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- Genre: Doctoral Dissertation
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
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
Description
Understanding intratumor heterogeneity and their driver genes is critical to
designing personalized treatments and improving clinical outcomes of cancers. Such
investigations require accurate delineation of the subclonal composition of a tumor, which
to date can only be reliably inferred from deep-sequencing data (>300x depth). The
resulting algorithm from the work presented here, incorporates an adaptive error model
into statistical decomposition of mixed populations, which corrects the mean-variance
dependency of sequencing data at the subclonal level and enables accurate subclonal
discovery in tumors sequenced at standard depths (30-50x). Tested on extensive computer
simulations and real-world data, this new method, named model-based adaptive grouping
of subclones (MAGOS), consistently outperforms existing methods on minimum
sequencing depth, decomposition accuracy and computation efficiency. MAGOS supports
subclone analysis using single nucleotide variants and copy number variants from one or
more samples of an individual tumor. GUST algorithm, on the other hand is a novel method
in detecting the cancer type specific driver genes. Combination of MAGOS and GUST
results can provide insights into cancer progression. Applications of MAGOS and GUST
to whole-exome sequencing data of 33 different cancer types’ samples discovered a
significant association between subclonal diversity and their drivers and patient overall
survival.
designing personalized treatments and improving clinical outcomes of cancers. Such
investigations require accurate delineation of the subclonal composition of a tumor, which
to date can only be reliably inferred from deep-sequencing data (>300x depth). The
resulting algorithm from the work presented here, incorporates an adaptive error model
into statistical decomposition of mixed populations, which corrects the mean-variance
dependency of sequencing data at the subclonal level and enables accurate subclonal
discovery in tumors sequenced at standard depths (30-50x). Tested on extensive computer
simulations and real-world data, this new method, named model-based adaptive grouping
of subclones (MAGOS), consistently outperforms existing methods on minimum
sequencing depth, decomposition accuracy and computation efficiency. MAGOS supports
subclone analysis using single nucleotide variants and copy number variants from one or
more samples of an individual tumor. GUST algorithm, on the other hand is a novel method
in detecting the cancer type specific driver genes. Combination of MAGOS and GUST
results can provide insights into cancer progression. Applications of MAGOS and GUST
to whole-exome sequencing data of 33 different cancer types’ samples discovered a
significant association between subclonal diversity and their drivers and patient overall
survival.
ContributorsAhmadinejad, Navid (Author) / Liu, Li (Thesis advisor) / Maley, Carlo (Committee member) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2019
Description
The human papillomavirus (HPV) is a double-stranded DNA virus responsible for causing upwards of 80% of head and neck cancers in the oropharyngeal region. Current treatments, including surgery, chemotherapy, and/or radiation, are aggressive and elicit toxic effects. HPV is a pathogen that expresses viral-specific oncogenic proteins that play a role in cancer progression. These proteins may serve as potential targets for immunotherapeutic applications. Engineered T cell receptor (TCR) therapy may be an advantageous approach for HPV-associated cancers. In TCR therapy, TCRs are modified to express a receptor that is specific to an immunogenic antigen (part of the virus/cancer capable of eliciting an immune response). Since HPV-associated oropharyngeal cancers typically express unique viral proteins, it is important to identify the TCRs capable of recognizing these proteins. Evidence supports that head and neck cancers typically experience high levels of immune cell infiltration and are subsequently associated with increased survival rates. Most of the immune cell infiltrations in HPV+ HNSCC are CD8+ T lymphocytes, drawing attention to their prospective use in cellular immunotherapies. While TCRs are highly specific, the TCR repertoire is extremely diverse; enabling the immune system to fight off numerous pathogens. In project 1, I review approaches to analyzing TCR diversity and explore the use of DNA origami in retrieving paired TCR sequences from a population. The results determine that DNA origami can be used within a monoclonal population but requires further optimization before being applied in a polyclonal setting. In project 2, I investigate HPV-specific T-cell dysfunction; I detect low frequency HPV-specific CD8+ T cells, determine that they are tumor specific, and show that HPV+HNSCC patients exhibit increased epitope-specific levels of CD8+T cell exhaustion. In project 3, I apply methods to expand and isolate TCRαβ sequences derived from donors stimulated with a previously identified HPV epitope. Single-cell analysis provide ten unique TCRαβ pairs with corresponding CDR3 sequences that may serve as therapeutic candidates. This thesis contributes to fundamental immunology by contributing to the knowledge of T cell dysfunction within HPV+HNSCC and further reveals TCR gene usage within an HPV stimulated population, thus identifying potential TCR pairs for adoptive cell therapies.
ContributorsUlrich, Peaches Rebecca (Author) / Anderson, Karen S (Thesis advisor) / Lake, Douglas (Committee member) / Maley, Carlo (Committee member) / Varsani, Arvind (Committee member) / Arizona State University (Publisher)
Created2020
Description
The representation of a patient’s characteristics as the parameters of a model is a key component in many studies of personalized medicine, where the underlying mathematical models are used to describe, explain, and forecast the course of treatment. In this context, clinical observations form the bridge between the mathematical frameworks and applications. However, the formulation and theoretical studies of the models and the clinical studies are often not completely compatible, which is one of the main obstacles in the application of mathematical models in practice. The goal of my study is to extend a mathematical framework to model prostate cancer based mainly on the concept of cell-quota within an evolutionary framework and to study the relevant aspects for the model to gain useful insights in practice. Specifically, the first aim is to construct a mathematical model that can explain and predict the observed clinical data under various treatment combinations. The second aim is to find a fundamental model structure that can capture the dynamics of cancer progression within a realistic set of data. Finally, relevant clinical aspects such as how the patient's parameters change over the course of treatment and how to incorporate treatment optimization within a framework of uncertainty quantification, will be examined to construct a useful framework in practice.
ContributorsPhan, Tin (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric J (Committee member) / Crook, Sharon (Committee member) / Maley, Carlo (Committee member) / Bryce, Alan (Committee member) / Arizona State University (Publisher)
Created2021
Description
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic, declared in March 2020 resulted in an unprecedented scientific effort that led to the deployment in less than a year of several vaccines to prevent severe disease, hospitalizations, and death from coronavirus disease 2019 (COVID-19). Most vaccine models focus on the production of neutralizing antibodies against the spike (S) to prevent infection. As the virus evolves, new variants emerge that evade neutralizing antibodies produced by natural infection and vaccination, while memory T cell responses are long-lasting and resilient to most of the changes found in variants of concern (VOC). Several lines of evidence support the study of T cell-mediated immunity in SARS-CoV-2 infections. First, T cell reactivity against SARS-CoV-2 is found in both (cluster of differentiation) CD4+ and CD8+ T cell compartments in asymptomatic, mild, and severe recovered COVID-19 patients. Second, an early and stronger CD8+ T cell response correlates with less severe COVID-19 disease [1-4]. Third, both CD4+ and CD8+ T cells that are reactive to SARS-CoV-2 viral antigens are found in healthy unexposed individuals suggesting that cross-reactive and conserved epitopes may be protective against infection.
The current study is focused on the T cell-mediated response, with special attention to conserved, non-spike-cross-reactive epitopes that may be protective against SARS-CoV-2. The first chapter reviews the importance of epitope prediction in understanding the T cell-mediated responses to a pathogen. The second chapter centers on the validation of SARS-CoV-2 CD8+ T cell predicted peptides to find conserved, immunodominant, and immunoprevalent epitopes that can be incorporated into the next generation of vaccines against severe COVID-19 disease. The third chapter explores pre-existing immunity to SARS-CoV-2 in a pre-pandemic cohort and finds two highly immunogenic epitopes that are conserved among human common cold coronaviruses (HCoVs). To end, the fourth chapter explores the concept of T cell receptor (TCR) cross-reactivity by isolating SARS-CoV-2-reactive TCRs to elucidate the mechanisms of cross-reactivity to SARS-CoV-2 and other human coronaviruses (HCoVs).
ContributorsCarmona, Jacqueline (Author) / Anderson, Karen S (Thesis advisor) / Lake, Douglas (Thesis advisor) / Maley, Carlo (Committee member) / Mangone, Marco (Committee member) / LaBaer, Joshua (Committee member) / Arizona State University (Publisher)
Created2023