CONTEXT: Selection for trastuzumab therapy depends on a companion diagnostic assessment of HER2 by either immunohistochemistry (IHC) for protein overexpression or fluorescence in situ hybridization (FISH) to detect gene amplification. Although many studies have compared IHC to FISH, few have compared the tests to the true gold standard, tumor response. OBJECTIVE: To compare HER2 testing by FISH and IHC along with a third immunofluorescence-based assay (automated quantitative analysis-tissue microarray [AQUA-TMA]) and to assess the value of each test for prediction of response to trastuzumab. DESIGN: Immunohistochemistry and FISH assays were done on both whole slides (IHC-WS and FISH-WS) and on TMAs (IHC-TMA and FISH-TMA). AQUA was only done on TMAs (AQUA-TMA). Response was assessed according to modified Response Evaluation Criteria in Solid Tumors. RESULTS: AQUA-TMA scores showed a significant linear relationship to both the FISH signal ratio and IHC scores on whole sections and TMAs. Assay assessment by outcome showed no association between response and FISH-WS ratio (P = .96), FISH-TMA (P = .55), IHC-WS (P = .75), or IHC-TMA (P = .06), but a significant relationship between AQUA score and categoric response was observed (P = .01). Assessed as a function of outcome using models of logistic regression, both AQUA-TMA and IHC-TMA were equally significant (P = .01). FISH-WS was the most sensitive assay, with a significantly higher true-positive fraction than all other tests except AQUA-TMA, although it was the least specific. IHC-TMA was the most specific assay. The lowest misclassification rate was achieved using AQUA-TMA (0.30). CONCLUSIONS: Both AQUA-TMA and IHC-TMA were substantially more predictive than the FISH or IHC-WS tests. Although these results are derived from a small retrospective series, they suggest that accurate measurement of protein expression and unbiased selection of tissue for measurement may be key factors in prediction of response.
CONTEXT: Selection for trastuzumab therapy depends on a companion diagnostic assessment of HER2 by either immunohistochemistry (IHC) for protein overexpression or fluorescence in situ hybridization (FISH) to detect gene amplification. Although many studies have compared IHC to FISH, few have compared the tests to the true gold standard, tumor response. OBJECTIVE: To compare HER2 testing by FISH and IHC along with a third immunofluorescence-based assay (automated quantitative analysis-tissue microarray [AQUA-TMA]) and to assess the value of each test for prediction of response to trastuzumab. DESIGN: Immunohistochemistry and FISH assays were done on both whole slides (IHC-WS and FISH-WS) and on TMAs (IHC-TMA and FISH-TMA). AQUA was only done on TMAs (AQUA-TMA). Response was assessed according to modified Response Evaluation Criteria in Solid Tumors. RESULTS:AQUA-TMA scores showed a significant linear relationship to both the FISH signal ratio and IHC scores on whole sections and TMAs. Assay assessment by outcome showed no association between response and FISH-WS ratio (P = .96), FISH-TMA (P = .55), IHC-WS (P = .75), or IHC-TMA (P = .06), but a significant relationship between AQUA score and categoric response was observed (P = .01). Assessed as a function of outcome using models of logistic regression, both AQUA-TMA and IHC-TMA were equally significant (P = .01). FISH-WS was the most sensitive assay, with a significantly higher true-positive fraction than all other tests except AQUA-TMA, although it was the least specific. IHC-TMA was the most specific assay. The lowest misclassification rate was achieved using AQUA-TMA (0.30). CONCLUSIONS: Both AQUA-TMA and IHC-TMA were substantially more predictive than the FISH or IHC-WS tests. Although these results are derived from a small retrospective series, they suggest that accurate measurement of protein expression and unbiased selection of tissue for measurement may be key factors in prediction of response.
Authors: Kristian Jensen; Rikke Krusenstjerna-Hafstrøm; Jesper Lohse; Kenneth H Petersen; Helene Derand Journal: Mod Pathol Date: 2016-10-21 Impact factor: 7.842
Authors: David T Yang; Philip J Quann; Adam M Petrich; Catherine P Leith; Ken H Young; Brad S Kahl Journal: Appl Immunohistochem Mol Morphol Date: 2011-01
Authors: Malini Harigopal; William E Barlow; Greg Tedeschi; Peggy L Porter; I-Tien Yeh; Charles Haskell; Robert Livingston; Gabriel N Hortobagyi; George Sledge; Charles Shapiro; James N Ingle; David L Rimm; Daniel F Hayes Journal: Am J Pathol Date: 2010-02-11 Impact factor: 4.307
Authors: Maria Vassilakopoulou; Fabio Parisi; Summar Siddiqui; Allison M England; Elizabeth R Zarella; Valsamo Anagnostou; Yuval Kluger; David G Hicks; David L Rimm; Veronique M Neumeister Journal: Lab Invest Date: 2014-11-24 Impact factor: 5.662
Authors: Eitan M Akirav; Maria-Teresa Baquero; Lynn W Opare-Addo; Michael Akirav; Eva Galvan; Jake A Kushner; David L Rimm; Kevan C Herold Journal: Diabetes Date: 2011-02-09 Impact factor: 9.461
Authors: Dana Faratian; Andrew H Sims; Peter Mullen; Charlene Kay; Inhwa Um; Simon P Langdon; David J Harrison Journal: PLoS One Date: 2011-08-31 Impact factor: 3.240
Authors: Antonio C Wolff; M Elizabeth H Hammond; David G Hicks; Mitch Dowsett; Lisa M McShane; Kimberly H Allison; Donald C Allred; John M S Bartlett; Michael Bilous; Patrick Fitzgibbons; Wedad Hanna; Robert B Jenkins; Pamela B Mangu; Soonmyung Paik; Edith A Perez; Michael F Press; Patricia A Spears; Gail H Vance; Giuseppe Viale; Daniel F Hayes Journal: Arch Pathol Lab Med Date: 2013-10-07 Impact factor: 5.534