@EnderlingLab: quantitative models to personalize oncology

Personalized radiotherapy

 

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-immune ecosystem

 

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.

 

Dynamic predictive biomarkers

 

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

News

Dr. Mohammad Zahid publishes in International Journal of Radiation Oncology, Biology, Physics 

In this article, we simulate radiation response as a reduction in the tumor carrying capacity in the classic logistic growth model. We demonstrate that a simple mathematical model can describe a variety of tumor volume dynamics. Combining historically observed patient responses with a few patient-specific tumor volume measurements allowed for the accurate prediction of patient outcomes, which may inform treatment adaptation and personalization.

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Dr. Enderling assumes office of SMB President 

Dr. Heiko Enderling assumed the office of the President of the Society for Mathematical Biology during the SMB Annual Meeting in June. He will serve for 2 years until June, 2023. As president, Dr. Enderling will advance SMB's mission to promote the development and dissemination of research and education at the interface between the mathematical and biological sciences.

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USF Medical Engineering Capstone students win 1st place at NIBIB competition 

Carolyna Yamamoto Alves Pinto, Jacob Yarinsky and Abby Blocker won the Steven H. Krosnick Prize from NIBIB’s annual Design by Biomedical Undergraduate Teams (DEBUT) Challenge for their Eucovent device that allows multiple patients to be treated with a single ventilator, earning the prize’s $20,000 award for the 2021 competition.

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ABC action news

 

EnderlingLab awarded 2-year NCI R21 award 

Drs. Heiko Enderling (Mathematical Oncology), Solmaz Sahebjam (Neurooncology), and Michael Yu (Radiation Oncology) have been awarded an NIH/NCI R21 award for their project "Developing mathematical model driven optimized recurrent glioblastoma therapies". This project aims to identify radiation fractionations that counteract the evolution of resistance to immunotherapy in recurrent high-grade glioma patients.

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