Marc S. Greenblatt

Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results

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.

HGVS Recommendations for the Description of Sequence Variants: 2016 Update

The consistent and unambiguous description of sequence variants is essential to report and exchange information on the analysis of a genome. In particular, DNA diagnostics critically depends on accurate and standardized description and sharing of the variants detected. The sequence variant nomenclature system proposed in 2000 by the Human Genome Variation Society has been widely adopted and has developed into an internationally accepted standard. The recommendations are currently commissioned through a Sequence Variant Description Working Group (SVD‐WG) operating under the auspices of three international organizations: the Human Genome Variation Society (HGVS), the Human Variome Project (HVP), and the Human Genome Organization (HUGO). Requests for modifications and extensions go through the SVD‐WG following a standard procedure including a community consultation step. Version numbers are assigned to the nomenclature system to allow users to specify the version used in their variant descriptions. Here, we present the current recommendations, HGVS version 15.11, and briefly summarize the changes that were made since the 2000 publication. Most focus has been on removing inconsistencies and tightening definitions allowing automatic data processing. An extensive version of the recommendations is available online, at http://www.HGVS.org/varnomen.

In silico analysis of missense substitutions using sequence‐alignment based methods

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 ∼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. Hum Mutat 29(11), 1327–1336, 2008.

Genetic evidence and integration of various data sources for classifying uncertain variants into a single model.

Genetic testing often results in the finding of a variant whose clinical significance is unknown. A number of different approaches have been employed in the attempt to classify such variants. For some variants, case‐control, segregation, family history, or other statistical studies can provide strong evidence of direct association with cancer risk. For most variants, other evidence is available that relates to properties of the protein or gene sequence. In this work we propose a Bayesian method for assessing the likelihood that a variant is pathogenic. We discuss the assessment of prior probability, and how to combine the various sources of data into a statistically valid integrated assessment with a posterior probability of pathogenicity. In particular, we propose the use of a two‐component mixture model to integrate these various sources of data and to estimate the parameters related to sensitivity and specificity of specific kinds of evidence. Further, we discuss some of the issues involved in this process and the assumptions that underpin many of the methods used in the evaluation process. Hum Mutat 29(11), 1265–1272, 2008.

Assessment of functional effects of unclassified genetic variants

Inherited predisposition to disease is often linked to reduced activity of a disease associated gene product. Thus, quantitation of the influence of inherited variants on gene function can potentially be used to predict the disease relevance of these variants. While many disease genes have been extensively characterized at the functional level, few assays based on functional properties of the encoded proteins have been established for the purpose of predicting the contribution of rare inherited variants to disease. Much of the difficulty in establishing predictive functional assays stems from the technical complexity of the assays. However, perhaps the most challenging aspect of functional assay development for clinical testing purposes is the absolute requirement for validation of the sensitivity and specificity of the assays and the determination of positive predictive values (PPVs) and negative predictive values (NPVs) of the assays relative to a “gold standard” measure of disease predisposition. In this commentary, we provide examples of some of the functional assays under development for several cancer predisposition genes (BRCA1, BRCA2, CDKN2A, and mismatch repair [MMR] genes MLH1, MSH2, MSH6, and PMS2) and present a detailed review of the issues associated with functional assay development. We conclude that validation is paramount for all assays that will be used for clinical interpretation of inherited variants of any gene, but note that in certain circumstances information derived from incompletely validated assays may be valuable for classification of variants for clinical purposes when used to supplement data derived from other sources. Hum Mutat 29(11), 1314–1326, 2008.

Defective nucleotide excision repair in xpc mutant mice and its association with cancer predisposition.

Mice that are genetically engineered are becoming increasingly more powerful tools for understanding the molecular pathology of many human hereditary diseases, especially those that confer an increased predisposition to cancer. We have generated mouse strains defective in the Xpc gene, which is required for nucleotide excision repair (NER) of DNA. Homozygous mutant mice are highly prone to skin cancer following exposure to UVB radiation, and to liver and lung cancer following exposure to the chemical carcinogen acetylaminofluorene (AAF). Skin cancer predisposition is significantly augmented when mice are additionally defective in Trp53 (p53) gene function. We also present the results of studies with mice that are heterozygous mutant in the Apex (Hap1, Ref-1) gene required for base excision repair and with mice that are defective in the mismatch repair gene Msh2. Double and triple mutant mice mutated in multiple DNA repair genes have revealed several interesting overlapping roles of DNA repair pathways in the prevention of mutation and cancer.

Genetics: The human variome project

An ambitious plan to collect, curate, and make accessible information on genetic variations affecting human health is beginning to be realized.

Calibration of multiple in silico tools for predicting pathogenicity of mismatch repair gene missense substitutions.

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 (R2 = 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.

How to catch all those mutations--the report of the third Human Variome Project Meeting, UNESCO Paris, May 2010.

The third Human Variome Project (HVP) Meeting “Integration and Implementation” was held under UNESCO Patronage in Paris, France, at the UNESCO Headquarters May 10–14, 2010. The major aims of the HVP are the collection, curation, and distribution of all human genetic variation affecting health. The HVP has drawn together disparate groups, by country, gene of interest, and expertise, who are working for the common good with the shared goal of pushing the boundaries of the human variome and collaborating to avoid unnecessary duplication. The meeting addressed the 12 key areas that form the current framework of HVP activities: Ethics; Nomenclature and Standards; Publication, Credit and Incentives; Data Collection from Clinics; Overall Data Integration and Access—Peripheral Systems/Software; Data Collection from Laboratories; Assessment of Pathogenicity; Country Specific Collection; Translation to Healthcare and Personalized Medicine; Data Transfer, Databasing, and Curation; Overall Data Integration and Access—Central Systems; and Funding Mechanisms and Sustainability. In addition, three societies that support the goals and the mission of HVP also held their own Workshops with the view to advance disease‐specific variation data collection and utilization: the International Society for Gastrointestinal Hereditary Tumours, the Micronutrient Genomics Project, and the Neurogenetics Consortium. Hum Mutat 71:1374–1381, 2010.

Practical guidelines addressing ethical issues pertaining to the curation of human locus-specific variation databases (LSDBs).

More than 1,000 Web‐based locus‐specific variation databases (LSDBs) are listed on the Website of the Human Genetic Variation Society (HGVS). These individual efforts, which often relate phenotype to genotype, are a valuable source of information for clinicians, patients, and their families, as well as for basic research. The initiators of the Human Variome Project recently recognized that having access to some of the immense resources of unpublished information already present in diagnostic laboratories would provide critical data to help manage genetic disorders. However, there are significant ethical issues involved in sharing these data worldwide. An international working group presents second‐generation guidelines addressing ethical issues relating to the curation of human LSDBs that provide information via a Web‐based interface. It is intended that these should help current and future curators and may also inform the future decisions of ethics committees and legislators. These guidelines have been reviewed by the Ethics Committee of the Human Genome Organization (HUGO). Hum Mutat 31:–6, 2010.