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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01CA188383-01
Application #
8766569
Study Section
Special Emphasis Panel (ZCA1-SRLB-1 (M2))
Program Officer
Couch, Jennifer A
Project Start
2014-09-16
Project End
2019-08-31
Budget Start
2014-09-16
Budget End
2015-08-31
Support Year
1
Fiscal Year
2014
Total Cost
$622,359
Indirect Cost
$241,533
Name
Stanford University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305