@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


Rebecca Bekker and Mohammad Zahid lead new paper in Journal for ImmunoTherapy of Cancer

Immunotherapies are a major breakthrough in oncology, yielding unprecedented response rates for some cancers. However, why not all patients have a favorable response remains unclear. There is an increasing appreciation of the contributions of the complex tumor microenvironment, and the tumor-immune ecosystem in particular, to treatment outcome. 

We present how integrated mathematical oncology approaches can help conceptualize the effect of various immunotherapies on a patient’s tumor and local immune environment, and how combinations of immunotherapy and cytotoxic therapy may be used to improve tumor response and control and limit toxicity on a per patient basis.

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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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New collaborative paper on the tumor-immune ecosystem published in Neoplasia 

We develop an in silico 3-dimensional agent-based model of diverse tumor-immune ecosystems (TIES) represented as anti- or pro-tumor immune phenotypes. We validate the model in 10,469 patients across 31 tumor types by demonstrating that clinically detected tumors have pro-tumor TIES. We then quantify the likelihood radiation induces antitumor TIES shifts toward immune-mediated tumor elimination by developing the individual Radiation Immune Score (iRIS). We show iRIS distribution across 31 tumor types is consistent with the clinical effectiveness of radiotherapy, and in combination with a molecular radiosensitivity index (RSI) combines to predict pan-cancer radiocurability.

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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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