Even when there is initial therapeutic sensitivity to a conventional chemotherapy or targeted therapy, tumors can become resistant and recur. Methods that model and predict therapeutic resistance of cancer can be extremely useful in the development of more effective treatments for cancer. Our long-term goal is to develop strategies to model, predict, and target therapeutic resistance of cancer. Our proposed approach is to utilize conditional transgenic mouse models combined with computational modeling. We hypothesize that therapeutic resistance to oncogene inactivation can be modeled and thus predicted as a consequence of clonal evolution of tumor cells driven by both cell autonomous and immune-mediated selective pressures. Our approach in Aim 1 is to build a mathematical model that incorporates the roles of the immune system and of evolutionary dynamics to predict the emergence of therapeutic resistance upon oncogene inactivation. Then, in Aim 2 we will experimentally interrogate the roles of the immune system and of evolutionary dynamics in the emergence of therapeutic resistance. We will directly examine immune effectors/cytokines and clonal evolution in our conditional transgenic mouse model with intravital microscopy and bioluminescence imaging. Finally, in Aim 3 we will validate in vivo our mathematical model's predictions of the emergence of therapeutic resistance under novel circumstances.
Even when there is initial therapeutic sensitivity to a conventional chemotherapy or targeted therapy, tumors can become resistant and recur. Our recent results have demonstrated unexpected ways in which the immune system has a critical role in determining the response to shutting down the oncogene driving the cancer. We propose to model, predict and target therapeutic resistance in cancer using transgenic mouse models and mathematical modeling together so that more effective anti-cancer therapies can be developed.
|Li, Yulin; Deutzmann, Anja; Choi, Peter S et al. (2016) BIM mediates oncogene inactivation-induced apoptosis in multiple transgenic mouse models of acute lymphoblastic leukemia. Oncotarget 7:26926-34|
|Gouw, Arvin M; Toal, Georgia G; Felsher, Dean W (2016) Metabolic vulnerabilities of MYC-induced cancer. Oncotarget 7:29879-80|
|Casey, Stephanie C; Tong, Ling; Li, Yulin et al. (2016) MYC regulates the antitumor immune response through CD47 and PD-L1. Science 352:227-31|
|Felsher, Dean W; Lowe, Leroy (2016) Affordable Cancer Medications Are Within Reach but We Need a Different Approach. J Clin Oncol 34:2194-5|
|Frieboes, Hermann B; Smith, Bryan R; Wang, Zhihui et al. (2015) Predictive Modeling of Drug Response in Non-Hodgkin's Lymphoma. PLoS One 10:e0129433|
|Yetil, Alper; Anchang, Benedict; Gouw, Arvin M et al. (2015) p19ARF is a critical mediator of both cellular senescence and an innate immune response associated with MYC inactivation in mouse model of acute leukemia. Oncotarget 6:3563-77|
|Casey, Stephanie C; Vaccari, Monica; Al-Mulla, Fahd et al. (2015) The effect of environmental chemicals on the tumor microenvironment. Carcinogenesis 36 Suppl 1:S160-83|
|Casey, Stephanie C; Amedei, Amedeo; Aquilano, Katia et al. (2015) Cancer prevention and therapy through the modulation of the tumor microenvironment. Semin Cancer Biol 35 Suppl:S199-223|
|Shroff, Emelyn H; Eberlin, Livia S; Dang, Vanessa M et al. (2015) MYC oncogene overexpression drives renal cell carcinoma in a mouse model through glutamine metabolism. Proc Natl Acad Sci U S A 112:6539-44|
|Kearney, Alper Y; Anchang, Benedict; Plevritis, Sylvia et al. (2015) ARF: connecting senescence and innate immunity for clearance. Aging (Albany NY) 7:613-5|
Showing the most recent 10 out of 21 publications