Although knowledge ofthe target of transformation Is important for an understanding ofthe natural history of cancers and has therapeutic implications, the cell of origin of most cancers is still unknown. As preliminary data, we have designed a stochastic mathematical model of stem and progenitor cells to study the evolutionary dynamics of initiation of JAK2V617F-positive myeloproliferative neoplasms (MPNs). We considered different evolutionary pathways in order to investigate which cell is the most likely cell of origin of JAK2V617F-mutant MPN. (i) The JAK2V617F mutation is acquired in a hematopoietic stem cell; (ii) a progenitor cell may first acquire a mutation conferring self-renewal, followed by acquisition of JAK2V617F; (iii) the JAK2V617F mutation first emerges in a progenitor cell, followed by a mutation conferring self-renewal; and (iv) a mutation conferring self-renewal to progenitors arises in the stem cell population without causing a change in the cell's phenotype, followed by the JAK2V617F mutation emerging in a progenitor cell. We found mathematical evidence that a progenitor is the most likely cell of origin of JAK2V617F-mutant MPN. In this project, we aim to 1) test the predictions of the mathematical framework in mouse models of JAK2-positive MPN and use this data to further refine the model; 2) design a mathematical framework to investigate the cell of origin of glioma stem cells; we will consider three different mutations leading to glioma formation: a mutation causing overexpression of PDGF and genetic alterations inactivating both alleles of the tumor suppressors PTEN and ARF. We will design stochastic mathematical models to investigate which cell is the most likely cell of origin of glioma. These investigations will also involve detailed three-dimensional computer simulations of glioma formation to investigate spatial as well as temporal aspects of tumorigenesis; and 3) validate the predictions and further refine the mathematical model with data derived from murine models of glioma which allow for expression of the known pathogenic mutations in the appropriate stem/progenitor cell compartments.
Knowledge of the cell of origin of human cancers will allow us to develop more accurate genetic models and to improve our understanding of the biology of these neoplasms. We will then use these in vivo models to better elucidate the pathogenesis of the diseases and to better understand whether the cell of origin might explain the limited benefits of therapies for these disorders.
|Chakrabarti, Shaon; Michor, Franziska (2017) Pharmacokinetics and Drug Interactions Determine Optimum Combination Strategies in Computational Models of Cancer Evolution. Cancer Res 77:3908-3921|
|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; Beroukhim, Rameen (2017) SeqLib: a C?++ API for rapid BAM manipulation, sequence alignment and sequence assembly. Bioinformatics 33:751-753|
|Zhao, Rui; Catalano, Paul; DeGruttola, Victor G et al. (2017) Estimating mono- and bi-phasic regression parameters using a mixture piecewise linear Bayesian hierarchical model. PLoS One 12:e0180756|
|Badri, H; Pitter, K; Holland, E C et al. (2016) Optimization of radiation dosing schedules for proneural glioblastoma. J Math Biol 72:1301-36|
|Pitter, Kenneth L; Tamagno, Ilaria; Alikhanyan, Kristina et al. (2016) Corticosteroids compromise survival in glioblastoma. Brain 139:1458-71|
|Garvey, Colleen M; Spiller, Erin; Lindsay, Danika et al. (2016) A high-content image-based method for quantitatively studying context-dependent cell population dynamics. Sci Rep 6:29752|
|Bolouri, Hamid; Zhao, Lue Ping; Holland, Eric C (2016) Big data visualization identifies the multidimensional molecular landscape of human gliomas. Proc Natl Acad Sci U S A 113:5394-9|
|Liu, Lin L; Brumbaugh, Justin; Bar-Nur, Ori et al. (2016) Probabilistic Modeling of Reprogramming to Induced Pluripotent Stem Cells. Cell Rep 17:3395-3406|
|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|
Showing the most recent 10 out of 162 publications