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BRAF p.Val600Glu (V600E) mutation detection in thyroid fine needle aspiration cell block samples: a feasibility study
Connull Leslie1, Fabienne Grieu-Iacopetta, Anna Richter
11Department of Anatomical Pathology, Pathwest Laboratory Medicine, Queen Elizabeth II Medical Centre 2School of Pathology and Laboratory Medicine, University of Western Australia, Perth, WA, Australia.
Pathology
|July 9, 2015
Summary
Detecting BRAF mutations in thyroid cell blocks using immunohistochemistry (IHC) and molecular testing is feasible. Combined methods offer high specificity for papillary thyroid carcinoma diagnosis.
Area of Science:
- Oncology
- Cytopathology
- Molecular Diagnostics
Background:
- Thyroid fine needle aspiration (FNA) cytology is crucial for diagnosis.
- Assessing BRAF mutation status aids in papillary thyroid carcinoma (PTC) management.
- Cell block material from FNA offers potential for molecular and IHC analysis.
Purpose of the Study:
- To evaluate the feasibility and accuracy of BRAF mutation detection in thyroid FNA cell blocks.
- To identify challenges related to cellular material and DNA quality for BRAF testing.
- To compare combined molecular and IHC methods with subsequent histology.
Main Methods:
- BRAF mutation status was assessed in 39 thyroid FNA cell block samples using molecular and IHC methods.
- Samples covered various diagnostic categories, including inconclusive and malignant cytology.
- Results were correlated with follow-up resection specimens when available.
Main Results:
- Combined molecular and IHC methods demonstrated 100% specificity for detecting BRAF mutations in PTC.
- Sensitivity for detecting BRAF mutations was 71.4% compared to histological confirmation.
- IHC identified two mutation-positive cases missed by molecular testing alone, confirmed by histology.
Conclusions:
- BRAF mutation detection in thyroid cell blocks is a viable and highly specific approach for PTC diagnosis.
- Combining molecular and IHC methods yields optimal results for BRAF mutation assessment.
- Cell block quality, particularly tumor cell content, may influence detection accuracy.
