Radiation therapy (RT) is the single most utilized therapeutic agent in oncology, yet advances in radiation oncology have primarily focused on beam properties. One obvious shortcoming of current clinical practice is that RT is planned without regard to any of the tumor-environmental factors that may influence outcome.
We integrate mathematical, computational, biological and clinical sciences to thoroughly investigate tumor growth and response to single or combination therapy. In close collaboration with experimentalists and clinicians, mathematical models that are parameterized with experimental and clinical data can help estimate patient-specific disease dynamics, and predict response to different treatments or treatment protocols.
Tumor-associated antigens, stress proteins, and danger-associated molecular patterns are endogenous immune adjuvants that can both initiate and continually stimulate an immune response against a tumor. In retaliation, tumors can hijack intrinsic immune regulatory programs, thereby facilitating continued growth despite an activated antitumor immune response. Clinically apparent tumors have co-evolved with the patient’s immune system and form a complex Tumor-immune ecosystem.
We combine experimental studies and clinical data to calibrate and rigorously validate mathematical and computational frameworks that simulates the complex adaptive tumor-immune interactions, and how cancer therapies change the tumor-immune ecosystem.
Despite new strategies in “precision medicine” in which the screening or specific therapy is guided by molecular biomarkers, treatment protocols rarely vary between patients. Putative biomarkers are often collected at single time points (such as a genomic profile at biopsy, or cancer stage including tumor size, lymph node involvement, and metastatic load) and are rarely predictive or prognostic.
Our group pioneers the approach to harness patient-specific dynamics as biomarkers for treatment response. With mathematical models describing biomarker dynamics over time, we can make predictions and compare and evaluate clinical responses against the prediction. This identifies actionable triggers for treatment adaptation and quantitative personalized oncology
EnderlingLab receives R21 to predict patient-specific prostate cancer treatment responses
Intermittent androgen deprivation therapy (IADT) is a promising strategy to counteract evolution of resistance in prostate cancer patients. However, successful implementation of IADT requires identification of resistance mechanisms, prediction of responses, and determination of clinically actionable triggers of when to pause and when to resume IADT cycles.
In this work we propose to develop a mathematical framework to explore the contribution of prostate cancer stem cell dynamics to evolving resistance, and to use these dynamics in computer simulations to reliably forecast the response in subsequent treatment cycles on a per patient basis.
Enakshi receives Delta Q Outstanding Researcher Award
Congratulations Enakshi D. Sunassee on her recent "Delta Q Outstanding Researcher Award" for the Chemical and Biomedical Engineering Department at USF for her work on radiotherapy modeling in our group. Well done Enakshi.
The Optimal Radiation Dose to Induce Robust Systemic Anti-Tumor Immunity
The optimal radiation dose and dose fractionation to induce antitumor immunity, as well as order and timing with immunotherapeutic agents cannot be derived with the limited experimental and clinical resources, and the quest for optimal radiation-immune synergy is necessarily multidisciplinary.
In this paper we introduce a novel mathematical model calibrated with experimental data to make inroads into deciphering the complexity of radiation and immune system synergy.
Summer interns take on quantitative personalized oncology
We are excited to work with seven interns this summer. Lazlo, Katie, Michael, Keena, Thomas, Alexandria and Ambika are working on different treatment response prediction models for glioma, prostate, head and neck and cervical cancer