A Classification Model Relative to Splicing for Variants of Unknown Clinical Significance: Application to theCFTRGene

Abstract

Molecular diagnosis of cystic fibrosis and cystic fibrosis transmembrane regulator (CFTR)-related disorders led to the worldwide identification of nearly 1,900 sequence variations in the CFTR gene that consist mainly of private point mutations and small insertions/deletions. Establishing their effect on the function of the encoded protein and therefore their involvement in the disease is still challenging and directly impacts genetic counseling. In this context, we built a decision tree following the international guidelines for the classification of variants of unknown clinical significance (VUCS) in the CFTR gene specifically focused on their consequences on splicing. We applied general and specific criteria, including comprehensive review of literature and databases, familial genetics data, and thorough in silico studies. This model was tested on 15 intronic and exonic VUCS identified in our cohort. Six variants were classified as probably nonpathogenic considering their impact on splicing and eight as probably pathogenic, which include two apparent missense mutations. We assessed the validity of our method by performing minigenes studies and confirmed that 93% (14/15) were correctly classified. We provide in this study a high-performance method that can play a full role in interpreting the results of molecular diagnosis in emergency context, when functional studies are not achievable.

Defining the disease liability of variants in the cystic fibrosis transmembrane conductance regulator gene

“…Computational approaches predicting disease liability have been applied to splice site 59 and missense changes 19-21 to classify CFTR variants. These approaches lack specificity needed for definitive clinical classification.…”

Does extensive genotyping and nasal potential difference testing clarify the diagnosis of cystic fibrosis among patients with single-organ manifestations of cystic fibrosis?

“…The role of in silico tools to predict the functional consequences of rare CFTR mutations has also been evaluated 30 31. While in silico tools may provide insight into the potential pathogenicity of rare mutations, they were shown to predict the clinical severity of missense mutations with known clinical consequences poorly, raising considerable doubt over their diagnostic role in mutations with variable or unknown clinical consequences 32…”

Genetics of cystic fibrosis: CFTR mutation classifications toward genotype-based CF therapies

“…However, the computational methods mainly based on evolutionary conservation and protein structure, have limitations (Dorfman et al, 2010). Notably, for the prediction of splicing defects, the use of a combination of tools is more powerful than individual ones (Aissat et al, 2012, Raynal et al, 2013. In silico predictions need to be validated by either in vivo measures (when possible) or in vitro assays.…”