Cellular transitions are fundamental to many steps of carcinogenesis and tumor progression. Such transitions are broadly studied, but general models have been historically limited to qualitative descriptions. This contrasts with phase transitions in physical systems, which are well characterized within the context of the physico- chemical laws, and can be partially understood, in a predictive capacity, using simple, precise models such as the Ising model. Such models are based upon a system of interacting lattice sites. A parameter (e.g. Temperature) is varied, and the fluctuations of the lattice sites are analyzed as the system approaches and passes through a critical point. All critical system-specific details are captured in the interactions between the lattice sites, and the models can yield specific, experimentally verifiable predictions. Ising-like in silico models have guided theoretical studies of transitions in various gene or protein regulatory networks, although resultant predictions can be challenging to experimentally test. We seek a general approach where the experimental input is a statistically large number of single cell measurements, with many protein and metabolite analytes quantitatively measured per cell. From this data we capture the fluctuations and thereby determine the analyte-analyte correlations. In an Ising model analogy, such measurements define the site interactions. These inputs permit straightforward theoretic models for resolving cellular steady states, transitions between steady states, and for making testable predictions. Studies of the chemically-induced-carcinogenesis transition provide preliminary data/proof of concept.
For Aim 1 we develop a picture of cancer cell steady states using integrated metabolic and proteomic single cell assays on cancer models of Glioblastoma Multiforme and Melanoma.
In Aims 2 and 3 we expand this approach to two apparent cellular transitions associated with resistance against targeted therapies: the adaptation of heterogeneous brain cancers to certain targeted inhibitors, and a drug-induced cellular de-differentiation observed in melanomas and other tumors in response to immunotherapy and targeted inhibitors.
All aims are joint experiment/theory aims.
Aims 2 -3 involve in vivo testing of predictions, as well as exome sequencing and global RNA-seq kinetic studies to complement the single cell kinetic analyses. Anticipated outcomes of the work include a general, quantitative approach towards describing cellular transitions associated with cancer. Further, we propose to mine those descriptions of cellular transitions to identify therapy combinations that are designed to hit targets that drive tumor growth, as well as those that drive the transition (and thus promote resistance) Preliminary data to support of this goal is provided. Additionally, guidance for non-continuous therapy dosing (e.g. metronomic or pulsatile regimens) that exploit knowledge of the kinetics, barriers, and reversibility of the transition to resistance is anticipated
Transitions between two cellular phenotypes can often be used to demarcate the steps associated with the onset and progression of cancer. The proposed research program is focused on developing, through experiment and theory, a general and quantitative approach towards understanding such transitions. The proposed approach draws from the physico- chemical sciences, and is shown to have validity, using preliminary data, for understanding the process in which healthy human breast cells switch to a cancerous phenotype following exposure to chemical carcinogens found in cigarette smoke. We propose to study cellular transitions associated with the resistance of drug resistance, within the cancers of glioblastoma multiforme, and melanoma. In both case, we analyze the transition to therapies that can be combined with the primary treatment to halt the transition, and thus halt the development of resistance. We also describe how a detailed kinetic picture of certain transitions that appear reversible can provide guidance for non-continuous therapy dosing for improved tumor killing.
