Validation of IHC4 algorithms for prediction of risk of recurrence in early breast cancer using both conventional and quantitative IHC approaches.

Authors

null

Jason Christiansen

HistoRx, Inc, Branford, CT

Jason Christiansen , John M. S. Bartlett , Mark Gustavson , David Rimm , Tammy Robson , Cornelis J. H. Van De Velde , Annette Hasenburg , Dirk Guenter Kieback , Hein Putter , Christos Markopoulos , Luc Yves Dirix , Caroline M. Seynaeve , Daniel Rea

Organizations

HistoRx, Inc, Branford, CT, Ontario Institute for Cancer Research, Toronto, ON, Canada, HistoRx, Inc., Branford, CT, Department of Pathology, Yale University School of Medicine, New Haven, CT, Edinburgh Cancer Research UK Centre, Edinburgh, United Kingdom, Leiden University Medical Center, Leiden, Netherlands, University Hospital Freiburg, Freiburg, Germany, Helios Hospital Aue, Aue, Germany, Department of Medical Statistics, Leiden University Medical Center, Leiden, Netherlands, Hellenic Cooperative Oncology Group, Athens, Greece, TCRG-A/Oncology Centre, St. Augustinus Hospital, Antwerp, Belgium, Department of Medical Oncology, Erasmus University Medical Center, Daniel den Hoed Cancer Center, Rotterdam, Netherlands, University of Birmingham, Birmingham, United Kingdom

Research Funding

Other
Background: Hormone receptors, HER2 and Ki67 are residual risk markers in early breast cancer. Combining these markers into a unified algorithm (IHC4) provides information on residual recurrence risk of patients treated with hormone therapies. This study aimed to independently investigate the validity of the IHC4 algorithm for residual risk prediction using both conventional (DAB)-IHC and quantitative immunofluorescence (QIF-AQUA). Methods: The TEAM pathology study recruited >4500 samples from patients treated in the TEAM trial. TMAs were stained for ER, PgR, HER2 and Ki67 using QIF-AQUA technology or DAB-based immunohistochemistry (DAB-IHC). Central HER2 FISH was performed. Quantitative image analysis was used to generate expression scores that were normalized to produce “IHC4 algorithm” as well as novel algorithm scores.  Algorithm scores were compared with disease recurrence in univariate and multivariate Cox Proportional Hazards models. Results: Both DAB-IHC and QIF-AQUA IHC4 continuous models were significant (P<0.0001) for prediction of disease recurrence with a continuous Hazard Ratio (HR) of 1.011 (1.010 – 1.013) for QIF-AQUA IHC4 versus 1.008 (1.007 – 1.010) for the DAB-IHC IHC4 model using the published IHC4 algorithm (Cuzick et al 2011).  Binning continuous model scores (4 bins) by Kaplan-Meier survival analysis was used to graphically illustrate these effects.  De novo models for both DAB-IHC and QIF-AQUA were also significantly (P<0.0001) predictive of residual risk in early breast cancer. Additionally, all 4 models were independent predictors of recurrence (P<0.0001) with other recognized clinical prognostic factors in multivariate analysis.  Although results from DAB and QIF-AQUA were modestly correlated, the QIF-AQUA model showed enhanced prediction of recurrence in both Cox Proportional Hazards Modeling and C-index calculations. Conclusions: Either conventional DAB or QIF-AQUA methods of IHC provided evidence supporting the clinical utility of IHC4 algorithms in the context of the TEAM study.  With careful standardization, either of these IHC4 assays should be considered for prediction of residual risk in early breast cancer.

Disclaimer

This material on this page is ©2024 American Society of Clinical Oncology, all rights reserved. Licensing available upon request. For more information, please contact licensing@asco.org

Abstract Details

Meeting

2012 ASCO Annual Meeting

Session Type

Poster Discussion Session

Session Title

Breast Cancer - HER2/ER

Track

Breast Cancer

Sub Track

ER+

Citation

J Clin Oncol 30, 2012 (suppl; abstr 517)

DOI

10.1200/jco.2012.30.15_suppl.517

Abstract #

517

Poster Bd #

7

Abstract Disclosures

Similar Abstracts

First Author: David H Aggen

First Author: Stefania Gori

Abstract

2021 ASCO Quality Care Symposium

Characterizing attitudes related to child-bearing in young women diagnosed with early breast cancer (EBC).

First Author: Saumya Umashankar