We are establishing a postdoctoral training program to be associated with our NIH-funded Physical Science Oncology Center (PSOC) that will train young investigators to be at the interface between cancer biology and computational biology. The PSOC is a unique NCI program that blends engineering, physics and mathematical modeling with genetic and molecular studies of cancer. The PSOC initiative comprises a network of 12 centers around the country that are highly interactive and carry out multiple collaborative projects. Our PSOC combines mathematical modeling of the evolutionary dynamics of cancer cells with in vitro and in vivo modeling to validate and iteratively revise the mathematical frameworks. The PSOC and our proposed PSOC Training Program (PSOC-TP) includes Memorial Sloan-Kettering Cancer Center and Dana-Farber Cancer Institute. The PSOC-TP trainees will have full access to the PSOC network both for educational and professional networking purposes. With this PSOC-TP, we propose to train cohorts of 2 postdocs each for a 2- year period. At the end of the 2-year period, the trainees will move on to other support programs and the next cohort will enter our program. We will accept two types of trainees: trainees with a primary focus on computational biology, who will be provided with didactic and hands-on training in cancer biology;and trainees with a primary interest in cancer biology, who will receive training in computational biology. The instruction component of our program will be comprised of two courses per year, one in horizontal learning (part #1 in year 1, part #2 in year 2), blending both computational biology and cancer biology;and a second course on mathematical models of cancer. The cancer biology and computational biology mentors will form 3 pairs, each with a distinct project which spans across both disciplines. Once accepted into the program, the two trainees will choose one of the projects to work on jointly. The primary mentors will be those associated with the chosen project. The research component of the PSOC-TP will build upon one of the three PSOC research projects. In the first project, evolutionary mathematical modeling will be used to predict the order in which mutations are accumulated during tumor development. In the second project, trainees will use mathematical and computational methods to predict the most likely cell of origin for tumors. In the third projec, trainees will use applied mathematics techniques to predict the risk of resistance arising during specific treatment strategies, and identify the optimum strategy to prevent the emergence of resistance. These projects will form the basis of the PSOC-TP independent research projects of the trainees, which may go well beyond the research proposed in the PSOC. By having a trainee learning the complimentary view of both disciplines, we hope to truly merge the two fields together and create a partnership that will transcend the program.
The work proposed in this PSOC Training Program will produce researchers that are trained in both computational and cancer biology to address questions in cancer research with novel, interdisciplinary techniques. Trainees will be embedded into an already existing, highly interconnected physical science oncology network to help answer several key questions in oncology. By establishing a physical science oncology training program, we will effectively start to merge the two fields and drive forward the interdisciplinary study of cancer and establish mathematical modeling of cancer as an independent discipline.
| Rico-Varela, Jennifer; Singh, Tanya; McCutcheon, Sean et al. (2015) EGF as a New Therapeutic Target for Medulloblastoma Metastasis. Cell Mol Bioeng 8:553-565 |
| Pietras, Alexander; Katz, Amanda M; Ekström, Elin J et al. (2014) Osteopontin-CD44 signaling in the glioma perivascular niche enhances cancer stem cell phenotypes and promotes aggressive tumor growth. Cell Stem Cell 14:357-69 |
| Charles, Nikki A; Holland, Eric C; Gilbertson, Richard et al. (2011) The brain tumor microenvironment. Glia 59:1169-80 |
