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 build mathematical models of radiotherapy for individual patients. 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
Dr. Mohammad Zahid publishes in Applied Radiation Oncology
To personalize radiation therapy dose fractionation protocols, it will be necessary to first quantitatively describe tumor volume reduction dynamics and subsequently simulate the results of alternative fractionation schemes. This article demonstrates that the Proliferation Saturation Index, PSI, model fits data from head and neck cancer patients, and the results suggest a benefit from alternative fractionation schemes for a selected subset of patients.
Dr. Renee Brady-Nicholls publishes in Nature Communications
Intermittent androgen deprivation therapy (IADT) is an attractive treatment for biochemically recurrent prostate cancer (PCa), whereby cycling treatment on and off can reduce cumulative dose and limit toxicities. Model simulations based on response dynamics from the first IADT cycle identify patients who would benefit from concurrent docetaxel, demonstrating the feasibility and potential value of adaptive clinical trials guided by patient-specific mathematical models of intratumoral evolutionary dynamics.
Dr. Heiko Enderling named Fellow and elected President-elect of the Society for Mathematical Biology
The Society for Mathematical Biology, founded in 1973, promotes the development and dissemination of research and education at the interface between the mathematical and biological sciences. It does so through its meetings, awards, and publications. The Society serves a diverse community of researchers and educators in academia, in industry, and government agencies throughout the world.
Dr. Enderling will begin serving as President-Elect in July, 2020, before assuming Presidency at the SMB annual meeting in July 2021.
EnderlingLab and Shari Pilon-Thomas awarded 5-year NCI U01 award
Drs. Heiko Enderling and Shari Pilon-Thomas have been awarded an NIH/NCI PSOC U01 award for their project "Predict radiation-induced shifts in patient-specific tumor immune ecosystem composition to harness immunological consequences of radiotherapy". This project aims to identify radiation fractionations that specifically focus on enhancing immune responses and immune cell infiltration into the tumor as biomarker to predict treatment response.