• Abbas Agaimy, Claudia Otto, Alexander Braun, Helene Geddert, Inga-Marie Schaefer, and Florian Haller.
• Institute of Pathology, Friedrich Alexander University Erlangen, Germany.
• Int J Clin Exp Patho. 2013 Jan 1; 6 (9): 1839-46.

AimsGenotyping is a prerequisite for tyrosine kinase inhibitor therapy in high risk and malignant GIST. About 10% of GISTs are wild-type for KIT but carry PDGFRA mutations. Applying the traditional approach, mutation analysis of these cases is associated with higher costs if all hotspots regions in KIT (exon 9, 11, 13, 17) are performed at first. Our aim was to evaluate the predictive value of a combined histomorphological-immunohistochemical pattern analysis of PDGFRA-mutated GISTs to efficiently direct KIT and PDGFRA mutation analysis.MethodsThe histomorphology and PDGFRA immunostaining pattern was studied in a test cohort of 26 PDGFRA mutants. This was then validated on a cohort of 94 surgically resected GISTs with mutations in KIT (n=72), PDGFRA (n=15) or with wild-type status (n=7) on a tissue microarray. The histological subtype (spindled, epithelioid, mixed), PDGFRA staining pattern (paranuclear dot-like/Golgi, cytoplasmic and/or membranous), and extent of staining were determined without knowledge of the genotype. The combination of histomorphology and immunophenotype were used to classify tumors either as PDGFRA- or non-PDGFRA phenotype.ResultsPDGFRA-mutated GISTs were significantly more often epithelioid (p<0.001) and had a higher PDGFRA expression, compared to KIT-mutants (p<0.001). Paranuclear PDGFRA immunostaining was almost exclusively observed in PDGFRA mutants (p<0.001). The sensitivity and specificity of this combined histological-immunohistochemical approach to predict the PDGFRA-genotype was 100% and 99%, respectively (p=6x10(-16)).ConclusionA combination of histomorphology and PDGFRA immunostaining is a reliable predictor of PDGFRA genotype in GIST. This approach allows direct selection of the "gene/exons of relevance" to be analyzed and may help to reduce costs and work load and shorten processing time of GIST genotyping by mutation analysis.

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