Three of the critical issues in modern cancer research and evolutionary theory include (a) an understanding of the progression of mutations over time, (b) an identification of the cell of origin of these tumors, (c) and the differential response of cancer cell populations to therapy. In this grant proposal, we have created a consortium of investigators that will blend mathematical modeling of the evolutionary dynamics of cancer cells with in vitro and in vivo modeling to validate, and iteratively revise the mathematical frameworks. In the first project we will use evolutionary mathematical modeling to predict the order in which mutations are accumulated during glioma and leukemia development. We will validate our predictions with genetically engineered mouse modeling of these tumors where the temporal order of these mutations is experimentally manipulable. In the second project we will use evolutionary mathematical modeling to predict the most likely cell of origin for gliomas and leukemias. Again, we will use mouse modeling of gliomagenensis and leukemiagenesis that allows us to validate and refine the mathematical models and determine what oncogenes will in fact allow the predicted cells to serve as the origin for these tumors. In the final project, we will use evolutionary theory to describe the differential response to targeted therapy in lung adenocarcinomas and to radiation therapy in medulloblastomas. We will use the mathematical framework to predict the risk of resistance emerging during a particular dosing strategy and determine the optimal approach that will maximally prevent the evolution of resistance. The mathematical framework will again be revised and validated with in vitro and in vivo modeling. These three projects utilize a single cell measurement core facility that allows simultaneous measurements of individual cells for multiple values that impact the mathematical modeling parameters. In the process, we will build a team of interactive investigators that work with other PS-OCs, the NIH, and the outside biologic and mathematical communities. In addition, we have established core education and training curricula to train the next generation of investigators at the interface of these two disciplines.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Specialized Center--Cooperative Agreements (U54)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1-SRLB-9 (O1))
Program Officer
Kuhn, Nastaran Z
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Dana-Farber Cancer Institute
United States
Zip Code
Han, Lin; Zi, Xiaoyuan; Garmire, Lana X et al. (2014) Co-detection and sequencing of genes and transcripts from the same single cells facilitated by a microfluidics platform. Sci Rep 4:6485
Dunn, Gavin P; Cheung, Hiu Wing; Agarwalla, Pankaj K et al. (2014) In vivo multiplexed interrogation of amplified genes identifies GAB2 as an ovarian cancer oncogene. Proc Natl Acad Sci U S A 111:1102-7
Singh, Tanya; Kothapalli, Chandrasekhar; Varma, Devika et al. (2014) Carboxymethylcellulose hydrogels support central nervous system-derived tumor-cell chemotactic migration: comparison with conventional extracellular matrix macromolecules. J Biomater Appl 29:433-41
Meador, Catherine B; Micheel, Christine M; Levy, Mia A et al. (2014) Beyond histology: translating tumor genotypes into clinically effective targeted therapies. Clin Cancer Res 20:2264-75
Marusyk, Andriy; Tabassum, Doris P; Altrock, Philipp M et al. (2014) Non-cell-autonomous driving of tumour growth supports sub-clonal heterogeneity. Nature 514:54-8
Foo, Jasmine; Michor, Franziska (2014) Evolution of acquired resistance to anti-cancer therapy. J Theor Biol 355:10-20
Tkach, Karen E; Oyler, Jennifer E; Altan-Bonnet, Grégoire (2014) Cracking the NF-?B code. Sci Signal 7:pe5
Guo, Shangqin; Zi, Xiaoyuan; Schulz, Vincent P et al. (2014) Nonstochastic reprogramming from a privileged somatic cell state. Cell 156:649-62
Leder, Kevin; Pitter, Ken; Laplant, Quincey et al. (2014) Mathematical modeling of PDGF-driven glioblastoma reveals optimized radiation dosing schedules. Cell 156:603-16
Elitas, Meltem; Brower, Kara; Lu, Yao et al. (2014) A microchip platform for interrogating tumor-macrophage paracrine signaling at the single-cell level. Lab Chip 14:3582-8

Showing the most recent 10 out of 95 publications