Matching Items (5)
Filtering by
- All Subjects: Biology
Description
The immune system plays a dual role during neoplastic progression. It can suppress tumor growth by eliminating cancer cells, and also promote neoplastic expansion by either selecting for tumor cells that are fitter to survive in an immunocompetent host or by establishing the right conditions within the tumor microenvironment. First, I present a model to study the dynamics of subclonal evolution of cancer. I model selection through time as an epistatic process. That is, the fitness change in a given cell is not simply additive, but depends on previous mutations. Simulation studies indicate that tumors are composed of myriads of small subclones at the time of diagnosis. Because some of these rare subclones harbor pre-existing treatment-resistant mutations, they present a major challenge to precision medicine. Second, I study the question of self and non-self discrimination by the immune system, which is fundamental in the field in cancer immunology. By performing a quantitative analysis of the biochemical properties of thousands of MHC class I peptides, I find that hydrophobicity of T cell receptors contact residues is a hallmark of immunogenic epitopes. Based on these findings, I further develop a computational model to predict immunogenic epitopes which facilitate the development of T cell vaccines against pathogen and tumor antigens. Lastly, I study the effect of early detection in the context of Ebola. I develope a simple mathematical model calibrated to the transmission dynamics of Ebola virus in West Africa. My findings suggest that a strategy that focuses on early diagnosis of high-risk individuals, caregivers, and health-care workers at the pre-symptomatic stage, when combined with public health measures to improve the speed and efficacy of isolation of infectious individuals, can lead to rapid reductions in Ebola transmission.
ContributorsChowell-Puente, Diego (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Anderson, Karen S (Thesis advisor) / Maley, Carlo C (Committee member) / Wilson Sayres, Melissa A (Committee member) / Blattman, Joseph N (Committee member) / Arizona State University (Publisher)
Created2016
Description
Agassiz’s desert tortoise (Gopherus agassizii) is a long-lived species native to the Mojave Desert and is listed as threatened under the US Endangered Species Act. To aid conservation efforts for preserving the genetic diversity of this species, we generated a whole genome reference sequence with an annotation based on deep transcriptome sequences of adult skeletal muscle, lung, brain, and blood. The draft genome assembly for G. agassizii has a scaffold N50 length of 252 kbp and a total length of 2.4 Gbp. Genome annotation reveals 20,172 protein-coding genes in the G. agassizii assembly, and that gene structure is more similar to chicken than other turtles. We provide a series of comparative analyses demonstrating (1) that turtles are among the slowest-evolving genome-enabled reptiles, (2) amino acid changes in genes controlling desert tortoise traits such as shell development, longevity and osmoregulation, and (3) fixed variants across the Gopherus species complex in genes related to desert adaptations, including circadian rhythm and innate immune response. This G. agassizii genome reference and annotation is the first such resource for any tortoise, and will serve as a foundation for future analysis of the genetic basis of adaptations to the desert environment, allow for investigation into genomic factors affecting tortoise health, disease and longevity, and serve as a valuable resource for additional studies in this species complex.
Data Availability: All genomic and transcriptomic sequence files are available from the NIH-NCBI BioProject database (accession numbers PRJNA352725, PRJNA352726, and PRJNA281763). All genome assembly, transcriptome assembly, predicted protein, transcript, genome annotation, repeatmasker, phylogenetic trees, .vcf and GO enrichment files are available on Harvard Dataverse (doi:10.7910/DVN/EH2S9K).
ContributorsTollis, Marc (Author) / DeNardo, Dale F (Author) / Cornelius, John A (Author) / Dolby, Greer A (Author) / Edwards, Taylor (Author) / Henen, Brian T. (Author) / Karl, Alice E. (Author) / Murphy, Robert W. (Author) / Kusumi, Kenro (Author)
Created2017-05-31
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
Cancer is a disease which can affect all animals across the tree of life. Certain species have undergone natural selection to reduce or prevent cancer. Mechanisms to block cancer may include, among others, a species possessing additional paralogues of tumor suppressor genes, or decreasing the number of oncogenes within their genome. To understand cancer prevention patterns across species, I developed a bioinformatic pipeline to identify copies of 545 known tumor suppressor genes and oncogenes across 63 species of mammals. I used phylogenetic regressions to test for associations between cancer gene copy numbers and a species’ life history. I found a significant association between cancer gene copies and species’ longevity quotient. Additional paralogues of tumor suppressor genes and oncogenes is not solely dependent on body size, but rather the balance between body size and longevity. Additionally, there is a significance association between life history traits and genes that are both germline and somatic tumor suppressor genes. The bioinformatic pipeline identified large tumor suppressor gene and oncogene copy numbers in the naked mole rat (Heterocephalus glaber), armadillo (Dasypus novemcinctus), and the two-fingered sloth (Choloepus hoffmanni). These results suggest that increased paralogues of tumor suppressor genes and oncogenes are these species’ modes of cancer resistance.
ContributorsSchneider-Utaka, Aika Kunigunda (Author) / Maley, Carlo C (Thesis advisor) / Wilson, Melissa A. (Committee member) / Tollis, Marc (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