Evaluation of MLH1 variants of unclear significance. Issue 7 (30th April 2018)

• Record Type: Journal Article
• Authors: Köger, Nicole; Paulsen, Lea; López‐Kostner, Francisco; Della Valle, Adriana; Vaccaro, Carlos Alberto; Palmero, Edenir Inêz; Alvarez, Karin; Sarroca, Carlos; Neffa, Florencia; Kalfayan, Pablo German; Gonzalez, Maria Laura; Rossi, Benedito Mauro; Reis, Rui Manuel; Brieger, Angela; Zeuzem, Stefan; Hinrichsen, Inga; Dominguez‐Valentin, Mev; Plotz, Guido
• Is Part Of: Genes, chromosomes & cancer. Volume 57:Issue 7(2018)
• Page Start: 350
• Page End: 358
• Publication Date: 2018-04-30
• Subjects: classification -- Lynch syndrome -- mlh1 -- pathogenicity -- variant of uncertain significance; Cancer -- Genetic aspects -- Periodicals; 616.994042
• DOI: 10.1002/gcc.22536
• Languages: English
• ISSNs: 1045-2257

Abstract

Abstract: Inactivating mutations in the MLH1 gene cause the cancer predisposition Lynch syndrome, but for small coding genetic variants it is mostly unclear if they are inactivating or not. Nine such MLH1 variants have been identified in South American colorectal cancer (CRC) patients (p.Tyr97Asp, p.His112Gln, p.Pro141Ala, p.Arg265Pro, p.Asn338Ser, p.Ile501del, p.Arg575Lys, p.Lys618del, p.Leu676Pro), and evidence of pathogenicity or neutrality was not available for the majority of these variants. We therefore performed biochemical laboratory testing of the variant proteins and compared the results to protein in silico predictions on structure and conservation. Additionally, we collected all available clinical information of the families to come to a conclusion concerning their pathogenic potential and facilitate clinical diagnosis in the affected families. We provide evidence that four of the alterations are causative for Lynch syndrome, four are likely neutral and one shows compromised activity which can currently not be classified with respect to its pathogenic potential. The work demonstrates that biochemical testing, corroborated by congruent evolutionary and structural information, can serve to reliably classify uncertain variants when other data are insufficient.