Question: Can one accurately forecast disease progression for individual patients based on a sufficiently rich profile of the ‘state’ of their disease?
To realize the promise of truly personalized therapy, our group is developing a host of mathematical forecasting models of metastatic progression for various cancer types
Over the past 10 years, we have been developing cell compartment models of cancer cell trafficking throughout the human body. In these models, each compartment is an anatomical site where a metastatic tumor could develop (with some probability), and each of the sites has indigenous cells able to move to a new site according to a stochastic network diagram of cancer progression we have developed for different solid tumor cancers. The cells enter and leave each site via the circulation, and each cell is allowed to mutate, apoptose, and divide. The basic framework is a Markov chain model of progression whose transition matrix governs the probabilities of disease progression from one anatomical site to another. The transition probabilities can be estimated based both on large autopsy data sets of untreated patients (for baseline models), as well as through the use of detailed longitudinal data sets associated with patients which we obtain from several major cancer centers in the United States. Disease progression with these models is artificially simulated based on collections of random walkers (Monte Carlo simulations) leaving the primary tumor and transitioning from site to site across the metastatic network. Our group uses these kinds of models to form statistical forecasts of cancer progression, test hypotheses associated with various therapeutic procedures, and computationally produce artificial patient databases with the correct statistical properties (i.e. matching the ensemble clinical data).
Here are four papers to read where you can learn more about our approach. See full publication list for others.
- PK Newton, J Mason, K Bethel, LA Bazhenova, J Nieva, P Kuhn, A stochastic Markov chain model to describe lung cancer growth and metastasis, PloS one 7(4) e34637 (2012)
- PK Newton, J Mason, K Bethel, LA Bazhenova, J Nieva, L Norton, P Kuhn, Spreaders and sponges define metastasis in lung cancer: A Markov chain Monte Carlo mathematical model, Cancer Res. 73(9) 2760-2760 (2013)
- PK Newton, J Mason, B Hurt, K Bethel, LA Bazhenova, J Nieva, P Kuhn, Entropy, complexity, and Markov diagrams for random walk cancer models, Sci. Rep. 4 7558 (2014)
- PK Newton, J Mason, N Venkatappa, MS Jochelson, B Hurt, J Nieva, E Comen, L Norton, P Kuhn, Spatiotemporal progression of metastatic breast cancer: A Markov chain model highlighting the role of early metastatic sites, NPJ Breast Cancer 1 15018 (2015)
