The tumor stroma is an integral part of the tumor that becomes reprogrammed by unknown mechanisms to co-evolve with epithelial tumor cells and provide an environment conducive for tumor initiation and progression. The mechanisms involved in how this diversity is established and the specific communication between cells that is necessary for fostering tumorigenesis is completely unknown. We propose to take a combined mouse-human approach that utilizes novel genetic, genomic and proteomic technologies to expose how p53 in stromal fibroblasts communicate with tumor cells and the rest of the tumor microenvironment during the initial stages of breast cancer. The Overarching Hypothesis is: Disruption of the p53 pathway in stromal fibroblasts alters tumor-stroma communication, accelerates the initiation and progression of mammary tumors and contributes to the diversity and heterogeneity observed in tumors of human breast cancer patients. This Project includes three aims.
The first aim will use mouse models to identify p53-regulated transcriptional (mRNA and miR) programs in four separate cell compartments of mammary glands/tumors at defined stages of tumor progression.
AIM 2 is also a discovery-based aim that will define proteomic profiles of laser-capture microdissected (LCM) stroma and tumor compartments isolated from mammary glands/tumors of mice described in AIM 1. Transcriptome and proteomic data sets will be integrated to develop mechanistic models of signaling across cell types in the tumor microenvironment.
AIM 3 will combine mouse derived data sets from AIMs 1 &2 with transcriptome and proteomic data sets derived from patient breast tumor stroma to identify signatures that provide insights into the biology of breast cancer, and to instruct patient subtype stratification, predict clinical outcome and develop small compounds that specifically target the tumor stroma. A significant aspect of our project is the combined use of genetic mouse models and human models to identify the mechanisms of stroma-tumor crosstalk that are most relevant to human breast cancer biology. This combined mouse-human approach to cross-analyze mouse and human patient stroma data sets holds the promise to improve the stratification of stromal subclasses and molecular patient subtypes, the ability to predict clinical outcome, and together with novel bioinformatics approaches, to guide the identification of compounds that target unexpected processes of the tumor-stroma dialogue.
Achieving these goals will significantly advance our basic knowledge of tumor-stroma interactions that are likely to be foundational for many types of cancer in addition to breast cancer, will guide the stratification of breast cancer tumor stroma subclasses, and will help develop small compounds to treat different stromal subclasses found in breast cancer patients.
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