622–629, 2007. Published 2007 Wiley‐Liss, Inc.
Mutation screening of the breast and ovarian cancer–predisposition genes
BRCA1 and
BRCA2 is becoming an increasingly important part of clinical practice. Classification of rare nontruncating sequence ...variants in these genes is problematic, because it is not known whether these subtle changes alter function sufficiently to predispose cells to cancer development. Using data from the Myriad Genetic Laboratories database of nearly 70,000 full-sequence tests, we assessed the clinical significance of 1,433 sequence variants of unknown significance (VUSs) in the BRCA genes. Three independent measures were employed in the assessment: co-occurrence in
trans of a VUS with known deleterious mutations; detailed analysis, by logistic regression, of personal and family history of cancer in VUS-carrying probands; and, in a subset of probands, an analysis of cosegregation with disease in pedigrees. For each of these factors, a likelihood ratio was computed under the hypothesis that the VUSs were equivalent to an “average” deleterious mutation, compared with neutral, with respect to risk. The likelihood ratios derived from each component were combined to provide an overall assessment for each VUS. A total of 133 VUSs had odds of at least 100:1 in favor of neutrality with respect to risk, whereas 43 had odds of at least 20:1 in favor of being deleterious. VUSs with evidence in favor of causality were those that were predicted to affect splicing, fell at positions that are highly conserved among BRCA orthologs, and were more likely to be located in specific domains of the proteins. In addition to their utility for improved genetics counseling of patients and their families, the global assessment reported here will be invaluable for validation of functional assays, structural models, and
in silico analyses.
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