ClassifyingMLH1andMSH2variants using bioinformatic prediction, splicing assays, segregation, and tumor characteristics

Abstract

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 ten 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, 16/19 variants were considered clinically relevant. Bioinformatic tools for prediction of splicing aberrations need improvement before use without supporting studies to assess variant pathogenicity. Classification

Integrated analysis of unclassified variants in mismatch repair genes

“…Instead, for MMR genes, there are not well-established models or well-characterized features 42 so that, at the time of writing, a very few groups have attempted to classify MMR UVs with the Bayesian likelihood method. The study by Arnold et al 24 investigated several variants, three of which are in common with our data (MLH1 c.307-29CϾA, c.1039-8 TϾA and MSH2 p.Gly322Asp): only c.307-29CϾA was analyzed by a similar comprehensive approach and assigned to class 3, as it is in our study.…”

“…Moreover, three of the class 1 or 2 UVs (MSH2 p.Gly322Asp, MLH1 p.Ser406Asn and p.Val716Met) were classified as benign in a structured assessment study on MMR ambiguous mutations by Barnetson et al 34 MSH2 p.Gly322Asp and MLH1 c.1039-8TϾA were also considered by Arnold et al, 24 but not studied further, because they were found at polymorphic frequency in unaffected controls. This points to the potential value of our classification scheme.…”

“…16 -23 Statistical evaluation UVs were classified using likelihood of pathogenicity for both quantitative and qualitative data. For individual variants, we used a Bayesian approach to incorporate direct and indirect evidence of pathogenicity in a single model, 4,13 starting from a prior probability of 0.5 24 and assuming statistical independence of the sources of information. Posterior probability of pathogenicity was obtained from the likelihood ratio and categorized in five classes.…”

Evaluation of reported pathogenic variants and their frequencies in a Japanese population based on a whole-genome reference panel of 2049 individuals

“…An intronic variant, c.1668-1 G > A, found in a heterozygous individual in 2KJPN, may cause exon skipping and is considered to be pathogenic [40]. Another missense variant, p.Arg687Trp, found in a heterozygous individual, was previously reported in two Japanese families with LS [41].…”

Curating gene variant databases (LSDBs): Toward a universal standard

“…For genes with a large diagnostic interest, for example, breast and colon cancer, a committee of experts can be established that regularly meets to discuss the topic and give their expert opinion on the associated functional consequences and list this in the database. This committee can use software tools specifically built for the gene of interest to evaluate the potential consequences of each variant [Arnold et al, 2009;Jordan et al, 2011;Tavtigian et al, 2008].…”