Targeted agents are revolutionizing cancer treatment. However, important challenges remain. In particular, even among patients with the same known mutation that sensitizes them to a particular targeted therapy, there is a significant range of responses to treatment, from no response (progressive disease) to complete response (e100% tumor volume reduction). What drives this response variability is poorly understood, and response to treatment is generally determined after the fact. In addition, tumors invariably develop resistance to treatment and recur. Identifying-early in the course of therapy-patients that will or will not respond to a given therapeutic regimen and predicting the durability of response would be of enormous clinical benefit: In addition to limiting patients' exposure to the toxicities associated with unsuccessful therapies, it would allow patients the opportunity to switch to a potentially more efficacious treatment. As there are many therapeutic regimens available, and many more being developed, switching treatment early in the course of therapy is a very real option-but only if a reliable method to determine early response were available. Unfortunately, existing methods of determining response and progression are inadequate, as they require long clinical observation times with consequent discomfort, financial burden as well as inability to pursue alternative options. The overall goal of this project is to integrate quantitative in vitro and in vivo imaging measurements to predict the maximum patient tumor response early in the course of oncogene-targeted therapy, in order to enable alternative treatment options that minimize or prevent the emergence of the resistant phenotype. A major barrier to this goal is the lack of quantitative data dynamically linking clinical tumor response t underlying response at the cellular level. Preliminary studies show the feasibility of combining imaging modalities at three biological scales: 2D culture, where drug response can be quantified accurately and dynamically by automated microscopy; 3D bioreactor, more closely simulating in vivo and addressable both by microscopy and magnetic resonance (MR) imaging; rat brain tumor xenografts, an excellent preclinical drug treatment model suitable to MR imaging. The three levels will be integrated by mathematical models incorporating quantifiable parameters and suitable to in vivo validation.
In Aim 1 we will optimize extraction of parameters from 2D and 3D microscopy and MR imaging data of the erlotinib-responsive (PC9-DS9) and resistant (PC9-BR1) human lung cancer cell lines, well-studied models for oncogene-addicted lung cancer. From these data we will establish a look up table of proliferation and death rates linking 2D microscopy and 3D bioreactor MR estimates.
In Aim 2 we will quantify tumor growth dynamics of erlotinib-treated DS9/BR1 mixed cultures in the 3D bioreactor, by initializing and constraining an image-based model.
In Aim 3 we will test predicting acute resistance to oncogene directed therapy in brain tumor xenografts of DS9/BR1 mixtures, by integrating in vivo MRI data with microscopy data and model them to monitor the spatiotemporal appearance of the resistant phenotype.

Public Health Relevance

Targeted therapy is the most promising avenue to successful cancer treatment. Complex arrays of resistance mechanisms, however, undermine outcomes. Robust methods to predict the onset of resistance as early as possible would open avenues to alternative treatment strategies in a personalized way. Successful completion of this proposal will enable development of innovative tools to predict treatment efficacy for a particular patient, so that ineffective therapies could be switched off or exchanged for potentially more effective ones.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Tata, Darayash B
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Vanderbilt University Medical Center
Anatomy/Cell Biology
Schools of Medicine
United States
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Hormuth 2nd, David A; Weis, Jared A; Barnes, Stephanie L et al. (2018) Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer. Int J Radiat Oncol Biol Phys 100:1270-1279
Paudel, B Bishal; Harris, Leonard A; Hardeman, Keisha N et al. (2018) A Nonquiescent ""Idling"" Population State in Drug-Treated, BRAF-Mutated Melanoma. Biophys J 114:1499-1511
Rocha, H L; Almeida, R C; Lima, E A B F et al. (2018) A HYBRID THREE-SCALE MODEL OF TUMOR GROWTH. Math Models Methods Appl Sci 28:61-93
Hormuth 2nd, David A; Eldridge, Stephanie L; Weis, Jared A et al. (2018) Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details. Methods Mol Biol 1711:225-241
Jones, Zack W; Leander, Rachel; Quaranta, Vito et al. (2018) A drift-diffusion checkpoint model predicts a highly variable and growth-factor-sensitive portion of the cell cycle G1 phase. PLoS One 13:e0192087
Jarrett, Angela M; Lima, Ernesto A B F; Hormuth 2nd, David A et al. (2018) Mathematical models of tumor cell proliferation: A review of the literature. Expert Rev Anticancer Ther 18:1271-1286
McKenna, Matthew T; Weis, Jared A; Quaranta, Vito et al. (2018) Variable Cell Line Pharmacokinetics Contribute to Non-Linear Treatment Response in Heterogeneous Cell Populations. Ann Biomed Eng 46:899-911
Woodall, Ryan T; Barnes, Stephanie L; Hormuth 2nd, David A et al. (2018) The effects of intravoxel contrast agent diffusion on the analysis of DCE-MRI data in realistic tissue domains. Magn Reson Med 80:330-340
Lima, E A B F; Ghousifam, N; Ozkan, A et al. (2018) Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data. Sci Rep 8:14558
McKenna, Matthew T; Weis, Jared A; Brock, Amy et al. (2018) Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer. Transl Oncol 11:732-742

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