@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 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


Applications accepted for High school internships in summer 2019


HIP IMO is an integrated mathematical oncology centric internship program that delivers interdisciplinary team science research experiences for high school students aged 16 or older  by the time of the internship. This mentored summer training program is designed for motivated aspiring scientists to help prepare them for interdisciplinary cancer research careers.

The program runs for 8 weeks during the Hillsborough County public schools summer break, June 10 - August 2, 2019.

Program details and application



PhD positions available 


The Integrated Mathematical Oncology Major consists of focused training in mathematical modeling for cancer biology and clinical oncology problems. Students will also receive interdisciplinary training in the broader field of cancer biology and immunology through coursework and immersion in the Moffitt Cancer Center’s research endeavors.

Moffitt Cancer Center has the largest group of mathematical oncologists in its unique Integrated Mathematical Oncology department. Moffitt Cancer Center is the central hub for translational mathematical modeling research, providing a unique training environment for graduate studies.

Program details and application

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.

> Poleszczuk et al. Int. J. Mol. Sci., 19(11), 2018 



PostDoc position available 

We seek a talented individual to work in the unique research environment of the Integrated Mathematical Oncology (IMO) department at Moffitt Cancer Center on the University of South Florida campus in Tampa. IMO integrates mathematicians, computer scientists, and physicists together with imaging specialists as well as clinical and experimental oncologists to develop novel approaches for the understanding, treatment and prevention of cancer.

The Ideal Candidate has a desire to work closely with experimentalists and clinicians, has experience in modeling biological systems, with a preference for those with knowledge of cancer and or radiation therapy, experience in developing/writing publications in quality peer reviewed scientific journals, the ability to develop mathematical models and to program (Matlab, Python, R, C, Java, etc.), visualize and analyze numerical/experimental data, demonstrated creativity, high motivation, and good communication skills

Details and application