This goal of this application is to support a Mentored Quantitative Research Career Development Award (K25) for the applicant, Jared Weis, Ph.D., in the field of cancer biology, modeling, and imaging. A career development plan has been developed that includes immersive advanced didactic coursework and laboratory training that is essential to build Dr. Weis's knowledge and skill in cancer biology, both basic science and translational. This career development plan will also build upon the essential skills required of an independent investigator: grant writing, mentoring, scientific review, and research ethics. During the training, Dr. Weis will acquire expertise in cancer cellular/molecular biology and translational research techniques. Combined with the applicant's previous formal training in biomedical engineering, including computational modeling and imaging sciences, this career development award will ensure transition into independent investigator status researching multi-scale cancer mechanobiology with a particular emphasis in mathematical oncology/computational modeling. A multidisciplinary committee of exceptional investigators in fields of breast cancer biology, mathematical modeling, biostatistics, cancer mechanobiology, and imaging science has been formed and will guide Dr. Weis's career development in cancer research. The proposed career development plan will provide protected time to gain valuable cancer biology research knowledge and experience and enable Dr. Weis to make important impacts in oncology early in his career as an independent investigator. In this application, the proposed work represents an innovative and highly significant approach that seeks to investigate the development and validation of a cohesive multi-scale biomechanical mathematical modeling approach to link the cellular level mechanobiology interactions between cancer cells and extracellular matrix that direct cancer cell proliferation, motility, and aggressiveness, with clinically relevant non-invasive imaging data. The approach utilizes innovative image-based methods for measuring mechanical stiffness combined with mathematical models of tumor cell growth and response to treatment.
The specific aims reflect a comprehensive study that seeks to validate modeling approaches and quantitatively characterize the association between mechanics and cancer, integrating information derived from multiple length scales, from the cellular level to the macroscopic level and includes: in vitr cell culture studies (Aim 1), in vitro bioreactor studies (Aim 2), and in vivo pre-clinical cancer models (Aim 3). If successful, the methods described in this proposal will provide a sound biomechanical mathematical modeling framework that is initialized and constrained by clinically relevant non-invasive imaging (elastography and diffusion-weighted MRI) that parameterizes key cellular level information influenced by mechanical signaling. The parameterization is then utilized for efforts directed at predictive modeling of tumor growth and response to therapy, sensitive to tumor mechanics-induced aggressiveness.

Public Health Relevance

Mechanical signaling is strongly implicated in contributing to tumor progression and therapeutic resistance. This application investigates the use of image-based mathematical models to quantify and characterize the association between mechanics and cancer at multiple length scales, spanning from cellular to tissue scale. The overall goal of this application is to develop biomechanical mathematical models that guide the use of clinically relevant non-invasive imaging data to identify cellular level mechanobiology properties of cancer and predict responsiveness to therapy.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Mentored Quantitative Research Career Development Award (K25)
Project #
5K25CA204599-03
Application #
9308920
Study Section
Subcommittee I - Transistion to Independence (NCI)
Program Officer
Jakowlew, Sonia B
Project Start
2016-07-01
Project End
2021-06-30
Budget Start
2018-07-01
Budget End
2019-06-30
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Wake Forest University Health Sciences
Department
Biomedical Engineering
Type
Schools of Medicine
DUNS #
937727907
City
Winston-Salem
State
NC
Country
United States
Zip Code
27157
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
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
Griesenauer, Rebekah H; Weis, Jared A; Arlinghaus, Lori R et al. (2018) Toward quantitative quasistatic elastography with a gravity-induced deformation source for image-guided breast surgery. J Med Imaging (Bellingham) 5:015003
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
Weis, Jared A; Miga, Michael I; Yankeelov, Thomas E (2017) Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy. Comput Methods Appl Mech Eng 314:494-512
Hormuth 2nd, David A; Weis, Jared A; Barnes, Stephanie L et al. (2017) A mechanically coupled reaction-diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth. J R Soc Interface 14:
Luo, Ma; Frisken, Sarah F; Weis, Jared A et al. (2017) Retrospective study comparing model-based deformation correction to intraoperative magnetic resonance imaging for image-guided neurosurgery. J Med Imaging (Bellingham) 4:035003
McKenna, Matthew T; Weis, Jared A; Barnes, Stephanie L et al. (2017) A Predictive Mathematical Modeling Approach for the Study of Doxorubicin Treatment in Triple Negative Breast Cancer. Sci Rep 7:5725
Griesenauer, Rebekah H; Weis, Jared A; Arlinghaus, Lori R et al. (2017) Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation. Phys Med Biol 62:4756-4776