Personalized Oncology

 

The major research focus of our "Quantitative Personalized Oncology" research group is developing integrative mathematical models for a specific clinical problem, use experimental and clinical data to calibrate and validate the model, and use the model to predict patient-specific treatment responses with the ultimate aim to assist clinical decision making on a per patient basis. We focus on models that can be informed with routinely collected clinical data, and simulate clinically approved treatments, protocols, and combination therapies.

Simple mathematical models are fit to retrospective clinical training data to derive parameter distributions for each participating mechanism. Parameter distributions with small variation will be collapsed into uniform rate constants, leaving variable mechanisms that are most likely to determine patient-specific  outcomes. Calibrated models will be validated on independent training data, before virtual "in silico" trials determine optimal treatment protocols on a per patient basis.

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Mathematical Modeling

 

Mathematical models of tumor dynamics have become more accurate and accepted in recent years and enable a better prediction of biological pathways that may be involved in the initiation and development of a tumor. The big aim for theoretical and practical oncologists is to find ways to treat the disease or improve the life of patients. Mathematical models help to identify crucial mechanisms to compare different treatments or design new treatment strategies. With the growing acceptance of models of tumor development the subsequent application of treatment planning will play an increasingly important role in the clinic. Using models one can compare different approaches or design new treatment strategies, which then can be tailored to individual patient data. With more information on cancer relevant to modeling becoming available the new well-parameterized models have the power to predict responses to various treatment techniques such as drug scheduling in chemotherapy, immunotherapy, and radiotherapy as well as combinations of these.

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Radiation Therapy

 

The ultimate aims of our cancer models are to assist in the designing of treatment protocols that can reduce mortality and improve patient quality of life. Conventional treatment of cancer is surgery combined with radiotherapy or chemotherapy for localized tumors or systemic treatment of advanced cancers, respectively. Although radiation is widely used as treatment, most scheduling is based on empirical knowledge and less on the predictions of sophisticated growth dynamical models of treatment response. Part of the failure to translate modeling research to the clinic may stem from language barriers, exacerbated by often-esoteric model renderings with inaccessible parameterization. Our lab develops different quantitative models for radiotherapy response in vitro, in vivo, and in patients. We study responses on the single cell level, different fractionated treatment protocols, and targeted intraoperative radiotherapy. The long term aim of our work is to develop precision radiation oncology together with Moffitt clinicians.

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Immune Therapy

 

Tumors grow within a host tissue that both facilitates progression by supplying nutrients and growth factors, and inhibits it through physical constraints and immune surveillance. Numerous studies suggest that a low immune response might be beneficial, if not necessary, for tumor growth, and only a strong immune response can counter tumor growth and thus inhibit progression. Tumors that develop to become clinically apparent have evolved to evade the immune system. The complex local and systemic tumor-immune ecological system may be tipped back in favor of effective immune surveillance and tumor regression through immunotherapeutic strategies, particularly when used with focal immune-activating therapies. Understanding how to induce robust antitumor immunity will provide novel rationales for optimizing combination therapies in clinical trials. We study the dynamic interaction of heterogeneous tumor populations with complex host immune response to understand how to harness the immune system for novel treatment protocols that synergize with radiotherapy or chemotherapy.

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Cancer Screening

 

Many cancers are detected at a late stage when cure is often impossible. Detection of tumors at early stage remains the single most efficient positive prognostic factor, but not many successful population level screening programs exist. Symptoms are typically absent in early malignant transformation, and those that do exist are often attributed to other causes. Further difficulty arises from the high inter-patient variability in the time from early indications to malignant transformation; prompting the need for reliable biomarkers of disease progression and quantitative tools for patient-specific predicting progression. These are the fundamental components for the recommendation of personalized screening schedules for individual patients. Such scheduling has the potential to minimize the risk of undetected malignant transformation, while avoiding excessive and costly over-screening. 

Abscopal Effect

 

The abscopal effect is the clinical observation of shrinking of unirradiated tumor metastases after radiation of individual tumor sites elsewhere in the body. Irradiation induces cell stress and immunogenic cell death, thereby exposing a wealth of previously hidden and new tumor associated antigens to the immune system. Local immune stimulation propagates through the host circulation to also surveil distant metastases. We develop mathematical models of local tumor-immune interactions as well as systemic dissimination of activated T cells through the host circulatory system.

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Cancer Stem Cells

 

Tumors are intrinsically heterogeneous. The majority of tumor cells have limited life span and replicative potential, and only a small minority — so-called cancer stem cells — live forever, divide infinitely and potentially produce more such stem cells. It is these stem cells that determine tumor formation, and their dynamics are counterintuitively inhibited by their non-stem progeny. Only a high migration rate can liberate stem cells and enable their migration to seed new clones in the vicinity of the original cluster. In this manner, the tumor continually ‘self-metastasizes’.

We use computer models to define the behavior of single cells, and then let single cells populate a computational domain. As the number of cancer cells increases over time, competition for environmental resources (such as space) defines population dynamics. A result is a cancer cell population — a tumor — growing sub-exponentially. Tumor progression is dictated by the ability of stem cells to form self-metastases that together form a malignant invasive morphology.

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Tumor Dormancy

 

For many decades, it has been appreciated that tumor progression is not monotonic, and development of a cancer cell does not equate to inevitable cancer presentation in the clinic. Tumor progression is challenged by numerous intrinsic and extrinsic bottlenecks that can hold the tumor in dormant stages for prolonged periods. Understanding how dormancy is controlled would therefore be an invaluable augment to current treatment modalities.

Even after a tumor is established, it can early on enter a state of dormancy marked by balanced cell proliferation and cell death. Although cancer stem cells are necessary for progression, their expansion and consequently tumor growth kinetics are surprisingly modulated by the dynamics of the non-stem cancer cells. Simulations of tumor growth show that cell proliferation, migration, and death combine in unexpected ways to control tumor progression and, thus, clinical cancer risk.

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