This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Linke, S. P. Articles by Diamond, C. Search for Related Content PubMed PubMed Citation Articles by Linke, S. P. Articles by Diamond, C.

A Multimarker Model to Predict Outcome in Tamoxifen-Treated Breast Cancer Patients

Authors' Affiliations: ¹ Prediction Sciences, La Jolla, California and ² Department of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

Requests for reprints: Steven P. Linke, Prediction Sciences, 9404 Genesee Avenue, Suite 210, La Jolla, CA 92037. Phone: 858-404-0454; Fax: 858-777-3614; E-mail: slinke{at}predict.net.

Purpose: This study was designed to produce a model to predict outcome in tamoxifen-treated breast cancer patients based on clinicopathologic features and multiple molecular markers.

Experimental Design: This was a retrospective study of 324 stage I to III female breast cancer patients treated with tamoxifen for whom standard clinicopathologic data and tumor tissue microarrays were available. Nine molecular markers were studied by semiquantitative immunohistochemistry and/or fluorescence in situ hybridization. Cox proportional hazards analysis was used to determine the contributions of each variable to disease-specific and overall survival, and machine learning was used to produce a model to predict patient outcome.

Results: On a univariate basis, the following features were significantly associated with worse survival: high pathologic tumor or nodal class, histologic grade, epidermal growth factor receptor, ERBB2, MYC, or TP53; absent estrogen receptor (ER) or progesterone receptor; and low BCL2. CCND1 and CDKN1B did not reach statistical significance. On a multivariate basis, nodal class, ER, and MYC were statistically significant as independent factors for survival. However, the benefit of ER-positive status was moderated by BCL2, ERBB2, and progesterone receptor. BCL2 and TP53 also interacted as an independent risk factor. A kernel partial least squares polynomial model was developed with an area under the receiver operating characteristic curve of 0.90.

Conclusions: Our data show the predictive value of BCL2, ERBB2, MYC, and TP53 in addition to the standard hormone receptors and clinicopathologic features, and they show the importance of conditional interpretation of certain molecular markers. Our multimarker predictive model performed significantly better than standard guidelines.

This article has been cited by other articles:

				I. Zlobec, R. Steele, L. Terracciano, J. R Jass, and A. Lugli Selecting immunohistochemical cut-off scores for novel biomarkers of progression and survival in colorectal cancer J. Clin. Pathol., October 1, 2007; 60(10): 1112 - 1116. [Abstract] [Full Text] [PDF]

				J. M. Giltnane, L. Ryden, M. Cregger, P.-O. Bendahl, K. Jirstrom, and D. L. Rimm Quantitative Measurement of Epidermal Growth Factor Receptor Is a Negative Predictive Factor for Tamoxifen Response in Hormone Receptor Positive Premenopausal Breast Cancer J. Clin. Oncol., July 20, 2007; 25(21): 3007 - 3014. [Abstract] [Full Text] [PDF]

				F. Bertucci, D. Birnbaum, and A. Goncalves Proteomics of Breast Cancer: Principles and Potential Clinical Applications Mol. Cell. Proteomics, October 1, 2006; 5(10): 1772 - 1786. [Abstract] [Full Text] [PDF]