Matching Items (54)
Description
Genomic structural variation (SV) is defined as gross alterations in the genome broadly classified as insertions/duplications, deletions inversions and translocations. DNA sequencing ushered structural variant discovery beyond laboratory detection techniques to high resolution informatics approaches. Bioinformatics tools for computational discovery of SVs however are still missing variants in the complex cancer genome. This study aimed to define genomic context leading to tool failure and design novel algorithm addressing this context. Methods: The study tested the widely held but unproven hypothesis that tools fail to detect variants which lie in repeat regions. Publicly available 1000-Genomes dataset with experimentally validated variants was tested with SVDetect-tool for presence of true positives (TP) SVs versus false negative (FN) SVs, expecting that FNs would be overrepresented in repeat regions. Further, the novel algorithm designed to informatically capture the biological etiology of translocations (non-allelic homologous recombination and 3&ndashD; placement of chromosomes in cells –context) was tested using simulated dataset. Translocations were created in known translocation hotspots and the novel&ndashalgorithm; tool compared with SVDetect and BreakDancer. Results: 53% of false negative (FN) deletions were within repeat structure compared to 81% true positive (TP) deletions. Similarly, 33% FN insertions versus 42% TP, 26% FN duplication versus 57% TP and 54% FN novel sequences versus 62% TP were within repeats. Repeat structure was not driving the tool's inability to detect variants and could not be used as context. The novel algorithm with a redefined context, when tested against SVDetect and BreakDancer was able to detect 10/10 simulated translocations with 30X coverage dataset and 100% allele frequency, while SVDetect captured 4/10 and BreakDancer detected 6/10. For 15X coverage dataset with 100% allele frequency, novel algorithm was able to detect all ten translocations albeit with fewer reads supporting the same. BreakDancer detected 4/10 and SVDetect detected 2/10 Conclusion: This study showed that presence of repetitive elements in general within a structural variant did not influence the tool's ability to capture it. This context-based algorithm proved better than current tools even with half the genome coverage than accepted protocol and provides an important first step for novel translocation discovery in cancer genome.
ContributorsShetty, Sheetal (Author) / Dinu, Valentin (Thesis advisor) / Bussey, Kimberly (Committee member) / Scotch, Matthew (Committee member) / Wallstrom, Garrick (Committee member) / Arizona State University (Publisher)
Created2014
Description
The processes of a human somatic cell are very complex with various genetic mechanisms governing its fate. Such cells undergo various genetic mutations, which translate to the genetic aberrations that we see in cancer. There are more than 100 types of cancer, each having many more subtypes with aberrations being unique to each. In the past two decades, the widespread application of high-throughput genomic technologies, such as micro-arrays and next-generation sequencing, has led to the revelation of many such aberrations. Known types and subtypes can be readily identified using gene-expression profiling and more importantly, high-throughput genomic datasets have helped identify novel sub-types with distinct signatures. Recent studies showing usage of gene-expression profiling in clinical decision making in breast cancer patients underscore the utility of high-throughput datasets. Beyond prognosis, understanding the underlying cellular processes is essential for effective cancer treatment. Various high-throughput techniques are now available to look at a particular aspect of a genetic mechanism in cancer tissue. To look at these mechanisms individually is akin to looking at a broken watch; taking apart each of its parts, looking at them individually and finally making a list of all the faulty ones. Integrative approaches are needed to transform one-dimensional cancer signatures into multi-dimensional interaction and regulatory networks, consequently bettering our understanding of cellular processes in cancer. Here, I attempt to (i) address ways to effectively identify high quality variants when multiple assays on the same sample samples are available through two novel tools, snpSniffer and NGSPE; (ii) glean new biological insight into multiple myeloma through two novel integrative analysis approaches making use of disparate high-throughput datasets. While these methods focus on multiple myeloma datasets, the informatics approaches are applicable to all cancer datasets and will thus help advance cancer genomics.
ContributorsYellapantula, Venkata (Author) / Dinu, Valentin (Thesis advisor) / Scotch, Matthew (Committee member) / Wallstrom, Garrick (Committee member) / Keats, Jonathan (Committee member) / Arizona State University (Publisher)
Created2014
Description
The ASU COVID-19 testing lab process was developed to operate as the primary testing site for all ASU staff, students, and specified external individuals. Tests are collected at various collection sites, including a walk-in site at the SDFC and various drive-up sites on campus; analysis is conducted on ASU campus and results are distributed virtually to all patients via the Health Services patient portal. The following is a literature review on past implementations of various process improvement techniques and how they can be applied to the ABCTL testing process to achieve laboratory goals. (abstract)
ContributorsKrell, Abby Elizabeth (Co-author) / Bruner, Ashley (Co-author) / Ramesh, Frankincense (Co-author) / Lewis, Gabriel (Co-author) / Barwey, Ishna (Co-author) / Myers, Jack (Co-author) / Hymer, William (Co-author) / Reagan, Sage (Co-author) / Compton, Carolyn (Thesis director) / McCarville, Daniel R. (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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
Introduction: Human papillomavirus (HPV) infection is seen in up to 90% of cases of cervical cancer, the third leading cancer cause of death in women. Current HPV screening focuses on only two HPV types and covers roughly 75% of HPV-associated cervical cancers. A protein based assay to test for antibody biomarkers against 98 HPV antigens from both high and low risk types could provide an inexpensive and reliable method to screen for patients at risk of developing invasive cervical cancer. Methods: 98 codon optimized, commercially produced HPV genes were cloned into the pANT7_cGST vector, amplified in a bacterial host, and purified for mammalian expression using in vitro transcription/translation (IVTT) in a luminescence-based RAPID ELISA (RELISA) assay. Monoclonal antibodies were used to determine immune cross-reactivity between phylogenetically similar antigens. Lastly, several protein characteristics were examined to determine if they correlated with protein expression. Results: All genes were successfully moved into the destination vector and 86 of the 98 genes (88%) expressed protein at an adequate level. A difference was noted in expression by gene across HPV types but no correlation was found between protein size, pI, or aliphatic index and expression. Discussion: Further testing is needed to express the remaining 12 HPV genes. Once all genes have been successfully expressed and purified at high concentrations, DNA will be printed on microscope slides to create a protein microarray. This microarray will be used to screen HPV-positive patient sera for antibody biomarkers that may be indicative of cervical cancer and precancerous cervical neoplasias.
ContributorsMeshay, Ian Matthew (Author) / Anderson, Karen (Thesis director) / Magee, Mitch (Committee member) / Katchman, Benjamin (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
The purpose of this thesis creative project was to create an educational video to present research findings on the increasingly important issue of human biospecimen preanalytic variables. When a human biospecimen, such as blood, urine, or tissue, is removed from the body, it is subjected to a plethora of variables that are not recorded or regulated in a vast majority of cases. Frequently, these samples arrive at the research or pathology lab with an unknown history, then undergo analysis for translational research purposes, or to guide clinical management decisions. Thus, compromised specimen quality caused by preanalytic variables has substantial, and potentially devastating, downstream effects. To identify the preanalytic variables with the greatest impact on blood and tissue specimen quality, 45 articles were gathered using PubMed and Google Scholar databases and cited. Based on the articles, the top five variables with the most detrimental effects were identified for both blood and tissue samples. Multiple sets of parameters ensuring specimen fitness were compared for each of the five variables for each specimen type. Then, specific parameters guaranteeing the fitness of the greatest number of analytes were verified. To present the research findings in greater detail, a paper was written that focused on identifying the top variables and key parameters to ensure analyte fitness. To present the overall issue in an easy-to-digest format, a storyboard and script were created as a guideline for a final video project. Ultimately, two alternate versions of the video were created to pertain to the audience of choice (one version for patients, one version for professionals). It is the hope that these videos will be used as educational tools to continue efforts to standardize and enforce human biospecimen preanalytic variable parameters. This is a necessary step to improve the accuracy of our biomedical research data and the healthcare of patients worldwide.
ContributorsAzcarate, Heather (Author) / Compton, Carolyn (Thesis director) / LaBaer, Joshua (Committee member) / Borges, Chad (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2018-12
Description
Triple-negative breast cancer (TNBC) is defined by the lack of three receptors (estrogen, progesterone, and HER2 receptors) and accounts for 12-17% of breast cancers. TNBC is an aggressive form of the disease associated with high rates of recurrence and mortality within five years. Inhibitor of Growth 4 (ING4) is a gene deleted in 16.5% and downregulated in 34% of breast tumors. The correlation between ING4 deficiencies and advanced tumors and poor patient survival implicates its tumor suppressive function in breast cancer. Low ING4 expression has been correlated with NFκB activation in metastatic breast tumors. Moreover, ING4 has been shown to inhibit NFkB-mediated gene transcription in various cancers, suggesting that ING4 may suppress cancer by inhibiting NFkB activation. However, the contribution of ING4 deficiencies and NFkB activation to aggressive TNBC progression is currently not well understood. We investigated the role of ING4 in the MDAmb231 TNBC cell line by genetically engineering the cells to overexpress or delete ING4. Cell growth and sensitivity to the chemotherapeutic agent doxorubicin were evaluated between the ING4-modified cell lines with or without TNFα to activate NFκB. The results showed that cell growths were comparable between the vector controls and ING4 overexpressing or deleted cell lines. In addition, TNFα treatment did not alter the growths of all cell lines, indicating that ING4 with or without NFkB activation did not play a role in determining the growth rates of TNBC. However, ING4 overexpressing cells were 20-30% more sensitive to 10 μM doxorubicin treatment, whereas ING4-deleted cells were 20-50% more resistant, suggesting that ING4 may determine chemotherapy response in TNBC. These findings suggest that tumors with low levels of ING4 may be more resistant to chemotherapy, thus requiring higher dosage and/or additional chemotherapy in patient treatment. Unexpectedly, TNFα sensitized all cell lines to doxorubicin regardless of ING4 expression levels, suggesting a TNFα function outside of NFκB activation in increasing doxorubicin sensitivity. It implicates that TNFα treatment may increase chemotherapy response in TNBC patients.
ContributorsUngor, Ashley Jordyn (Author) / Capco, David (Thesis director) / Kim, Suwon (Committee member) / Compton, Carolyn (Committee member) / Sanford School of Social and Family Dynamics (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Identifying disease biomarkers may aid in the early detection of breast cancer and improve patient outcomes. Recent evidence suggests that tumors are immunogenic and therefore patients may launch an autoantibody response to tumor associated antigens. Single-chain variable fragments of autoantibodies derived from regional lymph node B cells of breast cancer patients were used to discover these tumor associated biomarkers on protein microarrays. Six candidate biomarkers were discovered from 22 heavy chain-only variable region antibody fragments screened. Validation tests are necessary to confirm the tumorgenicity of these antigens. However, the use of single-chain variable autoantibody fragments presents a novel platform for diagnostics and cancer therapeutics.
ContributorsSharman, M. Camila (Author) / Magee, Dewey (Mitch) (Thesis director) / Wallstrom, Garrick (Committee member) / Petritis, Brianne (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor) / Virginia G. Piper Center for Personalized Diagnostics (Contributor) / Biodesign Institute (Contributor)
Created2012-12
Description
Background: The human papillomavirus (HPV) is the cause of virtually all cervical cancer, with over 520,000 new cases and 275,000 deaths annually. Although there are at least 200 unique HPV strains, only “high-risk” types, may progress to cancer. Serum antibodies to HPV oncoproteins are stable and specific markers that may be able to detect high-grade cervical intraepithelial neoplasia (CIN3). Biomarkers have potential as a rapid, point-of-care HPV screening tool for low resource areas in the way that traditional cytology cannot, and HPV DNA testing is not yet able to.
Methods: We have designed a multiplexed magnetics programmable bead ELISA (MagProBE) to profile the immune responses of the proteins from 11 high-risk HPV types and 2 low-risk types—106 genes in total. HPV genes were optimized for human expression and either built with PCR or commercially purchased, and cloned into the Gateway-compatible pANT7_cGST vector for in vitro transcription/translation (IVTT) in a MagProBE array. Anti-GST antibody (Ab) labeling was then used to measure gene expression.
Results: 53/106 (50%) HPV genes have been cloned and tested for expression of protein. 91% of HPV proteins expressed at levels above the background control (MFI = 2288), and the mean expression was MFI = 4318. Codon-optimized genes have also shown a 20% higher expression over non-codon optimized genes.
Conclusion: Although this research is ongoing, it suggests that gene optimization may improve IVTT expression of HPV proteins in human HeLa lysate. Once the remaining HPV proteins have been expression confirmed, the cDNA for each gene will be printed onto slides and tested in serologic assays to identify potential Ab biomarkers to CIN3.
Methods: We have designed a multiplexed magnetics programmable bead ELISA (MagProBE) to profile the immune responses of the proteins from 11 high-risk HPV types and 2 low-risk types—106 genes in total. HPV genes were optimized for human expression and either built with PCR or commercially purchased, and cloned into the Gateway-compatible pANT7_cGST vector for in vitro transcription/translation (IVTT) in a MagProBE array. Anti-GST antibody (Ab) labeling was then used to measure gene expression.
Results: 53/106 (50%) HPV genes have been cloned and tested for expression of protein. 91% of HPV proteins expressed at levels above the background control (MFI = 2288), and the mean expression was MFI = 4318. Codon-optimized genes have also shown a 20% higher expression over non-codon optimized genes.
Conclusion: Although this research is ongoing, it suggests that gene optimization may improve IVTT expression of HPV proteins in human HeLa lysate. Once the remaining HPV proteins have been expression confirmed, the cDNA for each gene will be printed onto slides and tested in serologic assays to identify potential Ab biomarkers to CIN3.
ContributorsResnik, Jack Isiah (Author) / Anderson, Karen (Thesis director) / Magee, Mitch (Committee member) / Purushothaman, Immanuel (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor)
Created2013-05
Description
For our project, we explored the growth of the ASU BioDesign Clinical Testing Laboratory (ABCTL) from a standard university research lab to a COVID-19 testing facility through a business lens. The lab has pioneered the saliva-test in the Western United States. This thesis analyzes the laboratory from various business concepts and aspects. The business agility of the lab and it’s quickness to innovation has allowed the lab to enjoy great success. Looking into the future, the laboratory has a promising future and will need to answer many questions to remain the premier COVID-19 testing institution in Arizona.
ContributorsQian, Michael (Co-author) / Cosgrove, Samuel (Co-author) / English, Corinne (Co-author) / Agee, Claire (Co-author) / Mattson, Kyle (Co-author) / Compton, Carolyn (Thesis director) / Schneller, Eugene (Committee member) / School of Accountancy (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05