Research article
TP53 mutation status and gene expression profiles are powerful prognostic markers of breast cancer
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* Corresponding authors: Anita Langerød anita.langerod@medisin.uio.no - Stefanie S Jeffrey ssj@stanford.edu
1 Department of Genetics, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical Center, Oslo, Norway N-0310
2 Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
3 Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA
4 Department of Mathematics, University of Oslo, Oslo, Norway N-0316
5 Department of Pathology, Rikshospitalet-Radiumhospitalet Medical Center, Oslo, Norway N-0310
6 Faculty of Medicine, University of Oslo, Oslo, Norway
7 Department of Surgery, Akershus University Hospital, Nordbyhagen, Norway N-1474
8 Cancer Center, Ullevål University Hospital, Oslo, Norway N-0407
9 Department of Surgery, Ullevål University Hospital, Oslo, Norway N-0407
Breast Cancer Research 2007, 9:R30 doi:10.1186/bcr1675
Published: 15 May 2007Abstract
Introduction
Gene expression profiling of breast carcinomas has increased our understanding of the heterogeneous biology of this disease and promises to impact clinical care. The aim of this study was to evaluate the prognostic value of gene expression-based classification along with established prognostic markers and mutation status of the TP53 gene (tumour protein p53) in a group of breast cancer patients with long-term (12 to 16 years) follow-up.
Methods
The clinical and histopathological parameters of 200 breast cancer patients were studied for their effects on clinical outcome using univariate/multivariate Cox regression. The prognostic impact of mutations in the TP53 gene, identified using temporal temperature gradient gel electrophoresis and sequencing, was also evaluated. Eighty of the samples were analyzed for gene expression using 42 K cDNA microarrays and the patients were assigned to five previously defined molecular expression groups. The strength of the gene expression based classification versus standard markers was evaluated by adding this variable to the Cox regression model used to analyze all samples.
Results
Both univariate and multivariate analysis showed that TP53 mutation status, tumor size and lymph node status were the strongest predictors of breast cancer survival for the whole group of patients. Analyses of the patients with gene expression data showed that TP53 mutation status, gene expression based classification, tumor size and lymph node status were significant predictors of survival. Breast cancer cases in the 'basal-like' and 'ERBB2+' gene expression subgroups had a very high mortality the first two years, while the 'highly proliferating luminal' cases developed the disease more slowly, showing highest mortality after 5 to 8 years. The TP53 mutation status showed strong association with the 'basal-like' and 'ERBB2+' subgroups, and tumors with mutation had a characteristic gene expression pattern.
Conclusion
TP53 mutation status and gene-expression based groups are important survival markers of breast cancer, and these molecular markers may provide prognostic information that complements clinical variables. The study adds experience and knowledge to an ongoing characterization and classification of the disease.