Matching Items (8)
Description
No two cancers are alike. Cancer is a dynamic and heterogeneous disease, such heterogeneity arise among patients with the same cancer type, among cancer cells within the same individual’s tumor and even among cells within the same sub-clone over time. The recent application of next-generation sequencing and precision medicine techniques is the driving force to uncover the complexity of cancer and the best clinical practice. The core concept of precision medicine is to move away from crowd-based, best-for-most treatment and take individual variability into account when optimizing the prevention and treatment strategies. Next-generation sequencing is the method to sift through the entire 3 billion letters of each patient’s DNA genetic code in a massively parallel fashion.
The deluge of next-generation sequencing data nowadays has shifted the bottleneck of cancer research from multiple “-omics” data collection to integrative analysis and data interpretation. In this dissertation, I attempt to address two distinct, but dependent, challenges. The first is to design specific computational algorithms and tools that can process and extract useful information from the raw data in an efficient, robust, and reproducible manner. The second challenge is to develop high-level computational methods and data frameworks for integrating and interpreting these data. Specifically, Chapter 2 presents a tool called Snipea (SNv Integration, Prioritization, Ensemble, and Annotation) to further identify, prioritize and annotate somatic SNVs (Single Nucleotide Variant) called from multiple variant callers. Chapter 3 describes a novel alignment-based algorithm to accurately and losslessly classify sequencing reads from xenograft models. Chapter 4 describes a direct and biologically motivated framework and associated methods for identification of putative aberrations causing survival difference in GBM patients by integrating whole-genome sequencing, exome sequencing, RNA-Sequencing, methylation array and clinical data. Lastly, chapter 5 explores longitudinal and intratumor heterogeneity studies to reveal the temporal and spatial context of tumor evolution. The long-term goal is to help patients with cancer, particularly those who are in front of us today. Genome-based analysis of the patient tumor can identify genomic alterations unique to each patient’s tumor that are candidate therapeutic targets to decrease therapy resistance and improve clinical outcome.
The deluge of next-generation sequencing data nowadays has shifted the bottleneck of cancer research from multiple “-omics” data collection to integrative analysis and data interpretation. In this dissertation, I attempt to address two distinct, but dependent, challenges. The first is to design specific computational algorithms and tools that can process and extract useful information from the raw data in an efficient, robust, and reproducible manner. The second challenge is to develop high-level computational methods and data frameworks for integrating and interpreting these data. Specifically, Chapter 2 presents a tool called Snipea (SNv Integration, Prioritization, Ensemble, and Annotation) to further identify, prioritize and annotate somatic SNVs (Single Nucleotide Variant) called from multiple variant callers. Chapter 3 describes a novel alignment-based algorithm to accurately and losslessly classify sequencing reads from xenograft models. Chapter 4 describes a direct and biologically motivated framework and associated methods for identification of putative aberrations causing survival difference in GBM patients by integrating whole-genome sequencing, exome sequencing, RNA-Sequencing, methylation array and clinical data. Lastly, chapter 5 explores longitudinal and intratumor heterogeneity studies to reveal the temporal and spatial context of tumor evolution. The long-term goal is to help patients with cancer, particularly those who are in front of us today. Genome-based analysis of the patient tumor can identify genomic alterations unique to each patient’s tumor that are candidate therapeutic targets to decrease therapy resistance and improve clinical outcome.
ContributorsPeng, Sen (Author) / Dinu, Valentin (Thesis advisor) / Scotch, Matthew (Committee member) / Wallstrom, Garrick (Committee member) / Arizona State University (Publisher)
Created2015
Description
Glioblastomas (GBMs) are the most aggressive type of brain tumor. GBMs are known for their aggressive and invasive nature because of their ability to easily grow and spread into the surrounding areas of the brain. The annual incidence rate of GBM is 2 to 3 people per 100,000 people in the United States and Europe, and the median survival for patients with an aggressive GBM is 14.6 months. The standard of care for GBMs follows a protocol of surgery, radiation concurrent with the chemotherapeutic drug, temozolomide (TMZ), followed by the administration of up to 6 cycles of TMZ in an adjuvant setting. The objective of this retrospective study was to compare the clinical responses in a patient cohort from varying amount of adjuvant TMZ cycles. Using patient overall survival, the responses to TMZ cycles were tested within different groupings, and the patient covariates were analyzed. The results from the different analyses indicated that survival success of GBM patients is not solely dependent on the number of TMZ cycles, but that other covariates can also affect survival outcomes.
ContributorsSuri, Yash (Author) / Swanson, Kristin (Thesis director) / Massey, Susan (Committee member) / School of Geographical Sciences and Urban Planning (Contributor) / School for the Science of Health Care Delivery (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Proteins are essential for most biological processes that constitute life. The function of a protein is encoded within its 3D folded structure, which is determined by its sequence of amino acids. A variation of a single nucleotide in the DNA during transcription (nSNV) can alter the amino acid sequence (i.e., a mutation in the protein sequence), which can adversely impact protein function and sometimes cause disease. These mutations are the most prevalent form of variations in humans, and each individual genome harbors tens of thousands of nSNVs that can be benign (neutral) or lead to disease. The primary way to assess the impact of nSNVs on function is through evolutionary approaches based on positional amino acid conservation. These approaches are largely inadequate in the regime where positions evolve at a fast rate. We developed a method called dynamic flexibility index (DFI) that measures site-specific conformational dynamics of a protein, which is paramount in exploring mechanisms of the impact of nSNVs on function. In this thesis, we demonstrate that DFI can distinguish the disease-associated and neutral nSNVs, particularly for fast evolving positions where evolutionary approaches lack predictive power. We also describe an additional dynamics-based metric, dynamic coupling index (DCI), which measures the dynamic allosteric residue coupling of distal sites on the protein with the functionally critical (i.e., active) sites. Through DCI, we analyzed 200 disease mutations of a specific enzyme called GCase, and a proteome-wide analysis of 75 human enzymes containing 323 neutral and 362 disease mutations. In both cases we observed that sites with high dynamic allosteric residue coupling with the functional sites (i.e., DARC spots) have an increased susceptibility to harboring disease nSNVs. Overall, our comprehensive proteome-wide analysis suggests that incorporating these novel position-specific conformational dynamics based metrics into genomics can complement current approaches to increase the accuracy of diagnosing disease nSNVs. Furthermore, they provide mechanistic insights about disease development. Lastly, we introduce a new, purely sequence-based model that can estimate the dynamics profile of a protein by only utilizing coevolution information, eliminating the requirement of the 3D structure for determining dynamics.
ContributorsButler, Brandon Mac (Author) / Ozkan, S. Banu (Thesis advisor) / Vaiana, Sara (Committee member) / Ghirlanda, Giovanna (Committee member) / Ros, Robert (Committee member) / Arizona State University (Publisher)
Created2016
Description
Over time, tumor treatment resistance inadvertently develops when androgen de-privation therapy (ADT) is applied to metastasized prostate cancer (PCa). To combat tumor resistance, while reducing the harsh side effects of hormone therapy, the clinician may opt to cyclically alternates the patient’s treatment on and off. This method,known as intermittent ADT, is an alternative to continuous ADT that improves the patient’s quality of life while testosterone levels recover between cycles. In this paper,we explore the response of intermittent ADT to metastasized prostate cancer by employing a previously clinical data validated mathematical model to new clinical data from patients undergoing Abiraterone therapy. This cell quota model, a system of ordinary differential equations constructed using Droop’s nutrient limiting theory, assumes the tumor comprises of castration-sensitive (CS) and castration-resistant (CR)cancer sub-populations. The two sub-populations rely on varying levels of intracellular androgen for growth, death and transformation. Due to the complexity of the model,we carry out sensitivity analyses to study the effect of certain parameters on their outputs, and to increase the identifiability of each patient’s unique parameter set. The model’s forecasting results show consistent accuracy for patients with sufficient data,which means the model could give useful information in practice, especially to decide whether an additional round of treatment would be effective.
ContributorsBennett, Justin Klark (Author) / Kuang, Yang (Thesis director) / Kostelich, Eric (Committee member) / Phan, Tin (Committee member) / School of Mathematical and Statistical Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Writing speculative fiction is a valuable method for exploring the potential societal transformations elicited by advances in science and technology. The aim of this project is to use speculative fiction to explore the potential consequences of precision medicine for individuals’ daily lives. Precision medicine is a vision of the future in which medicine is about predicting, and ultimately preventing disease before symptoms arise. The idea is that identification of all the factors that influence health and contribute to disease development will translate to better and less expensive healthcare and empower individuals to take responsibility for maintaining their own health and wellness. That future, as envisioned by the leaders of the Human Genome Project, the Institute for Systems Biology, and the Obama administration’s Precision Medicine Initiative, is assumed to be a shared future, one that everyone desires and that is self-evidently “better” than the present. The aim of writing speculative fiction about a “precision medicine” future is to challenge that assumption, to make clear the values underpinning that vision of precision medicine, and to leave open the question of what other possible futures could be imagined instead.
ContributorsVenkatraman, Richa (Author) / Brian, Jennifer (Thesis advisor) / Maienschein, Jane (Thesis advisor) / Hurlbut, James (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation investigates and describes the concept of precision medicine from historical, conceptual, capital investment, industry strategic, regulatory oversight, and medicalization perspectives. The study examines the various current and ongoing challenges, impacts, assimilations, and actual adaptive measures occurring within each of these areas as a result of the emergence and continued evolution of precision medicine as a medical discipline, as well as the technosocial advancements characteristic of precision medical products, such as companion diagnostics and targeted therapeutics, seeking market entry in the United States. The dissertation argues that there is a disjunction between precision medicine and historical governance, oversight, and medical practice mechanisms. Through case studies of two case products, Foundation Medicine’s F1CDx companion diagnostic and Novartis’ Kymriah CAR-T Cell therapeutic, the dissertation illustrates the impacts, destabilization and destandardization effects, and re-standardization efforts around a precision medicine diagnostic and therapy. As a central contribution, this dissertation demonstrates and illustrates the impact(s) that precision medicinal technologies are having on the technoscientific network involved in the creation, development, evaluation, governance, and implementation of medical products in the United States. Results revealed an emerging precision medical innovation model between and among member components of a precision medical ecosystem comprised of the above-mentioned focal areas and that, to fully understand the emerging precision medical innovation model, it is critical to understand not only the impacts of precision medical technologies on the individual components of the precision medicine ecosystem, but also the impacts, adaptations, assimilations, and occlusions inherent to the ecological relations within and across the ecosystem itself. Findings include the destabilization of the traditional drug development process across all stakeholder areas, characterized by the development of non-linear adaptive processes at both the premarket and post-market phases. Although the findings from this study are significant, it is likely that they are temporary in nature and will continue to evolve in accordance with the further advancement of precision medicine, ultimately re-stabilizing the precision medical development ecosystem.
ContributorsSeabrooke, Lee (Author) / Hurlbut, James B (Thesis advisor) / Miller, Clark (Thesis advisor) / Robert, Jason (Committee member) / Arizona State University (Publisher)
Created2021
Description
Glioblastoma (GBM) is the most aggressive adult brain tumor with a devastating median survival time of about fourteen months post-surgery and standard of care therapy with radiation and temozolomide. The low incidence of GBM, cost of developing novel therapeutics, and time cost of clinical trials are dis-incentives to develop novel therapies. To overcome that obstacle, we investigated the efficacy of repurposing four FDA approved drugs known to cross the blood brain barrier (BBB), minocycline, propranolol, chlorpromazine, and metformin, to inhibit signaling and metabolism in GBM cells.
Minocycline is a tetracycline class broad spectrum antibiotic commonly used to treat severe acne and other skin infections. Propranolol is a beta blocker type heart medication primarily used to treat high blood pressure and irregular heartbeat. Chlorpromazine is a phenothiazine antipsychotic usually used for schizophrenia. Metformin is the most widely used first-line oral treatment for type-2 diabetes. Based on a literature survey, minocycline is expected to prevent the phosphorylation of STAT3, a transcription factor downstream of EGFR; propranolol is expected to disrupt EGFR trafficking; chlorpromazine is expected to target the PI3K/mTOR/Akt signaling pathway; metformin is believed to exploit vulnerabilities in cancer cell metabolism, as well as upregulate AMPK against the PI3K/mTOR/Akt pathway.
Efficacy of minocycline in inhibiting EGFR-driven STAT3 activation was investigated using western blot analysis. Our results demonstrate that Minocycline effectively inhibits activation of EGFR-driven STAT3 in U373 glioma cells at 100μM. The ability of chlorpromazine to inhibit the PI3K/mTOR/Akt pathway was similarly tested via western blot, which showed inhibition of phosphorylated Akt and S6 at 10μM. Efficacy of propranolol in perturbing EGFR trafficking was evaluated using flow cytometry and immunofluorescence, which failed to depict altered membrane-associated EGFR abundance. Finally, concentration-dependent inhibition of colony formation was tested for all four drugs. Propranolol and minocycline showed potential biphasic stimulatory effects at 10μM, but all drugs inhibited cell growth at 50μM and higher. Efficacy of these drugs in the treatment of GBM is being further evaluated using in vitro neurosphere cultures from patients identified as having the cellular vulnerabilities potentially targeted by these drugs. Successful completion of this project will lead to in vivo efficacy testing of these four drugs in orthotopic GBM PDX models.
Minocycline is a tetracycline class broad spectrum antibiotic commonly used to treat severe acne and other skin infections. Propranolol is a beta blocker type heart medication primarily used to treat high blood pressure and irregular heartbeat. Chlorpromazine is a phenothiazine antipsychotic usually used for schizophrenia. Metformin is the most widely used first-line oral treatment for type-2 diabetes. Based on a literature survey, minocycline is expected to prevent the phosphorylation of STAT3, a transcription factor downstream of EGFR; propranolol is expected to disrupt EGFR trafficking; chlorpromazine is expected to target the PI3K/mTOR/Akt signaling pathway; metformin is believed to exploit vulnerabilities in cancer cell metabolism, as well as upregulate AMPK against the PI3K/mTOR/Akt pathway.
Efficacy of minocycline in inhibiting EGFR-driven STAT3 activation was investigated using western blot analysis. Our results demonstrate that Minocycline effectively inhibits activation of EGFR-driven STAT3 in U373 glioma cells at 100μM. The ability of chlorpromazine to inhibit the PI3K/mTOR/Akt pathway was similarly tested via western blot, which showed inhibition of phosphorylated Akt and S6 at 10μM. Efficacy of propranolol in perturbing EGFR trafficking was evaluated using flow cytometry and immunofluorescence, which failed to depict altered membrane-associated EGFR abundance. Finally, concentration-dependent inhibition of colony formation was tested for all four drugs. Propranolol and minocycline showed potential biphasic stimulatory effects at 10μM, but all drugs inhibited cell growth at 50μM and higher. Efficacy of these drugs in the treatment of GBM is being further evaluated using in vitro neurosphere cultures from patients identified as having the cellular vulnerabilities potentially targeted by these drugs. Successful completion of this project will lead to in vivo efficacy testing of these four drugs in orthotopic GBM PDX models.
ContributorsNeal, Tristan Thomas (Co-author) / Neal, Tristan (Co-author) / Byron, Sara (Co-author) / Dhruv, Harshil (Co-author, Committee member) / Berens, Michael (Co-author) / Wilson, Melissa (Thesis director) / Ferdosi, Shayesteh (Committee member) / School of Human Evolution & Social Change (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
A major hindrance to advances in the care of patients with malignant gliomas is the presence of the blood brain barrier (BBB) and blood-brain tumor barrier (BBTB) that greatly restricts drug access from the plasma to the tumor cells. Bubble-assisted Focused Ultrasound (BAFUS) has proven effective in opening the BBB for treatment of glial tumors in adults and pediatric cases. BAFUS has been previously shown to disrupt noninvasively, selectively, and transiently the BBB in small animals in vivo. However, there is a lack of an in vitro preclinical model suitable for testing the genetic determinants of endothelial cell tight junction integrity and vulnerability to the physical disruption. Our BBB organ-on-chip platform will enable precision medicine of brain cancers through identifying patient-specific parameters by which to open the BBB allowing use of drugs and drug combinations otherwise unsuitable. We intend to sequence these in vitro models to verify that the genotype (alleles/SNPs) of tight junction proteins contribute to BBB structure and integrity. To initiate this effort, we report the development of an ultrasound transparent organ-on-chip model populated by iPSC-derived endothelial cells (iPSC-EC) co-cultured with astrocytes. Western blot, immunocytochemistry, and transelectrical endothelial resistance (TEER) studies all convey expression of key EC proteins and marked barrier integrity. Successful iPSC differentiation, tight junction formation, and annotation of tight junction alleles will be presented. Efforts are underway to benchmark device-ultrasound interactions, disruption vulnerability, and determine associations between iPSC-EC genotype and phenotype.
ContributorsIyer, Jayashree (Author) / Acharya, Abhinav (Thesis director) / Berens, Michael E. (Committee member) / Tang, Nanyun (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2022-05