Each year 1.4 million new cases of breast cancer will occur among women worldwide and 50,000 women will die from this disease. In most cases, metastasis is the cause of death. Although progress has been made in broadly understanding the biology of breast tumors and metastases, most of the relevant molecules and pathways remain undefined. Moreover, the integration of multiple signaling pathways into networks and an understanding of their dynamic changes during progression to metastases are still missing.
The design and development of new therapies require a more thorough systems level understanding of the quantitative behavior of breast cancer growth and progression to metastases that arises from the dynamic interplay of cancer cell subpopulations and of signaling networks. The predominant goal of this project is to use a systems biology approach to unravel, integrate and mathematically model the cellular and molecular determinants of breast cancer progression to metastasis. We will use pathophysiologically relevant models of metastatic breast cancer and validate our findings using clinical specimens and data on patients’ outcome.
We propose three interweaved and complementary approaches. The first will use transcriptomic, phosphoproteomic, and single-cell mass cytometry to identify signaling networks driving the different stages of breast tumor progression to metastases and to assess the cellular heterogeneity in the various stages of the metastatic process. The second approach will use these datasets, to mathematically infer and model molecular and cellular dynamics during tumor progression and generate experimentally testable hypotheses and eventually identify the metastasis-initiating cells and their signaling network. Finally, proof of concept experiments using cellular systems in vitro and animal models in vivo will validate the predictions of the mathematical models and test the merit of candidate pathways and molecules as targets for therapy interfering with metastatic spread.
The combined use of pathophysiologically relevant models of metastatic breast cancer, clinical specimens, state-of-the-art technologies for analyzing global signaling networks at the single cell level, and high end computational and mathematical tools put our complementary team in a unique position for: a) modeling changes in cellular heterogeneity and signaling networks specific for each breast tumor cell subpopulation during tumor growth and progression to metastasis and b) assessing the clinically relevant molecular determinant of metastasis. We not only use state-of-the-art technologies, but also cross the boundaries between dry and wet laboratories and the ward. The mathematical model will allow computer simulation, generate experimentally testable predictions, and will ultimately pinpoint novel network-based targets for therapy that will improve the clinical management of patients with metastatic breast cancer.