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Evaluation of MLH1 variants of unclear significance
Author(s) -
Köger Nicole,
Paulsen Lea,
LópezKostner 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,
DominguezValentin Mev,
Plotz Guido
Publication year - 2018
Publication title -
genes, chromosomes and cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.754
H-Index - 119
eISSN - 1098-2264
pISSN - 1045-2257
DOI - 10.1002/gcc.22536
Subject(s) - mlh1 , lynch syndrome , in silico , genetics , gene , biology , colorectal cancer , pathogenicity , cancer , bioinformatics , computational biology , dna mismatch repair , microbiology and biotechnology
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.