A fundamental property of cancer is uncontrolled cell proliferation. Much knowledge has accumulated on altered genetic and signaling networks that drive uncontrolled proliferation. Recently, there has been a resurgence of interest in the intimate link between proliferation and metabolism, absolutely required to fulfill energy and biomass demands for cell division. In cancer, metabolic networks are highly adaptable, and often metabolism of cancer cells relies largely on aerobic glycolysis, a property referred to as the Warburg effect and akin to fermentation: even in the presence of oxygen, energy metabolism bypasses mitochondrial respiration. The dysregulated interface between metabolic networks and oncogene-modified proliferation networks is emerging as a fertile area to identify critical target nodes, or strategies to defy the drive to ever-adaptable uncontrolled proliferation.
This workshop will encompass a mix of experimentalists and mathematicians. Ideally, the former will be engaged on the production of large datasets on cancer cell proliferation, both at the cell population and single-cell level, and in response to microenvironment perturbations including anti-proliferative drugs. The latter will focus on mathematical models of proliferation and metabolism at several scales, including genetic, signaling and cellular, including a focus on the ability of cancer cell populations to regenerate and reprogram in response to hostile microenvironment and to targeted treatment, ultimately persisting in their proliferative state. Multi-scale models connecting the growth of cultured cancer cells and/or individual tumors to epidemiological data will also be considered.
Although tumor growth and cancer cell proliferation have been modeled mathematically for decades, adequate datasets have been scarce and fragmentary due to experimental limitations. Recently, several game-changing high-throughput technologies, including genomics, proteomics, and automated microscopy, have created remarkable opportunities for renewed modeling efforts. Furthermore, small-molecule drugs with exquisite specificity for signaling network nodes are in an intensive phase of development and deployment into clinical trials. As these targeted agents increasingly enter standard clinical practice, a major challenge is to improve outcomes by rational drugging strategies. Sheer combinatorics makes drug strategy testing in the field prohibitively expensive, both financially and temporally, opening avenues for mathematical and statistical approaches that, combined with experimentation, have the power to streamline testing.