We view cancer as an open complex adaptive system that can be characterized using necessary and sufficient data, eco-evolutionary first principles and sophisticated computational methods. Here we examine perturbations of that system through therapy. We propose that optimization of treatment requires solid understanding of the underlying dynamics which, in turn, permits accurate predictions of its response to any therapeutic perturbation. We will focus our efforts on three of these teams each of which will concentrate on active clinical trials that investigate different tumors (multiple myeloma, prostate cancer and glioblastoma) treated with different therapeutic strategies (multidrug chemotherapy, hormonal treatment, and immunotherapy). In each trial, the multidisciplinary team will apply an existing computational model that captures the intratumoral evolutionary and ecological dynamics using available clinical data. The general goal is to predict response and resistance to therapy in each patient. Using an iterative approach, each team will work with the Bench-to-Beside Core and Project 1 to optimize the models’ predictive power by exploring alternative mathematical methods and inclusion of new data elements from each trial (e.g. multi-parametric imaging, circulating tumor cells, etc.). In follow-on trials using the same tumors and treatment strategies, we will validate model predictions, apply variations in the treatment approaches (or combinations of approaches) suggested by parallel pre-clinical experiments in Project 1 and model simulations in the Bench-to-Beside Core.


Project Leadership: Robert Gatenby, M.D.; Ariosto Silva, Ph.D.; Kristin Swanson, Ph.D. (Mayo Clinic)





"An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells."

Silva A, Jacobson T, Meads M, Distler A, Shain K.

J Vis Exp. 2015 Jul 15;(101):e53070. doi: 10.3791/53070.

PMID: 26274375


"A preclinical assay for chemosensitivity in multiple myeloma."

Khin ZP, Ribeiro ML, Jacobson T, Hazlehurst L, Perez L, Baz R, Shain K, Silva AS.

Cancer Res. 2014 Jan 1;74(1):56-67. doi: 10.1158/0008-5472.CAN-13-2397.

PMID: 24310398


"Defining the Immune Phenotype for Glioblastoma Multiforme: One Step Closer to Understanding Our Enemy."

Rahme RJ, Aoun RJ, Pines AR, Swanson KR, Bendok BR.

World Neurosurg. 2016 Nov;95:576-577. doi: 10.1016/j.wneu.2016.08.063. No abstract available.

PMID: 27565464


"In silico analysis suggests differential response to bevacizumab and radiation combination therapy in newly diagnosed glioblastoma."

Hawkins-Daarud A, Rockne R, Corwin D, Anderson AR, Kinahan P, Swanson KR.

J R Soc Interface. 2015 Aug 6;12(109):20150388. doi: 10.1098/rsif.2015.0388.

PMID: 26202682


"Patient-specific mathematical neuro-oncology: using a simple proliferation and invasion tumor model to inform clinical practice."

Jackson PR, Juliano J, Hawkins-Daarud A, Rockne RC, Swanson KR.

Bull Math Biol. 2015 May;77(5):846-56. doi: 10.1007/s11538-015-0067-7. Review.

PMID: 25795318


"Predicting Malignant Nodules from Screening CT Scans."

Hawkins S, Wang H, Liu Y, Garcia A, Stringfield O, Krewer H, Li Q, Cherezov D, Gatenby RA, Balagurunathan Y, Goldgof D, Schabath MB, Hall L, Gillies RJ.

J Thorac Oncol. 2016 Dec;11(12):2120-2128. doi: 10.1016/j.jtho.2016.07.002.

PMID: 27422797


"Radiomics: Images Are More than Pictures, They Are Data."

Gillies RJ, Kinahan PE, Hricak H.

Radiology. 2016 Feb;278(2):563-77. doi: 10.1148/radiol.2015151169.

PMID: 26579733