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- All Subjects: adaptive therapy
- Creators: Maley, Carlo
- Creators: Forrest, Stephanie
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 results in controlling tumor growth. The purpose of this thesis is to draft a protocol to study adaptive therapy in a preclinical model of breast cancer on MCF7, estrogen receptor-positive, cells that have evolved resistance to fulvestrant and palbociclib (MCF7 R). In this study, we used two protocols: drug dose adjustment and intermittent therapy. The MCF7 R cell lines were injected into the mammary fat pads of 11-month-old NOD/SCID gamma (NSG) mice (18 mice) which were then treated with gemcitabine.<br/>The results of this experiment did not provide complete information because of the short-term treatments. In addition, we saw an increase in the tumor size of a few of the treated mice, which could be due to the metabolism of the drug at that age, or because of the difference in injection times. Therefore, these adaptive therapy protocols on hormone-refractory breast cancer cell lines will be repeated on young, 6-week old mice by injecting the cell lines at the same time for all mice, which helps the results to be more consistent and accurate.
ContributorsConti, Aviona (Author) / Maley, Carlo (Thesis director) / Blattman, Joseph (Committee member) / Seyedi, Sareh (Committee member) / School of Life Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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 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
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 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
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 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
Description
Phenotypic and molecular profiling demonstrates a high degree of heterogeneity in the breast tumors. TP53 tumor suppressor is mutated in 30% of all breast tumors and the mutation frequency in basal-like subtype is as high as 80% and co-exists with several other somatic mutations in different genes. It was hypothesized that tumor heterogeneity is a result of a combination of neo-morphic functions of specific TP53 driver mutations and distinct co-mutations or the co-drivers for each type of TP53 mutation. The 10 most common p53 missense mutant proteins found in breast cancer patients were ectopically expressed in normal-like mammary epithelial cells and phenotypes associated with various hallmarks of cancer examined. Supporting the hypothesis, a wide spectrum of phenotypic changes in cell survival, resistance to apoptosis and anoikis, cell migration, invasion and polarity was observed in the mutants compared to wildtype p53 expressing cells. The missense mutants R248W, R273C and Y220C were most aggressive. Integrated analysis of ChIP and RNA seq showed distinct promoter binding profiles of the p53 mutant proteins different than wildtype p53, implying altered transcriptional activity of mutant p53 proteins and the phenotypic heterogeneity of tumors. Enrichment and model-based pathway analyses revealed dysregulated adherens junction and focal adhesion pathways associated with the aggressive p53 mutants. As several somatic mutations co-appear with mutant TP53, we performed a functional assay to fish out the relevant collaborating driver mutations, the co-drivers. When PTEN was deleted by CRISPR-Cas9 in non-invasive p53-Y234C mutant cell, an increase in cell invasion was observed justifying the concept of co-drivers. A genome wide CRISPR library-based screen on p53-Y234C and R273C cells identified separate candidate co-driver mutations that promoted cell invasion. The top candidates included several mutated genes in breast cancer patients harboring TP53 mutations and were associated with cytoskeletal and apoptosis resistance pathways. Overall, the combined approach of molecular profiling and functional genomics screen highlighted distinct sets of co-driver mutations that can lead to heterogeneous phenotypes and promote aggressiveness in cells with different TP53 mutation background, which can guide development of novel targeted therapies.
ContributorsPal, Anasuya (Author) / LaBaer, Joshua (Thesis advisor) / Roberson, Robert (Committee member) / Van Horn, Wade (Committee member) / Maley, Carlo (Committee member) / Arizona State University (Publisher)
Created2019
Modeling Spatial Competition and Adaptive Therapy Protocols in Three-Dimensional Vascularized Tumors
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 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