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Diagn Mol Pathol. 2013 Jun;22(2):70-5. doi: 10.1097/PDM.0b013e31827e6984.

Evaluation of 2-year experience with EGFR mutation analysis of small diagnostic samples.

Author information

1
Department of Cancer Genetics, National Cancer Institute, Faculty of Medicine, Comenius University, Bratislava, Slovakia. k.hlinkova@gmail.com

Abstract

Mutation analysis of the epidermal growth factor receptor (EGFR) gene is an essential part of the diagnostic algorithm in patients with metastatic or recurrent non-small cell lung cancer (NSCLC). Small biopsies or cytology specimens represent >80% of the available diagnostic material. EGFR mutation analyses were realized on 835 samples (675 cytology specimens, 151 formalin-fixed paraffin-embedded blocks, 5 tumors, and 4 pleural effusions). EGFR mutation analysis was performed by high-resolution melting analysis in combination with mutant-enriched polymerase chain reaction and sequencing analysis. Because of increased risk of inaccuracy in histology diagnosis of small specimens, all subtypes of NSCLC were analyzed. EGFR mutations were detected in 83 cases (10%). EGFR mutation testing failed in 5% (42/835) and was associated with poor cellularity, low percentage of tumor cells, and bad quality of DNA. Although 281 samples were evaluated as insufficient material (poor cellularity and/or unrepresentative tumor content), mutation rates were 7%. Although only adenocarcinomas or NSCLC-not otherwise specified are recommended for EGFR mutation testing, EGFR mutations in 11% of the large cell carcinomas and 4% of the squamous cell carcinomas were observed. Our results indicate that defined algorithm for EGFR testing of small diagnostic samples is sensitive, fast, and suitable even for samples with poor cellularity. The results of this testing should be evaluated depending on tumor content and DNA quality for each sample individually. At the conclusion of our results, we recommend to realize EGFR mutation analysis of small diagnostic samples regardless of the histologic subtypes of NSCLC.

PMID:
23628817
DOI:
10.1097/PDM.0b013e31827e6984
[Indexed for MEDLINE]

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