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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
3U54CA143798-05S1
Application #
8915834
Study Section
Special Emphasis Panel (ZCA1-SRLB-9 (O1))
Program Officer
Kuhn, Nastaran Z
Project Start
2009-09-30
Project End
2015-08-31
Budget Start
2013-09-01
Budget End
2015-08-31
Support Year
5
Fiscal Year
2014
Total Cost
$173,000
Indirect Cost
$73,000
Name
Dana-Farber Cancer Institute
Department
Type
DUNS #
076580745
City
Boston
State
MA
Country
United States
Zip Code
02215
Wala, Jeremiah A; Bandopadhayay, Pratiti; Greenwald, Noah F et al. (2018) SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Res 28:581-591
Chakrabarti, Shaon; Michor, Franziska (2017) Pharmacokinetics and Drug Interactions Determine Optimum Combination Strategies in Computational Models of Cancer Evolution. Cancer Res 77:3908-3921
Wala, Jeremiah; Beroukhim, Rameen (2017) SeqLib: a C?++ API for rapid BAM manipulation, sequence alignment and sequence assembly. Bioinformatics 33:751-753
Amankulor, Nduka M; Kim, Youngmi; Arora, Sonali et al. (2017) Mutant IDH1 regulates the tumor-associated immune system in gliomas. Genes Dev 31:774-786
Garvey, Colleen M; Gerhart, Torin A; Mumenthaler, Shannon M (2017) Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques. J Vis Exp :
Wala, Jeremiah; Zhang, Cheng-Zhong; Meyerson, Matthew et al. (2016) VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules. Bioinformatics 32:2029-31
Amato, Katherine R; Wang, Shan; Tan, Li et al. (2016) EPHA2 Blockade Overcomes Acquired Resistance to EGFR Kinase Inhibitors in Lung Cancer. Cancer Res 76:305-18
Badri, H; Pitter, K; Holland, E C et al. (2016) Optimization of radiation dosing schedules for proneural glioblastoma. J Math Biol 72:1301-36
Wee, Boyoung; Pietras, Alexander; Ozawa, Tatsuya et al. (2016) ABCG2 regulates self-renewal and stem cell marker expression but not tumorigenicity or radiation resistance of glioma cells. Sci Rep 6:25956
Pitter, Kenneth L; Tamagno, Ilaria; Alikhanyan, Kristina et al. (2016) Corticosteroids compromise survival in glioblastoma. Brain 139:1458-71

Showing the most recent 10 out of 163 publications