Research article
An integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer
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* Corresponding author: Andrea H Bild andreab@genetics.utah.edu
- † Equal contributors
1 Department of Pharmacology and Toxicology, University of Utah, 112 Skaggs Hall, Salt Lake City, UT 84112, USA
2 Duke Institute for Genome Sciences & Policy, Duke University Medical Center, 2121 CIEMAS, Durham, NC 27701, USA
3 Lineberger Comprehensive Cancer Center, University of North Carolina, 102 Mason Farm Road, Chapel Hill, NC 27599, USA
4 Department of Genetics, University of North Carolina, 120 Mason Farm Road, Chapel Hill, NC 27599, USA
5 The Pulmonary Center, Boston University School of Medicine, 715 Albany St, Boston, MA 02118, USA
6 Department of Pathology & Laboratory Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
7 Division of Hematology/Oncology, Department of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
8 Carolina Center for Genome Sciences, 5016 Genetic Medicine Building, University of North Carolina, Chapel Hill, NC 27599, USA
Breast Cancer Research 2009, 11:R55 doi:10.1186/bcr2344
Published: 28 July 2009Abstract
Introduction
Perhaps the major challenge in developing more effective therapeutic strategies for the treatment of breast cancer patients is confronting the heterogeneity of the disease, recognizing that breast cancer is not one disease but multiple disorders with distinct underlying mechanisms. Gene-expression profiling studies have been used to dissect this complexity, and our previous studies identified a series of intrinsic subtypes of breast cancer that define distinct populations of patients with respect to survival. Additional work has also used signatures of oncogenic pathway deregulation to dissect breast cancer heterogeneity as well as to suggest therapeutic opportunities linked to pathway activation.
Methods
We used genomic analyses to identify relations between breast cancer subtypes, pathway deregulation, and drug sensitivity. For these studies, we use three independent breast cancer gene-expression data sets to measure an individual tumor phenotype. Correlation between pathway status and subtype are examined and linked to predictions for response to conventional chemotherapies.
Results
We reveal patterns of pathway activation characteristic of each molecular breast cancer subtype, including within the more aggressive subtypes in which novel therapeutic opportunities are critically needed. Whereas some oncogenic pathways have high correlations to breast cancer subtype (RAS, CTNNB1, p53, HER1), others have high variability of activity within a specific subtype (MYC, E2F3, SRC), reflecting biology independent of common clinical factors. Additionally, we combined these analyses with predictions of sensitivity to commonly used cytotoxic chemotherapies to provide additional opportunities for therapeutics specific to the intrinsic subtype that might be better aligned with the characteristics of the individual patient.
Conclusions
Genomic analyses can be used to dissect the heterogeneity of breast cancer. We use an integrated analysis of breast cancer that combines independent methods of genomic analyses to highlight the complexity of signaling pathways underlying different breast cancer phenotypes and to identify optimal therapeutic opportunities.