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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., (© 2022 Wiley Periodicals LLC.)

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., (© 2021 Wiley Periodicals LLC.)

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 abstracted 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., (© 2020 Wiley Periodicals LLC.)

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., (© 2019 Wiley Periodicals, Inc.)

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification., (© 2019 Wiley Periodicals, Inc.)

Germline pathogenic variants in the TP53 gene cause Li-Fraumeni syndrome, a condition that predisposes individuals to a wide range of cancer types. Identification of individuals carrying a TP53 pathogenic variant is linked to clinical management decisions, such as the avoidance of radiotherapy and use of high-intensity screening programs. The aim of this study was to develop an evidence-based quantitative model that integrates independent in silico data (Align-GVGD and BayesDel) and somatic to germline ratio (SGR), to assign pathogenicity to every possible missense variant in the TP53 gene. To do this, a likelihood ratio for pathogenicity (LR) was derived from each component calibrated using reference sets of assumed pathogenic and benign missense variants. A posterior probability of pathogenicity was generated by combining LRs, and algorithm outputs were validated using different approaches. A total of 730 TP53 missense variants could be assigned to a clinically interpretable class. The outputs of the model correlated well with existing clinical information, functional data, and ClinVar classifications. In conclusion, these quantitative outputs provide the basis for individualized assessment of cancer risk useful for clinical interpretation. In addition, we propose the value of the novel SGR approach for use within the ACMG/AMP guidelines for variant classification., (© 2019 Wiley Periodicals, Inc.)

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., (© 2018 Wiley Periodicals, Inc.)

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., (© 2016 The Authors. **Human Mutation published by Wiley Periodicals, Inc.)

Classification of rare missense substitutions observed during genetic testing for patient management is a considerable problem in clinical genetics. The Bayesian integrated evaluation of unclassified variants is a solution originally developed for BRCA1/2. Here, we take a step toward an analogous system for the mismatch repair (MMR) genes (MLH1, MSH2, MSH6, and PMS2) that confer colon cancer susceptibility in Lynch syndrome by calibrating in silico tools to estimate prior probabilities of pathogenicity for MMR gene missense substitutions. A qualitative five-class classification system was developed and applied to 143 MMR missense variants. This identified 74 missense substitutions suitable for calibration. These substitutions were scored using six different in silico tools (Align-Grantham Variation Grantham Deviation, multivariate analysis of protein polymorphisms [MAPP], MutPred, PolyPhen-2.1, Sorting Intolerant From Tolerant, and Xvar), using curated MMR multiple sequence alignments where possible. The output from each tool was calibrated by regression against the classifications of the 74 missense substitutions; these calibrated outputs are interpretable as prior probabilities of pathogenicity. MAPP was the most accurate tool and MAPP + PolyPhen-2.1 provided the best-combined model (R(2)  = 0.62 and area under receiver operating characteristic = 0.93). The MAPP + PolyPhen-2.1 output is sufficiently predictive to feed as a continuous variable into the quantitative Bayesian integrated evaluation for clinical classification of MMR gene missense substitutions., (© 2012 Wiley Periodicals, Inc.)

Mismatch repair (MMR) gene sequence variants of uncertain clinical significance are often identified in suspected Lynch syndrome families, and this constitutes a challenge for both researchers and clinicians. Multifactorial likelihood model approaches provide a quantitative measure of MMR variant pathogenicity, but first require input of likelihood ratios (LRs) for different MMR variation-associated characteristics from appropriate, well-characterized reference datasets. Microsatellite instability (MSI) and somatic BRAF tumor data for unselected colorectal cancer probands of known pathogenic variant status were used to derive LRs for tumor characteristics using the Colon Cancer Family Registry (CFR) resource. These tumor LRs were combined with variant segregation within families, and estimates of prior probability of pathogenicity based on sequence conservation and position, to analyze 44 unclassified variants identified initially in Australasian Colon CFR families. In addition, in vitro splicing analyses were conducted on the subset of variants based on bioinformatic splicing predictions. The LR in favor of pathogenicity was estimated to be ~12-fold for a colorectal tumor with a BRAF mutation-negative MSI-H phenotype. For 31 of the 44 variants, the posterior probabilities of pathogenicity were such that altered clinical management would be indicated. Our findings provide a working multifactorial likelihood model for classification that carefully considers mode of ascertainment for gene testing., (© 2012 Wiley Periodicals, Inc.)

Lynch syndrome (LS) is caused by germline mutations in DNA mismatch repair (MMR) genes and is the most prevalent hereditary colorectal cancer syndrome. A significant proportion of variants identified in MMR and other common cancer susceptibility genes are missense or noncoding changes whose consequences for pathogenicity cannot be easily interpreted. Such variants are designated as "variants of uncertain significance" (VUS). Management of LS can be significantly improved by identifying individuals who carry a pathogenic variant and thus benefit from screening, preventive, and therapeutic measures. Also, identifying family members that do not carry the variant is important so they can be released from the intensive surveillance. Determining which genetic variants are pathogenic and which are neutral is a major challenge in clinical genetics. The profound mechanistic knowledge on the genetics and biochemistry of MMR enables the development and use of targeted assays to evaluate the pathogenicity of variants found in suspected patients with LS. We describe different approaches for the functional analysis of MMR gene VUS and propose development of a validated diagnostic framework. Furthermore, we call attention to common misconceptions about functional assays and endorse development of an integrated approach comprising validated assays for diagnosis of VUS in patients suspected of LS., (© 2012 Wiley Periodicals, Inc.)

Germline mutations in the PALB2 gene are associated with an increased risk of developing breast cancer but little is known about the frequencies of rare variants in PALB2 and the nature of the variants that influence risk. We selected participants recruited to the Women's Environment, Cancer, and Radiation Epidemiology (WECARE) Study and screened lymphocyte DNA from cases with contralateral breast cancer (n = 559) and matched controls with unilateral breast cancer (n = 565) for PALB2 mutations. Five pathogenic PALB2 mutations were identified among the cases (0.9%) versus none among the controls (P = 0.04). The first-degree female relatives of these five carriers demonstrated significantly higher incidence of breast cancer than relatives of noncarrier cases, indicating that pathogenic PALB2 mutations confer an estimated 5.3-fold increase in risk (95% CI: 1.8-13.2). The frequency of rare (<1% MAF) missense mutations was similar in both groups (23 vs. 21). Our findings confirm in a population-based study setting of women with breast cancer the strong risk associated with truncating mutations in PALB2 that has been reported in family studies. Conversely, there is no evidence from this study that rare PALB2 missense mutations strongly influence breast cancer risk., (© 2012 Wiley Periodicals, Inc.)

Clinical mutation screening of the BRCA1 and BRCA2 genes for the presence of germline inactivating mutations is used to identify individuals at elevated risk of breast and ovarian cancer. Variants identified during screening are usually classified as pathogenic (increased risk of cancer) or not pathogenic (no increased risk of cancer). However, a significant proportion of genetic tests yields variants of uncertain significance (VUS) that have undefined risk of cancer. Individuals carrying these VUS cannot benefit from individualized cancer risk assessment. Recently, a quantitative "posterior probability model" for assessing the clinical relevance of VUS in BRCA1 or BRCA2, which integrates multiple forms of genetic evidence has been developed. Here, we provide a detailed review of this model. We describe the components of the model and explain how these can be combined to calculate a posterior probability of pathogenicity for each VUS. We explain how the model can be applied to public data and provide tables that list the VUS that have been classified as not pathogenic or pathogenic using this method. While we use BRCA1 and BRCA2 VUS as examples, the method can be used as a framework for classification of the pathogenicity of VUS in other cancer genes., (© 2011 Wiley Periodicals, Inc.)

Unclassified sequence variants (UVs) arising from clinical mutation screening of cancer susceptibility genes present a frustrating issue to clinical genetics services and the patients that they serve. We created an open-access database holding missense substitutions from the breast and ovarian cancer susceptibility genes BRCA1 and BRCA2. The main inclusion criterion is that each variant should have been assessed in a published work that used the Bayesian integrated evaluation of unclassified BRCA gene variants. Transfer of data on these substitutions from the original publications to our database afforded an opportunity to analyze the missense substitutions under a single model and to remove inconsistencies that arose during the evolution of the integrated evaluation over the last decade. This analysis also afforded the opportunity to reclassify these missense substitutions according to the recently published IARC 5-Class system. From an initial set of 248 missense substitutions, 31 were set aside due to nonnegligible probability to interfere with splicing. Of the remaining substitutions, 28 fell into one of the two pathogenic classes (IARC Class 4 or 5), 174 fell into one of the two nonpathogenic classes (IARC Class 1 or 2), and 15 remain in IARC Class 3, "Uncertain." The database is available at http://brca.iarc.fr/LOVD., (© 2011 Wiley Periodicals, Inc.)

As genetic testing for predisposition to human diseases has become an increasingly common practice in medicine, the need for clear interpretation of the test results is apparent. However, for many disease genes, including the breast cancer susceptibility genes BRCA1 and BRCA2, a significant fraction of tests results in the detection of a genetic variant for which disease association is not known. The finding of an "unclassified" variant (UV)/variant of uncertain significance (VUS) complicates genetic test reporting and counseling. As these variants are individually rare, a large collaboration of researchers and clinicians will facilitate studies to assess their association with cancer predisposition. It was with this in mind that the ENIGMA consortium (www.enigmaconsortium.org) was initiated in 2009. The membership is both international and interdisciplinary, and currently includes more than 100 research scientists and clinicians from 19 countries. Within ENIGMA, there are presently six working groups focused on the following topics: analysis, clinical, database, functional, tumor histopathology, and mRNA splicing. ENIGMA provides a mechanism to pool resources, exchange methods and data, and coordinately develop and apply algorithms for classification of variants in BRCA1 and BRCA2. It is envisaged that the research and clinical application of models developed by ENIGMA will be relevant to the interpretation of sequence variants in other disease genes., (© 2011 Wiley Periodicals, Inc.)

Clinical management of breast cancer families is complicated by identification of BRCA1 and BRCA2 sequence alterations of unknown significance. Molecular assays evaluating the effect of intronic variants on native splicing can help determine their clinical relevance. Twenty-six intronic BRCA1/2 variants ranging from the consensus dinucleotides in the splice acceptor or donor to 53 nucleotides into the intron were identified in multiple-case families. The effect of the variants on splicing was assessed using HSF matrices, MaxEntScan and NNsplice, followed by analysis of mRNA from lymphoblastoid cell lines. A total of 12 variants were associated with splicing aberrations predicted to result in production of truncated proteins, including a variant located 12 nucleotides into the intron. The posterior probability of pathogenicity was estimated using a multifactorial likelihood approach, and provided a pathogenic or likely pathogenic classification for seven of the 12 spliceogenic variants. The apparent disparity between experimental evidence and the multifactorial predictions is likely due to several factors, including a paucity of likelihood information and a nonspecific prior probability applied for intronic variants outside the consensus dinucleotides. Development of prior probabilities of pathogenicity incorporating bioinformatic prediction of splicing aberrations should improve identification of functionally relevant variants and enhance multifactorial likelihood analysis of intronic variants., (© 2011 Wiley-Liss, Inc.)

Missense substitutions in high-risk cancer susceptibility genes create clinical uncertainty in the genetic counseling process. Multifactorial likelihood classification approaches and in vitro assays are useful for the classification of exonic sequence variants in BRCA1 and BRCA2, but these currently rely on the assumption that changes in protein function are the major biological mechanism of pathogenicity. This study investigates the potentially pathogenic role of aberrant splicing for exonic variants predicted to encode missense substitutions using patient-derived RNA. No splicing aberrations were identified for BRCA1c.5054C>T and BRCA2c.7336A>G, c.8839G>A, and c.9154C>T. However, RT-PCR analysis identified a major splicing aberration for BRCA1c.4868C>G(p.Ala1623Gly), a variant encoding a missense substitution considered likely to be neutral. Splicing aberrations were also observed for BRCA2c.7988A>T(p.Glu2663Val) and c.8168A>G(p.Asp2723Gly), but both variant and wildtype alleles were shown to be present in full-length mRNA transcripts, suggesting that variant protein may be translated. BRCA2 protein function assays indicated that BRCA2p.Glu2663Val, p.Asp2723Gly and p.Arg3052Trp missense proteins have abrogated function consistent with pathogenicity. Multifactorial likelihood analysis provided evidence for pathogenicity for BRCA1 c.5054C>T(p.Thr1685Ile) and BRCA2c.7988A>T(p.Glu2663Val), c.8168A>G(p.Asp2723Gly) and c.9154C>T(p.Arg3052Trp), supporting experimentally derived evidence. These findings highlight the need for improved bioinformatic prediction of splicing aberrations and to refine multifactorial likelihood models used to assess clinical significance.

Due to patterns of migration, selection, and population expansion, founder effects are common among humans. In Southern Brazil, a recurrent TP53 mutation, p.R337H, is detected in families with cancer predisposition. We have used whole locus resequencing and high-density single nucleotide polymorphism (SNP) genotyping to refine TP53 locus haplotype definitions. Haplotyping of 12 unrelated p.R337H carriers using a set of 29 tag SNPs, revealed that all subjects carried the same haplotype, and presence of the mutation on this haplotype was confirmed by allele-specific PCR. The probability that this haplotype occurs independently in all index cases was of 3.1x10(-9), demonstrating a founder effect. Analysis of the patterns of 103 tumors diagnosed in 12 families showed that the presence of p.R337H is associated with multiple cancers of the Li-Fraumeni Syndrome (LFS) spectrum, with relatively low penetrance before the age of 30 but a lifetime risk comparable to classical LFS. The p.R337H families are mostly distributed along a road axis historically known as the main route used by merchants of Portuguese origin in the XVIII and XIX century. This historical circumstance and the relatively low penetrance before the age of 30 may have contributed to the maintenance of this pathogenic mutation in a large, open population.

Mutation scanning using high-resolution melting curve analysis (HR-melt) is an effective and sensitive method to detect sequence variations. However, the presence of a common SNP within a mutation scanning amplicon may considerably complicate the interpretation of results and increase the number of samples flagged for sequencing by interfering with the clustering of samples according to melting profiles. A protocol describing simultaneous high-resolution gene scanning and genotyping has been reported. Here, we show that it can improve the sensitivity and the efficiency of large-scale case-control mutation screening. Two exons of ATM, both containing an SNP interfering with standard mutation scanning, were selected for screening of 1,356 subjects from an international breast cancer genetics study. Asymmetric PCR was performed in the presence of an SNP-specific unlabeled probe. Stratification of the samples according to their probe-target melting was aided by customized HR-melt software. This approach improved identification of rare known and unknown variants, while dramatically reducing the sequencing effort. It even allowed genotyping of tandem SNPs using a single probe. Hence, HR-melt is a rapid, efficient, and cost-effective tool that can be used for high-throughput mutation screening for research, as well as for molecular diagnostic and clinical purposes.

Reliable methods for predicting functional consequences of variants in disease genes would be beneficial in the clinical setting. This study was undertaken to predict, and confirm in vitro, splicing aberrations associated with mismatch repair (MMR) variants identified in familial colon cancer patients. Six programs were used to predict the effect of 13 MLH1 and 6 MSH2 gene variants on pre-mRNA splicing. mRNA from cycloheximide-treated lymphoblastoid cell lines of variant carriers was screened for splicing aberrations. Tumors of variant carriers were tested for microsatellite instability and MMR protein expression. Variant segregation in families was assessed using Bayes factor causality analysis. Amino acid alterations were examined for evolutionary conservation and physicochemical properties. Splicing aberrations were detected for 10 variants, including a frameshift as a minor cDNA product, and altered ratio of known alternate splice products. Loss of splice sites was well predicted by splice-site prediction programs SpliceSiteFinder (90%) and NNSPLICE (90%), but consequence of splice site loss was less accurately predicted. No aberrations correlated with ESE predictions for the nine exonic variants studied. Seven of eight missense variants had normal splicing (88%), but only one was a substitution considered neutral from evolutionary/physicochemical analysis. Combined with information from tumor and segregation analysis, and literature review, 16 of 19 variants were considered clinically relevant. Bioinformatic tools for prediction of splicing aberrations need improvement before use without supporting studies to assess variant pathogenicity. Classification of mismatch repair gene variants is assisted by a comprehensive approach that includes in vitro, tumor pathology, clinical, and evolutionary conservation data., (Copyright 2009 Wiley-Liss, Inc.)

Many individually rare missense substitutions are encountered during deep resequencing of candidate susceptibility genes and clinical mutation screening of known susceptibility genes. BRCA1 and BRCA2 are among the most resequenced of all genes, and clinical mutation screening of these genes provides an extensive data set for analysis of rare missense substitutions. Align-GVGD is a mathematically simple missense substitution analysis algorithm, based on the Grantham difference, which has already contributed to classification of missense substitutions in BRCA1, BRCA2, and CHEK2. However, the distribution of genetic risk as a function of Align-GVGD's output variables Grantham variation (GV) and Grantham deviation (GD) has not been well characterized. Here, we used data from the Myriad Genetic Laboratories database of nearly 70,000 full-sequence tests plus two risk estimates, one approximating the odds ratio and the other reflecting strength of selection, to display the distribution of risk in the GV-GD plane as a series of surfaces. We abstracted contours from the surfaces and used the contours to define a sequence of missense substitution grades ordered from greatest risk to least risk. The grades were validated internally using a third, personal and family history-based, measure of risk. The Align-GVGD grades defined here are applicable to both the genetic epidemiology problem of classifying rare missense substitutions observed in known susceptibility genes and the molecular epidemiology problem of analyzing rare missense substitutions observed during case-control mutation screening studies of candidate susceptibility genes., ((c) 2008 Wiley-Liss, Inc.)

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., ((c) 2008 Wiley-Liss, Inc.)

Genetic testing for mutations in high-risk cancer susceptibility genes often reveals missense substitutions that are not easily classified as pathogenic or neutral. Among the methods that can help in their classification are computational analyses. Predictions of pathogenic vs. neutral, or the probability that a variant is pathogenic, can be made based on: 1) inferences from evolutionary conservation using protein multiple sequence alignments (PMSAs) of the gene of interest for almost any missense sequence variant; and 2) for many variants, structural features of wild-type and variant proteins. These in silico methods have improved considerably in recent years. In this work, we review and/or make suggestions with respect to: 1) the rationale for using in silico methods to help predict the consequences of missense variants; 2) important aspects of creating PMSAs that are informative for classification; 3) specific features of algorithms that have been used for classification of clinically-observed variants; 4) validation studies demonstrating that computational analyses can have predictive values (PVs) of approximately 75 to 95%; 5) current limitations of data sets and algorithms that need to be addressed to improve the computational classifiers; and 6) how in silico algorithms can be a part of the "integrated analysis" of multiple lines of evidence to help classify variants. We conclude that carefully validated computational algorithms, in the context of other evidence, can be an important tool for classification of missense variants., ((c) 2008 Wiley-Liss, Inc.)

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.

Mutations in ATM are responsible for the autosomal recessive disorder ataxia telangiectasia. Heterozygous mutations in ATM have been associated with an elevated risk of breast cancer. We previously reported one breast cancer family in which ATM 7271T>G (V2424G) segregated with disease, and apparently acted in a dominant negative manner. We now report the screening of 782 multiple-case breast cancer families that identified two additional index cases with ATM 7271T>G. Phylogenetic sequence analysis showed that V2424 is a highly conserved residue, and that the 2424G variant is likely to interfere with function. To elucidate the consequences of this mutation, we expression profiled wild-type, heterozygous, and homozygous lymphoblastoid cell lines (LCLs) from Scottish and Australian families using an oligonucleotide microarray. Cluster analysis revealed 77 genes that were differentially expressed in homozygous and heterozygous V2424G cells (compared to wild-type) and 11 genes differentially expressed in the homozygous cells. We also evaluated the profiles of LCLs after exposure to ionizing radiation (IR) and identified 77 genes that were differentially expressed in wild-type cells, but not in homozygous or heterozygous V2424G cells. We validated the expression differences by RT-PCR in additional heterozygous V2424G LCLs from another breast cancer family. We found no consistent cytotoxicity or abrogation of ATM kinase activity after IR in seven heterozygous V2424G LCLs, compared to wild-type LCLs, but did find an increase in the number of chromosomal aberrations. These data suggest that the V2424G missense mutation acts largely as a dominant negative in terms of the associated expression profiles., ((c) 2006 Wiley-Liss, Inc.)

In association analyses, it is critical that informative single-nucleotide polymorphisms (SNPs) be selected for study and utilized appropriately. We sequenced 38 kb, including exons of ELAC2, promoter region and conserved upstream intergenic sequences. A comprehensive characterization of linkage disequilibrium (LD) structure and mutation history was performed using our principal components analysis (PCA) method and a phylogenetic analysis. We identified a complex pattern of LD structure consistent with the occurrence of both recombination and mutation events within ELAC2. Four overlapping and noncontiguous LD groups were defined. Eight tagging SNPs (tSNPs) were identified, accounting for over 90% of the genetic variation of the 19 total variants. We tested associations between familial early-onset prostate cancer (PRCA) and each variant independently and in haplotypes. We performed these tests using all 19 variants and the 8 tSNPs; the results using tSNP haplotypes accurately represent the association evidence for the full haplotypes. We observed increased evidence for association when SNPs were analyzed in haplotypes. The phylogenetic analysis indicated three haplotypes, clustered farthest from the root-node, all of which were found more often in cases than controls. These three haplotypes together showed the best evidence of association with familial, early-onset PRCA (P=0.0024; odds ratio=2.23; 95% CI, 1.33-3.74), indicating possible allelic heterogeneity. Our results suggest that 8 tSNPs are required to comprehensively assess associations in ELAC2, and that haplotypes should be considered for analysis, and that a knowledge of mutation history may be helpful in parsing allelic heterogeneity and suggesting combinations of haplotypes to be tested., ((c) 2005 Wiley-Liss, Inc.)

ELAC2 is a novel candidate cancer susceptibility gene located on chromosome 17p: Carriers of mutations in ELAC2 display a higher risk of developing prostate cancer. Overexpression of ELAC2 in tumor cells causes a delay in G2-M progression characterized by accumulation of cyclin B levels. Consistent with a function in mitosis, further biochemical analysis revealed that ELAC2 physically interacts with the gamma-tubulin complex. This is the first biologic insight into the function of this new putative cancer susceptibility gene, providing clues of how perturbation of ELAC2 might promote tumorigenesis through irregular cell division., (Copyright 2003 Wiley-Liss, Inc.)