Glioblastomas (GBM) are the most common and aggressive primary brain tumors. These tumors are divided into molecular subtypes denoted proneural (PN), mesenchymal (MES), classical (CL), and neural (NL) [1]. They are treated with radiation and chemotherapy with temozolomide (TMZ) following surgery but are nearly uniformly fatal because they do not completely respond to this cytotoxic therapy that is the mainstay of treatment. Patients die of recurrent disease that arises from cells that survive radiation and TMZ. The cells that are relatively resistant to standard therapy have stem cell characteristics and live in micro-environmental niches that promote stem cell behavior. Further, genetic alterations that occur along the evolution of these tumors promote resistance and drive the tumors toward molecular subtypes [2]. Therefore, one critical approach to achieving better outcomes for these patients is to understand the mechanisms cells use to maintain resistant phenotypes, and to optimize therapy based on the genetic alterations found in each tumor and the therapy that is given concurrently. In this project, we will use combined mathematical and mouse modeling strategies to identify significantly improved administration schedules for radiation and chemotherapyfor GBM.

We have assembled a team with an outstanding track record in exceptionally innovative research combining mathematical modeling of treatment response and mouse models of glioblastoma. In this proposal, we will (i) develop novel mathematical models that include the treatment effects of radiation, temozolomide (TMZ) and dexamethasone as well as additional genetic alterations; (ii) utilize novel mouse modeling systems that allow non-invasive monitoring of response; (iii) use novel cell culture systems that recapitulate the stem cell niche in these tumors; and (iv) use novel mouse modeling systems that allow acquisition of expression profiles in vivo from specific cell types, for instance cells with and without stem-like character.





1. Verhaak RG, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson MD, Miller CR, Ding L, Golub T, Mesirov JP, Alexe G, Lawrence M, O'Kelly M, Tamayo P, Weir BA, Gabriel S, Winckler W, Gupta S, Jakkula L, Feiler HS, Hodgson JG, James CD, Sarkaria JN, Brennan C, Kahn A, Spellman PT, Wilson RK, Speed TP, Gray JW, Meyerson M, Getz G, Perou CM, Hayes DN, Cancer Genome Atlas Research. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010;17(1):98-110.

2. Ozawa T, Riester M, Cheng Y, Huse JT, Squatrito M, Helmy K, Charles N, Michor F, Holland EC. Most human non-GCIMP glioblastoma subtypes evolve from a common proneural-like precursor glioma. Cancer Cell. 2014;26: 288-300.



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