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.
|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|
|Badri, H; Pitter, K; Holland, E C et al. (2016) Optimization of radiation dosing schedules for proneural glioblastoma. J Math Biol 72:1301-36|
|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|
|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|
|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|
|Xue, Qiong; Lu, Yao; Eisele, Markus R et al. (2015) Analysis of single-cell cytokine secretion reveals a role for paracrine signaling in coordinating macrophage responses to TLR4 stimulation. Sci Signal 8:ra59|
|Kleppe, Maria; Kwak, Minsuk; Koppikar, Priya et al. (2015) JAK-STAT pathway activation in malignant and nonmalignant cells contributes to MPN pathogenesis and therapeutic response. Cancer Discov 5:316-31|
|Meador, Catherine B; Jin, Hailing; de Stanchina, Elisa et al. (2015) Optimizing the sequence of anti-EGFR-targeted therapy in EGFR-mutant lung cancer. Mol Cancer Ther 14:542-52|
|Foo, Jasmine; Liu, Lin L; Leder, Kevin et al. (2015) An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer. PLoS Comput Biol 11:e1004350|
|Michor, Franziska; Beal, Kathryn (2015) Improving Cancer Treatment via Mathematical Modeling: Surmounting the Challenges Is Worth the Effort. Cell 163:1059-63|
Showing the most recent 10 out of 154 publications