BACKGROUND: The current gold standard for diagnostic classification of many solid-tissue neoplasms is immunohistochemistry (IHC) performed on formalin-fixed, paraffin-embedded (FFPE) tissue. Although IHC is commonly used, there remain important issues related to preanalytic variability, nonstandard methods, and operator bias that may contribute to clinically significant error. To increase the quantitative accuracy and reliability of FFPE tissue-based diagnosis, we sought to develop a clinical proteomic method to characterize protein expression in pathologic tissue samples rapidly and quantitatively. METHODS: We subclassified FFPE tissue from 136 clinical pituitary adenoma samples according to hormone translation with IHC and then extracted tissue proteins and quantified pituitary hormones with multiplex bead-based immunoassays. Hormone concentrations were normalized and compared across diagnostic groups. We developed a quantitative classification scheme for pituitary adenomas on archived samples and validated it on prospectively collected clinical samples. RESULTS: The most abundant relative hormone concentrations differentiated sensitively and specifically between IHC-classified hormone-expressing adenoma types, correctly predicting IHC-positive diagnoses in 85% of cases overall, with discrepancies found only in cases of clinically nonfunctioning adenomas. Several adenomas with clinically relevant hormone-expressing phenotypes were identified with this assay yet called "null" by IHC, suggesting that multiplex immunoassays may be more sensitive than IHC for detecting clinically meaningful protein expression. CONCLUSIONS: Multiplex immunoassays performed on FFPE tissue extracts can provide diagnostically relevant information and may exceed the performance of IHC in classifying some pituitary neoplasms. This technique is simple, largely amenable to automation, and likely applicable to other diagnostic problems in molecular pathology.
BACKGROUND: The current gold standard for diagnostic classification of many solid-tissue neoplasms is immunohistochemistry (IHC) performed on formalin-fixed, paraffin-embedded (FFPE) tissue. Although IHC is commonly used, there remain important issues related to preanalytic variability, nonstandard methods, and operator bias that may contribute to clinically significant error. To increase the quantitative accuracy and reliability of FFPE tissue-based diagnosis, we sought to develop a clinical proteomic method to characterize protein expression in pathologic tissue samples rapidly and quantitatively. METHODS: We subclassified FFPE tissue from 136 clinicalpituitary adenoma samples according to hormone translation with IHC and then extracted tissue proteins and quantified pituitary hormones with multiplex bead-based immunoassays. Hormone concentrations were normalized and compared across diagnostic groups. We developed a quantitative classification scheme for pituitary adenomas on archived samples and validated it on prospectively collected clinical samples. RESULTS: The most abundant relative hormone concentrations differentiated sensitively and specifically between IHC-classified hormone-expressing adenoma types, correctly predicting IHC-positive diagnoses in 85% of cases overall, with discrepancies found only in cases of clinically nonfunctioning adenomas. Several adenomas with clinically relevant hormone-expressing phenotypes were identified with this assay yet called "null" by IHC, suggesting that multiplex immunoassays may be more sensitive than IHC for detecting clinically meaningful protein expression. CONCLUSIONS: Multiplex immunoassays performed on FFPE tissue extracts can provide diagnostically relevant information and may exceed the performance of IHC in classifying some pituitary neoplasms. This technique is simple, largely amenable to automation, and likely applicable to other diagnostic problems in molecular pathology.
Authors: Angela K Odle; Melody L Allensworth-James; Noor Akhter; Mohsin Syed; Anessa C Haney; Melanie MacNicol; Angus M MacNicol; Gwen V Childs Journal: Endocrinology Date: 2016-08-29 Impact factor: 4.736