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Systematic Characterization Of Dasatinib Sensitivity And Resistance In Breast Cancer Cells: Rationale For Patient Selection And Treatment Design
Investigator Initiated Research
Triple-negative (TN) breast cancers are so named because they express neither estrogen nor progestorone receptors, and do not over-express the human epidermal growth factor 2 (HER2). This sub-group of breast cancers account for ~15% of all breast cancers and share several characteristic clinicopathological features in that they: 1) are frequently seen in BRCA1 mutation carriers and usually have a basal-like phenotype; 2) often occur in young females; 3) are associated with a poor short term prognosis and importantly, 4) are unlikely to be responsive to targeted therapies such as tamoxifen or trastuzumab. Therefore, there is an urgent need to identifying novel treatment agents for this sub-type of cancer. Recent studies suggest that a tyrosine kinase inhibitor (TKI), dasatinib, might be effective in treating TN breast tumors, which is in accordance with the fact that EGFR and c-Kit are cDNA expression signatures in TN tumors. In this proposal, we will use cutting edge molecular tools to study the molecular basis of the response to dasatinib (and to other TKIs) in cell lines that bear a TN signature.
1) Specific miRNA signatures can predict response to dasatinib treatment. Determining these signatures could have both diagnostic and therapeutic implications.
2) An innovative miRNA-based pathway analysis tool can be used to identify oncogenic pathways that contribute to dasatinib sensitivity and/or resistance. By so doing, we will be able to better understand the cellular signaling pathways involved in treatment response to dasatinib.
3) In-depth sequence analysis will identify mutations in SRC tyrosine kinase genes that confer resistance to dasatinib treatment. A comprehensive mutation profile will not only guide clinical application of dasatinib in breast cancer therapy but help better understand the mechanism underlying intrinsic and acquired dasatinib resistance.
RESEARCH DESIGN AND AIMS
Novel genomic approaches will be employed to test the listed hypotheses. These include miRNA profiling by deep sequencing and mutation analysis of the tyrosine kinase genes by bar-code parallel sequencing. Both technologies have been optimized by the principal investigators in the past 2 years. We will collect 26 cell lines of basal-like breast cancer (BLBC) type and examine the dasatinib sensitivity of each line using ATP-TCA assay. MicroRNA deep sequencing will be performed in 18 cell lines. A predictive model will be constructed by correlating the dasatinib responsiveness to miRNA profiles, and be further validated by 8 additional BLBC cell lines. Signaling pathways involved in dasatinib response will be inferred from the miRNA predictive signatures and validated with detailed functional analysis. To understand the role of mutation in resistance to dasatinib, we will generate tumor lines of acquired resistance. In-depth sequencing analysis will be performed to identify kinase mutations that contribute to dasatinib resistance.
UNIQUE ASPECTS OF THIS PROPOSAL
As genome biology emerges at the forefront of molecular oncology, unbiased analysis using high-throughput technology is likely to reveal the architecture of drug sensitivity/resistance that is not apparent from single-gene based approaches. In particular, miRNA-based pathway analysis, such as the one described in this proposal, is essential for a better understanding of how cancer drugs do and do not work. We expect the results from this study will provide a scientific basis for patient stratification in the clinical setting. The innovative computational and experimental tools developed along the course of this study can be broadly applied to characterize pathways and resistance mutations involved in response to other cancer drugs and ultimately will facilitate personalized therapeutic design for breast cancer patient care.
Breast cancer is a complicated disease. Over the last few years we have learned that there is a hidden architecture to breast cancer. Understanding this underlying structure will allow us to design and implement more effective therapies. Molecular analysis has revealed a type of breast cancer known as ?basal-like breast cancer?. These cancers that account for about 15% of all breast cancers, often are associated with a poor prognosis and tend to particularly affect young women. Moreover, these types of cancer do not usually express estrogen or progesterone receptor, and the protein targeted by the drug Herceptin ? is not over-expressed. This means that conventional targeted therapies (i.e tamoxifen or aromatase inhibitors, and Herceptin ?) cannot be used to treat these types of cancer. Some new drugs, that target cellular proteins known as ?kinases? may, however, be effective in this important sub-type of cancer. We want to understand more about the predictors of response to these drugs. We will do this by employing several innovative techniques that we and our colleagues have developed in the past few years. These techniques will allow us to study very large numbers of gene products all at the same time, which permit us to build up an unbiased, comprehensive picture of the factors that determine who will and who will not respond to these drugs, and why. We will study basal-like breast cancer cell lines, as they have been extensively characterized, using our novel experimental and analytic tools. Our data should provide important information on why some women with breast cancer respond to these drugs whereas others do not.