Cancer is a complex disease that is driven by interactions between tumor cells but also stromal cells and the microenvironment. We hypothesize that the interaction between the different cellular components of the tumor and the molecular signaling networks within each cell can delineate aggressive prostate cancer. We have selected intracellular and extracellular pathways and cell types that are representative of fundamental processes in human prostate cancer. We will use data from a large cohort of prostate cancer patients. These inputs, derived using state of the art methodology, and provided on a cell-per-cell basis will be used to derive a multi-scale mathematical model. This wealth of human data has not previously been achievable and will serve to both parameterize (on multiple scales) and validate the model. Mathematical modeling will generate a library of network triplets (i.e. intracellular signaling networks for tumor epithelium, normal stroma and reactive stroma) whose interactions can describe patient outcome. Networks will be selected using a genetic algorithm to identify and fix the fittest triplets. Triplets will then be selected based upon their ability to reflect invasive or non-invasive disease over a biologically relevant time period and subject to triage based upon their representation of histochemical characteristics. The most representative triplets will be validated against biological endpoints using in vivo experimentation. The validation phase will recapitulate key elements of the mathematical model in vivo to identify those models most functionally-relevant to human disease. These will be tested in vivo to make predictions that confirm or refute these results in silico. The most robust models (those that pass the testing and validation phases) will be compared to a test cohort of human clinical samples to correlate their characteristics against survival endpoints. Our unique combination of resources and team expertise represents an unparalleled environment providing a synergistic approach to understand prostate cancer beyond the limitations of currently applied scientific methodology. Our models begin and end with human data, assuring that the final products will provide new understanding of human prostate cancer.
Three specific aims will be addressed:
Specific aim 1) Develop and Parameterize a Multi-scale Mathematical Model of Prostate Cancer Specific Aim 2) Biological Validation and Testing of Candidate Mathematical Outcomes.
Specific Aim 3) Clinical Validation of Mathematical Outcomes.
Mathematical models have the potential to act as useful prognostic tools but have not yet been well developed for the study of cancer progression. By basing models on data-rich outputs from deconvolution microscopy examination of clinical samples new models with unparalleled detail will be created and tested. This will allow for the development of new prognostic tools and therapeutic strategies to control disease progression.
|Yagawa, Yohsuke; Robertson-Tessi, Mark; Zhou, Susan L et al. (2017) Systematic Screening of Chemokines to Identify Candidates to Model and Create Ectopic Lymph Node Structures for Cancer Immunotherapy. Sci Rep 7:15996|
|Picco, Noemi; Gatenby, Robert A; Anderson, Alexander R A (2017) Stem Cell Plasticity and Niche Dynamics in Cancer Progression. IEEE Trans Biomed Eng 64:528-537|
|Sasaki, Takeshi; Franco, Omar E; Hayward, Simon W (2017) Interaction of prostate carcinoma-associated fibroblasts with human epithelial cell lines in vivo. Differentiation 96:40-48|
|Erdogan, Begum; Ao, Mingfang; White, Lauren M et al. (2017) Cancer-associated fibroblasts promote directional cancer cell migration by aligning fibronectin. J Cell Biol 216:3799-3816|
|De Vivar, Andrea Diaz; Sayeeduddin, Mohammad; Rowley, David et al. (2017) Histologic features of stromogenic carcinoma of the prostate (carcinomas with reactive stroma grade 3). Hum Pathol 63:202-211|
|Sameni, Mansoureh; Cavallo-Medved, Dora; Franco, Omar E et al. (2017) Pathomimetic avatars reveal divergent roles of microenvironment in invasive transition of ductal carcinoma in situ. Breast Cancer Res 19:56|
|Kim, Eunjung; Rebecca, Vito W; Smalley, Keiran S M et al. (2016) Phase i trials in melanoma: A framework to translate preclinical findings to the clinic. Eur J Cancer 67:213-222|
|Cook, Leah M; Araujo, Arturo; Pow-Sang, Julio M et al. (2016) Predictive computational modeling to define effective treatment strategies for bone metastatic prostate cancer. Sci Rep 6:29384|
|Franco, Omar E; Tyson, Darren R; Konvinse, Katherine C et al. (2016) Altered TGF-?/? signaling drives cooperation between breast cancer cell populations. FASEB J 30:3441-3452|
|Henke, Alex; Franco, Omar E; Stewart, Grant D et al. (2016) Reduced Contractility and Motility of Prostatic Cancer-Associated Fibroblasts after Inhibition of Heat Shock Protein 90. Cancers (Basel) 8:|
Showing the most recent 10 out of 23 publications