Quantitative personalized oncology Lab
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We oppose racism and hate through integration, active allyship, continuous unlearning, learning, and listening.
Our lab works with the best of the best from all backgrounds, and we learn and benefit from each other’s diverse backgrounds, ideas, training, and ideas.
Quantitative Personalized Oncology
Mission: To integrate quantitative modeling into oncology decision making
Vision: Optimal adaptive cancer therapy for each patient
Our lab integrates mathematical, biological, and clinical sciences to
model, simulate, and predict treatment response for individual patients.
This positions us at the forefront of 'virtual trials' to personalize cancer treatment.
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Teaching the next
generation of
interdisciplinary
scientists
@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
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.
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.
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.
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.