John M S Bartlett1, Jason Christiansen, Mark Gustavson, David L Rimm, Tammy Piper, Cornelis J H van de Velde, Annette Hasenburg, Dirk G Kieback, Hein Putter, Christos J Markopoulos, Luc Y Dirix, Caroline Seynaeve, Daniel W Rea. 1. From the Transformative Pathology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada (Dr Bartlett); Biomarker and Companion Diagnostic Group, Edinburgh Cancer Research Centre, University of Edinburgh, Edinburgh, United Kingdom (Dr Bartlett and Ms Piper); Research and Development (Dr Christiansen) and Medical Affairs (Dr Gustavson), Genoptix, Inc, Carlsbad, California; the Department of Pathology, Yale University School of Medicine, New Haven, Connecticut (Dr Rimm); the Departments of Surgery (Dr van de Velde) and Medical Statistics and Bioinformatics (Dr Putter), Leiden University Medical Center, Leiden, The Netherlands; the Department of Gynecological Oncology, University Medical Center Freiburg, Freiburg, Germany (Dr Hasenburg); the Department of Obstetrics and Gynecology, Elblandklinikum, Riesa, Germany (Dr Kieback); the Department of Surgery, Athens University Medical School, Athens, Greece (Dr Markopoulos); Oncology Center, Sint-Augustinus, Wilrijk-Antwerp, Belgium (Dr Dirix); the Department of Medical Oncology, Erasmus MC-Daniel den Hoed Cancer Center, Rotterdam, The Netherlands (Dr Seynaeve); and Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, United Kingdom (Dr Rea). Dr Christiansen is now with Diagnostic Development at Ignyta, Inc, San Diego, California. Dr Gustavson is now with Diagnostics Department at MetaStat, Inc, Boston, Massachusetts. Dr Kieback is now with the Department of Obstetrics and Gynecology at Klinikum Vest Medical Center, Marl, Germany.
Abstract
CONTEXT: Hormone receptors HER2/neu and Ki-67 are markers of residual risk in early breast cancer. An algorithm (IHC4) combining these markers may provide additional information on residual risk of recurrence in patients treated with hormone therapy. OBJECTIVE: To independently validate the IHC4 algorithm in the multinational Tamoxifen Versus Exemestane Adjuvant Multicenter Trial (TEAM) cohort, originally developed on the trans-ATAC (Arimidex, Tamoxifen, Alone or in Combination Trial) cohort, by comparing 2 methodologies. DESIGN: The IHC4 biomarker expression was quantified on TEAM cohort samples (n = 2919) by using 2 independent methodologies (conventional 3,3'-diaminobezidine [DAB] immunohistochemistry with image analysis and standardized quantitative immunofluorescence [QIF] by AQUA technology). The IHC4 scores were calculated by using the same previously established coefficients and then compared with recurrence-free and distant recurrence-free survival, using multivariate Cox proportional hazards modeling. RESULTS: The QIF model was highly significant for prediction of residual risk (P < .001), with continuous model scores showing a hazard ratio (HR) of 1.012 (95% confidence interval [95% CI]: 1.010-1.014), which was significantly higher than that for the DAB model (HR: 1.008, 95% CI: 1.006-1.009); P < .001). Each model added significant prognostic value in addition to recognized clinical prognostic factors, including nodal status, in multivariate analyses. Quantitative immunofluorescence, however, showed more accuracy with respect to overall residual risk assessment than the DAB model. CONCLUSIONS: The use of the IHC4 algorithm was validated on the TEAM trial for predicting residual risk in patients with breast cancer. These data support the use of the IHC4 algorithm clinically, but quantitative and standardized approaches need to be used.
RCT Entities:
CONTEXT: Hormone receptors HER2/neu and Ki-67 are markers of residual risk in early breast cancer. An algorithm (IHC4) combining these markers may provide additional information on residual risk of recurrence in patients treated with hormone therapy. OBJECTIVE: To independently validate the IHC4 algorithm in the multinational Tamoxifen Versus Exemestane Adjuvant Multicenter Trial (TEAM) cohort, originally developed on the trans-ATAC (Arimidex, Tamoxifen, Alone or in Combination Trial) cohort, by comparing 2 methodologies. DESIGN: The IHC4 biomarker expression was quantified on TEAM cohort samples (n = 2919) by using 2 independent methodologies (conventional 3,3'-diaminobezidine [DAB] immunohistochemistry with image analysis and standardized quantitative immunofluorescence [QIF] by AQUA technology). The IHC4 scores were calculated by using the same previously established coefficients and then compared with recurrence-free and distant recurrence-free survival, using multivariate Cox proportional hazards modeling. RESULTS: The QIF model was highly significant for prediction of residual risk (P < .001), with continuous model scores showing a hazard ratio (HR) of 1.012 (95% confidence interval [95% CI]: 1.010-1.014), which was significantly higher than that for the DAB model (HR: 1.008, 95% CI: 1.006-1.009); P < .001). Each model added significant prognostic value in addition to recognized clinical prognostic factors, including nodal status, in multivariate analyses. Quantitative immunofluorescence, however, showed more accuracy with respect to overall residual risk assessment than the DAB model. CONCLUSIONS: The use of the IHC4 algorithm was validated on the TEAM trial for predicting residual risk in patients with breast cancer. These data support the use of the IHC4 algorithm clinically, but quantitative and standardized approaches need to be used.
Authors: Mark Francis Evans; Pamela Mary Vacek; Brian Lee Sprague; Gary Stephen Stein; Janet Lee Stein; Donald Lee Weaver Journal: J Cell Biochem Date: 2019-10-08 Impact factor: 4.429
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Authors: Amy R Peck; Melanie A Girondo; Chengbao Liu; Albert J Kovatich; Jeffrey A Hooke; Craig D Shriver; Hai Hu; Edith P Mitchell; Boris Freydin; Terry Hyslop; Inna Chervoneva; Hallgeir Rui Journal: Mod Pathol Date: 2016-06-17 Impact factor: 7.842
Authors: Daniel Y Joh; Jacob T Heggestad; Shengwei Zhang; Gray R Anderson; Jayanta Bhattacharyya; Suzanne E Wardell; Simone A Wall; Amy B Cheng; Faris Albarghouthi; Jason Liu; Sachi Oshima; Angus M Hucknall; Terry Hyslop; Allison H S Hall; Kris C Wood; E Shelley Hwang; Kyle C Strickland; Qingshan Wei; Ashutosh Chilkoti Journal: NPJ Breast Cancer Date: 2021-07-02