期刊
BRITISH JOURNAL OF CANCER
卷 107, 期 5, 页码 800-807出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/bjc.2012.338
关键词
breast cancer; HER2; prognostic model; HER2
类别
资金
- Breast Cancer Campaign, Cancer Research UK [C490/A10119, C490/A10124]
- US National Institutes of Health [CA122340, CA122340Z, CA116201]
- special Government Funding (EVO) of Kuopio University Hospital
- Cancer Fund of North Savo
- Finnish Cancer Organisation
- Academy of Finland [132473]
- University of Eastern Finland
- Helsinki University Central Hospital Research Fund
- Finnish Cancer Society
- Sigrid Juselius Foundation
- Susan G Komen for the Cure, Yorkshire Cancer Research
- Cambridge NIHR Biomedical Research Centre
- Cambridge Experimental Cancer Medicine Centre
- Michael Smith Foundation For Health Research
- Dutch Cancer Society [NKI DCS 2009-4363]
- Cancer Research UK
- The Francis Crick Institute [10119, 10124] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0611-10154] Funding Source: researchfish
BACKGROUND: Predict (www.predict.nhs.uk) is an online, breast cancer prognostication and treatment benefit tool. The aim of this study was to incorporate the prognostic effect of HER2 status in a new version (Predict +), and to compare its performance with the original Predict and Adjuvantl. METHODS: The prognostic effect of HER2 status was based on an analysis of data from 10 179 breast cancer patients from 14 studies in the Breast Cancer Association Consortium. The hazard ratio estimates were incorporated into Predict. The validation study was based on 1653 patients with early-stage invasive breast cancer identified from the British Columbia Breast Cancer Outcomes Unit. Predicted overall survival (OS) and breast cancer-specific survival (BCSS) for Predict I, Predict and Adjuvant! were compared with observed outcomes. RESULTS: All three models performed well for both OS and BCSS. Both Predict models provided better BCSS estimates than Adjuvantl. In the subset of patients with HER2-positive tumours, Predict + performed substantially better than the other two models for both OS and BCSS. CONCLUSION: Predict + is the first clinical breast cancer prognostication tool that includes tumour HER2 status. Use of the model might lead to more accurate absolute treatment benefit predictions for individual patients. British Journal of Cancer (2012) 107, 800-807. doi:10.1038/bjc.2012.338 www.bjcancer.com Published online 31 July 2012 (C) 2012 Cancer Research UK
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