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Description
Efforts to treat prostate cancer have seen an uptick, as the world’s most commoncancer in men continues to have increasing global incidence. Clinically, metastatic
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration

Efforts to treat prostate cancer have seen an uptick, as the world’s most commoncancer in men continues to have increasing global incidence. Clinically, metastatic
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
ContributorsReckell, Trevor (Author) / Kostelich, Eric (Thesis advisor) / Kuang, Yang (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2020
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Description
Adaptive therapy utilizes competitive interactions between resistant and sensitive cells by keeping some sensitive cells to control tumor burden with the aim of increasing overall survival and time to progression. The use of adaptive therapy to treat breast cancer, ovarian cancer, and pancreatic cancer in preclinical models has shown significant

Adaptive therapy utilizes competitive interactions between resistant and sensitive cells by keeping some sensitive cells to control tumor burden with the aim of increasing overall survival and time to progression. The use of adaptive therapy to treat breast cancer, ovarian cancer, and pancreatic cancer in preclinical models has shown significant results in controlling tumor growth. The adaptive therapy model comes from the integrated pest management agricultural strategy, predator prey model, and the unique intra- and inter-tumor heterogeneity of tumors. The purpose of this thesis is to analyze and compare gemcitabine dose response on hormone refractory breast cancer cells retrieved from mice using an adaptive therapy strategy with standard therapy treatment. In this study, we compared intermittent (drug holiday) adaptive therapy with maximum tolerated dose therapy. The MCF7 resistant cell lines to both fulvestrant and palbociclib were injected into the mammary fat pads of 8 weeks old NOD/SCID gamma (NSG) mice which were then treated with gemcitabine. Tumor burden graphs were made to track tumor growth/decline during different treatments while Drug Dose Response (DDR) curves were made to test the sensitivity of the cell lines to the drug gemcitabine. The tumor burden graphs showed success in controlling the tumor burden with intermittent treatment. The DDR curves showed a positive result in using the adaptive therapy treatment method to treat mice with gemcitabine. Due to some fluctuating DDR results, the sensitivity of the cell lines to gemcitabine needs to be further studied by repeating the DDR experiment on the other mice cell lines for stronger results.
ContributorsConti, Aviona Christina (Author) / Maley, Carlo (Thesis advisor) / Blattman, Joseph (Committee member) / Anderson, Karen (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Life history theory offers a powerful framework to understand evolutionary selection pressures and explain how adaptive strategies use the life history trade-off and differences in cancer defenses across the tree of life. There is often some cost to the phenotype of therapeutic resistance and so sensitive cells can usually outcompete

Life history theory offers a powerful framework to understand evolutionary selection pressures and explain how adaptive strategies use the life history trade-off and differences in cancer defenses across the tree of life. There is often some cost to the phenotype of therapeutic resistance and so sensitive cells can usually outcompete resistant cells in the absence of therapy. Adaptive therapy, as an evolutionary and ecologically inspired paradigm in cancer treatment, uses the competitive interactions between drug-sensitive, and drug-resistant subclones to help suppress the drug-resistant subclones. However, there remain several open challenges in designing adaptive therapies, particularly in extending this approach to multiple drugs. Furthermore, the immune system also plays a role in preventing and controlling cancers. Life history theory may help to explain the variation in immune cell levels across the tree of life that likely contributes to variance in cancer prevalence across vertebrates. However, this has not been previously explored. This work 1) describes resistance management for cancer, lessons cancer researchers learned from farmers since adaptive evolutionary strategies were inspired by the management of resistance in agricultural pests, 2) demonstrates how adaptive therapy protocols work with gemcitabine and capecitabine in a hormone-refractory breast cancer mouse model, 3) tests for a relationship between life history strategy and the immune system, and tests for an effect of immune cells levels on cancer prevalence across vertebrates, and 4) provides a novel approach to improve the teaching of life history theory. This work applies lessons that cancer researchers learned from pest managers, who face similar issues of pesticide resistance, to control cancers. It represents the first time that multiple drugs have been used in adaptive therapy for cancer, and the first time that adaptive therapy has been used on hormone-refractory breast cancer. I found that this evolutionary approach to cancer treatment prolongs survival in mice and also selects for the slow life history strategy. I also discovered that species with slower life histories have higher concentrations of white blood cells and a higher percentage of heterophils, monocytes and segmented neutrophils. Moreover, larger platelet size is associated with higher cancer prevalence in mammals.
ContributorsSeyedi, Seyedehsareh (Author) / Maley, Carlo (Thesis advisor) / Blattman, Joseph (Committee member) / Anderson, Karen (Committee member) / Wilson, Melissa (Committee member) / Huijben, Silvie (Committee member) / Gatenby, Robert (Committee member) / Arizona State University (Publisher)
Created2023
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Description
Resistance to existing anti-cancer drugs poses a key challenge in the field of medical oncology, in that it results in the tumor not responding to treatment using the same medications to which it responded previously, leading to treatment failure. Adaptive therapy utilizes evolutionary principles of competitive suppression, leveraging competition between

Resistance to existing anti-cancer drugs poses a key challenge in the field of medical oncology, in that it results in the tumor not responding to treatment using the same medications to which it responded previously, leading to treatment failure. Adaptive therapy utilizes evolutionary principles of competitive suppression, leveraging competition between drug resistant and drug sensitive cells, to keep the population of drug resistant cells under control, thereby extending time to progression (TTP), relative to standard treatment using maximum tolerated dose (MTD). Development of adaptive therapy protocols is challenging, as it involves many parameters, and the number of parameters increase exponentially for each additional drug. Furthermore, the drugs could have a cytotoxic (killing cells directly), or a cytostatic (inhibiting cell division) mechanism of action, which could affect treatment outcome in important ways. I have implemented hybrid agent-based computational models to investigate adaptive therapy, using either a single drug (cytotoxic or cytostatic), or two drugs (cytotoxic or cytostatic), simulating three different adaptive therapy protocols for treatment using a single drug (dose modulation, intermittent, dose-skipping), and seven different treatment protocols for treatment using two drugs: three dose modulation (DM) protocols (DM Cocktail Tandem, DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression), and four fixed-dose (FD) protocols (FD Cocktail Intermittent, FD Ping-Pong Intermittent, FD Cocktail Dose-Skipping, FD Ping-Pong Dose-Skipping). The results indicate a Goldilocks level of drug exposure to be optimum, with both too little and too much drug having adverse effects. Adaptive therapy works best under conditions of strong cellular competition, such as high fitness costs, high replacement rates, or high turnover. Clonal competition is an important determinant of treatment outcome, and as such treatment using two drugs leads to more favorable outcome than treatment using a single drug. Switching drugs every treatment cycle (ping-pong) protocols work particularly well, as well as cocktail dose modulation, particularly when it is feasible to have a highly sensitive measurement of tumor burden. In general, overtreating seems to have adverse survival outcome, and triggering a treatment vacation, or stopping treatment sooner when the tumor is shrinking seems to work well.
ContributorsSaha, Kaushik (Author) / Maley, Carlo C (Thesis advisor) / Forrest, Stephanie (Committee member) / Anderson, Karen S (Committee member) / Cisneros, Luis H (Committee member) / Arizona State University (Publisher)
Created2023
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Description
This review aims to provide a comprehensive review of the most recent literature on adaptive therapy, a promising new approach to cancer treatment that leverages evolutionary theory to prolong tumor control1. By capitalizing on the competition between drug-sensitive and drug-resistant cells, adaptive therapy has led to a paradigm shift in

This review aims to provide a comprehensive review of the most recent literature on adaptive therapy, a promising new approach to cancer treatment that leverages evolutionary theory to prolong tumor control1. By capitalizing on the competition between drug-sensitive and drug-resistant cells, adaptive therapy has led to a paradigm shift in oncology. Through mathematical and in silico models, researchers have examined key factors such as dose timing, cost of resistance, and spatial dynamics in tumor response to adaptive therapy. With a partial focus on preclinical experiments involving ovarian and breast cancer, this review will discuss the demonstrated effectiveness of adaptive therapy in improving progression free survival and tumor control. Through the review process, it was determined that dose modulation outperformed drug-vacation strategies, emphasizing the significance of tumor heterogeneity and spatial structure in accurately modeling adaptive therapy mechanisms. The potential of ongoing clinical trials to improve patient outcomes and long-term treatment efficacy is emphasized, while a thorough analysis of study methodologies shapes the future direction of adaptive therapy research.
ContributorsRichker, Harley (Author) / Maley, Carlo C (Thesis advisor) / Compton, Carolyn (Committee member) / Wilson, Melisaa (Committee member) / Arizona State University (Publisher)
Created2023
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Description
In contrast to traditional chemotherapy for cancer which fails to address tumor heterogeneity, raises patients’ levels of toxicity, and selects for drug-resistant cells, adaptive therapy applies ideas from cancer ecology in employing low-dose drugs to encourage competition between cancerous cells, reducing toxicity and potentially prolonging disease progression. Despite promising results

In contrast to traditional chemotherapy for cancer which fails to address tumor heterogeneity, raises patients’ levels of toxicity, and selects for drug-resistant cells, adaptive therapy applies ideas from cancer ecology in employing low-dose drugs to encourage competition between cancerous cells, reducing toxicity and potentially prolonging disease progression. Despite promising results in some clinical trials, optimizing adaptive therapy routines involves navigating a vast space of combina- torial possibilities, including the number of drugs, drug holiday duration, and drug dosages. Computational models can serve as precursors to efficiently explore this space, narrowing the scope of possibilities for in-vivo and in-vitro experiments which are time-consuming, expensive, and specific to tumor types. Among the existing modeling techniques, agent-based models are particularly suited for studying the spatial inter- actions critical to successful adaptive therapy. In this thesis, I introduce CancerSim, a three-dimensional agent-based model fully implemented in C++ that is designed to simulate tumorigenesis, angiogenesis, drug resistance, and resource competition within a tissue. Additionally, the model is equipped to assess the effectiveness of various adaptive therapy regimens. The thesis provides detailed insights into the biological motivation and calibration of different model parameters. Lastly, I propose a series of research questions and experiments for adaptive therapy that CancerSim can address in the pursuit of advancing cancer treatment strategies.
ContributorsShah, Sanjana Saurin (Author) / Daymude, Joshua J (Thesis advisor) / Forrest, Stephanie (Committee member) / Maley, Carlo C (Committee member) / Arizona State University (Publisher)
Created2023