Tamoxifen resistance in early breast cancer: statistical modelling of tissue markers to improve risk prediction

Baneshi, M. R., Warner, P., Anderson, N., Edwards, J. , Cooke, T. G. and Bartlett, J. M. S. (2010) Tamoxifen resistance in early breast cancer: statistical modelling of tissue markers to improve risk prediction. British Journal of Cancer, 102(10), pp. 1503-1510. (doi: 10.1038/sj.bjc.6605627)

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BACKGROUND: For over two decades, the Nottingham Prognostic Index (NPI) has been used in the United Kingdom to calculate risk scores and inform management about breast cancer patients. It is derived using just three clinical variables - nodal involvement, tumour size and grade. New scientific methods now make cost-effective measurement of many biological characteristics of tumour tissue from breast cancer biopsy samples possible. However, the number of potential explanatory variables to be considered presents a statistical challenge. The aim of this study was to investigate whether in ER+ tamoxifen-treated breast cancer patients, biological variables can add value to NPI predictors, to provide improved prognostic stratification in terms of overall recurrence-free survival (RFS) and also in terms of remaining recurrence free while on tamoxifen treatment (RFoT). A particular goal was to enable the discrimination of patients with a very low risk of recurrence. METHODS: Tissue samples of 401 cases were analysed by microarray technology, providing biomarker data for 72 variables in total, from AKT, BAD, HER, MTOR, PgR, MAPK and RAS families. Only biomarkers screened as potentially informative (i.e., exhibiting univariate association with recurrence) were offered to the multivariate model. The multiple imputation method was used to deal with missing values, and bootstrap sampling was used to assess internal validity and refine the model. RESULTS: Neither the RFS nor RFoT models derived included Grade, but both had better predictive and discrimination ability than NPI. A slight difference was observed between models in terms of biomarkers included, and, in particular, the RFoT model alone included HER2. The estimated 7-year RFS rates in the lowest-risk groups by RFS and RFoT models were 95 and 97%, respectively, whereas the corresponding rate for the lowest-risk group of NPI was 89%. CONCLUSION: The findings demonstrate considerable potential for improved prognostic modelling by incorporation of biological variables into risk prediction. In particular, the ability to identify a low-risk group with minimal risk of recurrence is likely to have clinical appeal. With larger data sets and longer follow-up, this modelling approach has the potential to enhance an understanding of the interplay of biological characteristics, treatment and cancer recurrence. British Journal of Cancer (2010) 102, 1503 - 1510. doi:10.1038/sj.bjc.6605627 www.bjcancer.com

Item Type:Articles
Keywords:Association Biomarkers Breast Breast Cancer Breast-Cancer Cancer Chemotherapy Design Discrimination England Family Follow-Up Her2 Her2 Signalling Histopathology Hormonal-Therapy Imputation Index Management Markers Measurement Mice Microarray Model Molecular Classification Multiple Imputation Number Observer Variation Patient Patients Prediction Predictors Prognostic-Factors Protein Expression Rates Receptor Recurrence Resistance Risk Risk Score Score Scotland Sets Statistical Model Survival Tamoxifen Resistance Technologies Technology Treatment Tumors United-Kingdom Variables
Glasgow Author(s) Enlighten ID:Cooke, Prof Timothy and Edwards, Professor Joanne
Authors: Baneshi, M. R., Warner, P., Anderson, N., Edwards, J., Cooke, T. G., and Bartlett, J. M. S.
College/School:College of Medical Veterinary and Life Sciences > Institute of Cancer Sciences
Journal Name:British Journal of Cancer

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