Using cell signaling and cell behavior for personalized medicine
Creating new methods to effectively implement personalized medicine, a priority of the UMHS, is the major aim of this M-cubed collaborative project. Classical statistical approaches have not been robust in helping us discern what therapies will be effective for which lesions in multiple diseases that involve complex interactions between several organ systems and the immune system, most notably cancer and chronic inflammatory diseases. By working with specifically engineered experimental systems and innovative mathematical and physical models of cancer, we can integrate datasets of very high dimensionality and validate methods with live single cell measurements performed on novel devices. Combinations and schedules of therapies can then be tailored using both new experimental and theoretical methods to discover the effect of combinations of drugs or other therapeutic approaches on the patients' live cells. We will develop, test in the laboratory, and formulate a plan for translation to the clinic of an integrated approach that utilizes Boolean modeling of cancer processes: signal transduction, invasion, and metastases to predict responses to drug combinations based on aberrant pathways measures in individual ive cells captured in microfluidic devices engineered for this purpose. The emphasis of this approach is integration rather than reductionist schemas. Tthis work is poised to be propelled to the next quantum level of translation and real application to the clinic, esepcially in breast cancer, by a small investment (such as Mcubed).
Presented at the GRC meeting on Lysosomes and Endocytosis, Andover, NH, June 2016
Presented at the ASCB Annual Meeting, San Diego, CA, Dec 2015