domingo, 21 de mayo de 2017

Improving Mutation Screening in Patients with Colorectal Cancer Predisposition Using Next-Generation Sequencing. - PubMed - NCBI

Improving Mutation Screening in Patients with Colorectal Cancer Predisposition Using Next-Generation Sequencing. - PubMed - NCBI



 2017 May 11. pii: S1525-1578(17)30029-6. doi: 10.1016/j.jmoldx.2017.04.005. [Epub ahead of print]

Improving Mutation Screening in Patients with Colorectal Cancer Predisposition Using Next-Generation Sequencing.

Abstract

Identification of genetic alterations is important for family risk assessment in colorectal cancers. Next-generation sequencing (NGS) technologies provide useful tools for single-nucleotide and copy number variation (CNV) identification in many genes and samples simultaneously. Herein, we present the validation of current Multiplicom MASTR designs of mismatch repair combined to familial adenomatous polyposis genes in a single PCR reamplification test for eight DNA samples simultaneously on a MiSeq apparatus. Blood samples obtained from 224 patients were analyzed. We correctly identified the 97 mutations selected among 48 samples tested in a validation cohort. PMS2 NGS analysis of the eight positive controls identified single-nucleotide variations not detected with targeted referent methods. As NGS method could not discriminate if some of them were assigned to PMS2 or pseudogenes, only CNV analysis with multiplex ligand probe-dependent amplification confirmation was retained for clinical use. Twenty-seven new variants of unknown significance, 21 disease-causing variants, and two CNVs were detected among the 176 patient samples analyzed in diagnosis routine. MUTYH disease-causing mutations were identified in two patient samples assessed for mismatch repair testing, confirming that this method facilitates accurate and rapid individual risk assessments. In one sample, the MUTYH mutation was associated with a MSH6 disease-causing mutation, suggesting that this method is helpful to identify additional cancer risk modifiers and provides a useful tool to optimize clinical issues.

PMID:
 
28502729
 
DOI:
 
10.1016/j.jmoldx.2017.04.005

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