Question: Can we harness the molecular diversity of cancer cell populations in a tumor to our advantage ?
We are developing algorithms and forecasting tools based on tumor entropy associated with different tissue cancers to build predictive models
A tumor is made up of a heterogeneous collection of cell types, all competing on a fitness landscape mediated by microenvironmental conditions that dictate their interactions. Despite the fact that much is known about cell signaling, cellular cooperation, and the functional constraints that affect cellular behavior, the specifics of how these constraints (and the range over which they act) affect the macroscopic growth laws that govern total volume, mass, and carrying capacity remain poorly understood. Our lab has developed a statistical mechanics approach to understanding this problem. We focus on the total number of possible phenotypic or genotypic states each cell can occupy and show how different assumptions on correlations of these states gives rise to the many different macroscopic tumor growth laws used in the literature. We are also developing various quantitative measures to compare the complexity of metastatic progression patterns associated with different cancer types. We have characterized the probability distribution functions associated with progression patterns across anatomical sites in the human body for eight major soft-tissue cancers and are using the conceptual and quantitative framework of entropy (from information theory) to calculate the inherent disorder or randomness of different cancer types. Comparative analysis is based on the Kullback-Leibler divergence, a measure of the difference between two probability distributions. This has proven to be a highly effective tool for monitoring entropy increase in models of cancer progression.
Here are three papers to read where you can learn more about our approach. See full publication list for others.
- J West, PK Newton, Cellular interactions constrain tumor growth, Proc. Nat’l Acad. Sci. 116(6) 1918-1923 (2019)
- J West, Z Hasnain, P Macklin, PK Newton, An evolutionary model of tumor cell kinetics and the emergence of molecular heterogeneity driving Gompertzian growth, SIAM Review 58(4) 716-736 (2016)
- PK Newton, J Mason, B Hurt, K Bethel, L Bazhenova, J Nieva, P Kuhn, Entropy, complexity and Markov diagrams for random walk cancer models, Sci. Reports 4 7558 (2014)
