The susceptibility gene for ataxia telangiectasia,
ATM, is also an intermediate-risk breast-cancer-susceptibility gene. However, the spectrum and frequency distribution of
ATM mutations that confer ...increased risk of breast cancer have been controversial. To assess the contribution of rare variants in this gene to risk of breast cancer, we pooled data from seven published
ATM case-control mutation-screening studies, including a total of 1544 breast cancer cases and 1224 controls, with data from our own mutation screening of an additional 987 breast cancer cases and 1021 controls. Using an in silico missense-substitution analysis that provides a ranking of missense substitutions from evolutionarily most likely to least likely, we carried out analyses of protein-truncating variants, splice-junction variants, and rare missense variants. We found marginal evidence that the combination of
ATM protein-truncating and splice-junction variants contribute to breast cancer risk. There was stronger evidence that a subset of rare, evolutionarily unlikely missense substitutions confer increased risk. On the basis of subset analyses, we hypothesize that rare missense substitutions falling in and around the FAT, kinase, and FATC domains of the protein may be disproportionately responsible for that risk and that a subset of these may confer higher risk than do protein-truncating variants. We conclude that a comparison between the graded distributions of missense substitutions in cases versus controls can complement analyses of truncating variants and help identify susceptibility genes and that this approach will aid interpretation of the data emerging from new sequencing technologies.
Variants in the DNA mismatch repair (MMR) gene MSH6, identified in individuals suspected of Lynch syndrome, are difficult to classify owing to the low cancer penetrance of defects in that gene. This ...not only obfuscates personalized health care but also the development of a rapid and reliable classification procedure that does not require clinical data.
The complete in vitro MMR activity (CIMRA) assay was calibrated against clinically classified MSH6 variants and, employing Bayes' rule, integrated with computational predictions of pathogenicity. To enable the validation of this two-component classification procedure we have employed a genetic screen to generate a large set of inactivating Msh6 variants, as proxies for pathogenic variants.
The genetic screen-derived variants established that the two-component classification procedure displays high sensitivities and specificities. Moreover, these inactivating variants enabled the direct reclassification of human variants of uncertain significance (VUS) as (likely) pathogenic.
The two-component classification procedure and the genetic screens provide complementary approaches to rapidly and cost-effectively classify the large majority of human MSH6 variants. The approach followed here provides a template for the classification of variants in other disease-predisposing genes, facilitating the translation of personalized genomics into personalized health care.
Multigene panel testing has led to an increase in the number of variants of uncertain significance identified in the TP53 gene, associated with Li‐Fraumeni syndrome. We previously developed a ...quantitative model for predicting the pathogenicity of P53 missense variants based on the combination of calibrated bioinformatic information and somatic to germline ratio. Here, we extended this quantitative model for the classification of P53 predicted missense variants by adding new pieces of evidence (personal and family history parameters, loss‐of‐function results, population allele frequency, healthy individual status by age 60, and breast tumor pathology). We also annotated which missense variants might have an effect on splicing based on bioinformatic predictions. This updated model plus annotation led to the classification of 805 variants into a clinically relevant class, which correlated well with existing ClinVar classifications, and resolved a large number of conflicting and uncertain classifications. We propose this model as a reliable approach to TP53 germline variant classification and emphasize its use in contributing to optimize TP53‐specific ACMG/AMP guidelines.
We propose the value of this novel TP53 multifactorial likelihood model for the following aspects:
(1)
Providing evidence for the utility of new components not yet considered by TP53‐specific ACMG/AMP guidelines,
(2)
Highlighting areas where the guidelines can be further optimized in subsequent versions,
(3)
Comparison of the two approaches to flag variants with contradictory classifications between both strategies for further investigation.
Clinical interpretation of germline missense variants represents a major challenge, including those in the TP53 Li–Fraumeni syndrome gene. Bioinformatic prediction is a key part of variant ...classification strategies. We aimed to optimize the performance of the Align‐GVGD tool used for p53 missense variant prediction, and compare its performance to other bioinformatic tools (SIFT, PolyPhen‐2) and ensemble methods (REVEL, BayesDel). Reference sets of assumed pathogenic and assumed benign variants were defined using functional and/or clinical data. Area under the curve and Matthews correlation coefficient (MCC) values were used as objective functions to select an optimized protein multisequence alignment with best performance for Align‐GVGD. MCC comparison of tools using binary categories showed optimized Align‐GVGD (C15 cut‐off) combined with BayesDel (0.16 cut‐off), or with REVEL (0.5 cut‐off), to have the best overall performance. Further, a semi‐quantitative approach using multiple tiers of bioinformatic prediction, validated using an independent set of nonfunctional and functional variants, supported use of Align‐GVGD and BayesDel prediction for different strength of evidence levels in ACMG/AMP rules. We provide rationale for bioinformatic tool selection for TP53 variant classification, and have also computed relevant bioinformatic predictions for every possible p53 missense variant to facilitate their use by the scientific and medical community.
Comparison of the predictive performance of different bioinformatic tools provides rationale for bioinformatic tool selection for TP53 variant classification. A semi‐quantitative approach using multiple tiers of bioinformatic prediction, validated using an independent set of non‐functional and functional variants, supports use of Align‐GVGD and BayesDel prediction for different strength of evidence levels in ACMG/AMP rules. Relevant bioinformatic predictions have been computed for every possible p53 missense variant to facilitate TP53 variant bioinformatic analysis by the scientific and medical community.
Functional assays provide important evidence for classifying the disease significance of germline variants in DNA mismatch repair genes. Numerous laboratories, including our own, have developed ...functional assays to study mismatch repair gene variants. However, previous assays are limited due to the model system employed, the manner of gene expression, or the environment in which function is assessed. Here, we developed a human cell‐based approach for testing the function of variants of uncertain significance (VUS) in the MLH1 gene. Using clustered regularly interspaced short palindromic repeats gene editing, we knocked in MLH1 VUS into the endogenous MLH1 loci in human embryonic stem cells. We examined their impact on RNA and protein, including their ability to prevent microsatellite instability and instigate a DNA damage response. A statistical clustering analysis determined the range of functions associated with known pathogenic or benign variants, and linear regression was performed using existing odds in favor of pathogenicity scores for these control variants to calibrate our functional assay results. By converting the functional outputs into a single odds in favor of pathogenicity score, variant classification expert panels can use these results to readily reassess these VUS. Ultimately, this information will guide proper diagnosis and disease management for suspected Lynch syndrome patients.
Rare inactivating mutations in BRCA1, BRCA2, ATM, TP53 and CHEK2 confer relative risks for breast cancer between about 2 and more than 10, but more common variants in these genes are generally ...considered of little or no clinical significance. Under the polygenic model for breast cancer carriers of multiple low-penetrance alleles are at high risk, but few such alleles have been reliably identified. We analysed 1037 potentially functional single nucleotide polymorphisms (SNPs) in candidate cancer genes in 473 women with two primary breast cancers and 2463 controls. Twenty-five of these SNPs were in BRCA1, BRCA2, ATM, TP53 and CHEK2. Among the 1037 SNPs there were a few significant findings, but hardly more than would be expected in this large experiment. There was, however, a significant trend in risk with increasing numbers of variant alleles for the 25 SNPs in BRCA1, BRCA2, ATM, TP53 and CHEK2 (Ptrend = 0.005). For the 21 of these with minor allele frequency <10% this trend was highly significant (Ptrend = 0.00004, odds ratio for 3 or more SNPs = 2.90, 95% CI 1.69–4.97). The individual effects of most of these risk alleles were undetectably small even in this well powered study, but the risk conferred by multiple variants is readily detectable and makes a substantial contribution to susceptibility. A risk score incorporating a suitably weighted sum of all potentially functional variants in these and a few other candidate genes may provide clinically useful identification of women at high genetic risk.
Most cancer susceptibility genes function as tumor suppressors; accordingly, the focus of mutation screening in breast cancer families has been to identify protein-truncating mutations. However, it ...is now clear that, for some breast cancer susceptibility genes, a significant proportion of the burden of disease comes from rare missense substitutions. Among genes that have been extensively evaluated, BRCA1, BRCA2, PALB2 and BRIP1 stand as examples where the majority of mutations lead to protein truncation;TP53 provides a counter example, where the majority of pathogenic variants are missense substitutions. In ATM and CHEK2, missense substitutions are probably equally or more important in terms of their frequency and attributable risk. Therefore, ongoing efforts to identify new susceptibility genes should not ignore missense variation.
The availability of disease‐specific genomic data is critical for developing new computational methods that predict the pathogenicity of human variants and advance the field of precision medicine. ...However, the lack of gold standards to properly train and benchmark such methods is one of the greatest challenges in the field. In response to this challenge, the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods. As part of the CAGI‐5 challenge, we evaluated the performance of 18 submissions and three additional methods in predicting the pathogenicity of single nucleotide variants (SNVs) in checkpoint kinase 2 (CHEK2) for cases of breast cancer in Hispanic females. As part of the assessment, the efficacy of the analysis method and the setup of the challenge were also considered. The results indicated that though the challenge could benefit from additional participant data, the combined generalized linear model analysis and odds of pathogenicity analysis provided a framework to evaluate the methods submitted for SNV pathogenicity identification and for comparison to other available methods. The outcome of this challenge and the approaches used can help guide further advancements in identifying SNV‐disease relationships.
ABSTRACT
Clinical mutation screening of the cancer susceptibility genes BRCA1 and BRCA2 generates many unclassified variants (UVs). Most of these UVs are either rare missense substitutions or ...nucleotide substitutions near the splice junctions of the protein coding exons. Previously, we developed a quantitative method for evaluation of BRCA gene UVs—the “integrated evaluation”—that combines a sequence analysis‐based prior probability of pathogenicity with patient and/or tumor observational data to arrive at a posterior probability of pathogenicity. One limitation of the sequence analysis‐based prior has been that it evaluates UVs from the perspective of missense substitution severity but not probability to disrupt normal mRNA splicing. Here, we calibrated output from the splice‐site fitness program MaxEntScan to generate spliceogenicity‐based prior probabilities of pathogenicity for BRCA gene variants; these range from 0.97 for variants with high probability to damage a donor or acceptor to 0.02 for exonic variants that do not impact a splice junction and are unlikely to create a de novo donor. We created a database http://priors.hci.utah.edu/PRIORS/ that provides the combined missense substitution severity and spliceogenicity‐based probability of pathogenicity for BRCA gene single‐nucleotide substitutions. We also updated the BRCA gene Ex‐UV LOVD, available at http://hci‐exlovd.hci.utah.edu, with 77 re‐evaluable variants.
We have re‐calibrated the native output from a splice site analysis program, MaxEntScan, to generate probabilities that sequence variants in BRCA1 or BRCA2 will be pathogenic because they damage mRNA splicing. Combined with our previous calibration of missense substitution severity (Tavtigian et al., Human Mutation 29: 1342–1354, 2008), we can now provide integrated prior probabilities in favor of pathogenicity that serve as the starting point for clinical classification of variants of unknown significance observed in these genes.
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