@EnderlingLab: quantitative models to personalize oncology

Cancer Therapy

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.

Abscopal Effect

The abscopal effect is the observation of systemic regression of metastatic nodules after local treatment of an individual metastatic site. We hypothesize that different metastatic sites within an individual patient have different potentials to induce an abscopal effect. Mathematical models of T cell trafficking on patient-specific metastatic disease could identity radiation treatment targets that induce systemic antitumor immunity.

Cancer Screening

The temporal evolution of biomarkers of disease progression can be modeled and simulated for individual patients and patient cohorts. A calibrated and validated model, informed with patient-specific biomarker information,  can guide personalized screening schedules to allow efficient monitoring of disease progression and the timely therapeutic intervention to improve patient outcomes.

Cancer Stem Cells

The cancer stem cell hypothesis postulates that only a subpopulation of cancer cells in a tumor is capable of initiating, sustaining, and reinitiating tumors, while the bulk of the population comprises non-stem cancer cells that lack tumor initiation potential. The interactions of these two phenotypically distinct populations can provoke various nonlinear growth kinetics in the emerging tumor, including aggressive growth, tumor dormancy and treatment-induced escape from dormancy.

News

Applications accepted for High school internships in summer 2019

hipimo

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 

PhdProgram

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

RadiationPaper

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