Matching Items (21)
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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

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
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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

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
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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

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.
ContributorsPeng, Sen (Author) / Dinu, Valentin (Thesis advisor) / Scotch, Matthew (Committee member) / Wallstrom, Garrick (Committee member) / Arizona State University (Publisher)
Created2015
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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

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
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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

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
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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

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.
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
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Description

This thesis project is the result of close collaboration with the Arizona State University Biodesign Clinical Testing Laboratory (ABCTL) to document the characteristics of saliva as a test sample, preanalytical considerations, and how the ABCTL utilized saliva testing to develop swift COVID-19 diagnostic tests for the Arizona community. As of

This thesis project is the result of close collaboration with the Arizona State University Biodesign Clinical Testing Laboratory (ABCTL) to document the characteristics of saliva as a test sample, preanalytical considerations, and how the ABCTL utilized saliva testing to develop swift COVID-19 diagnostic tests for the Arizona community. As of April 2021, there have been over 130 million recorded cases of COVID-19 globally, with the United States taking the lead with approximately 31.5 million cases. Developing highly accurate and timely diagnostics has been an important need of our country that the ABCTL has had tremendous success in delivering. Near the start of the pandemic, the ABCTL utilized saliva as a testing sample rather than nasopharyngeal (NP) swabs that were limited in supply, required highly trained medical personnel, and were generally uncomfortable for participants. Results from literature across the globe showed how saliva performed just as well as the NP swabs (the golden standard) while being an easier test to collect and analyze. Going forward, the ABCTL will continue to develop high quality diagnostic tools and adapt to the ever-evolving needs our communities face regarding the COVID-19 pandemic.

ContributorsSmetanick, Jennifer (Author) / Compton, Carolyn (Thesis director) / Magee, Mitch (Committee member) / School of Life Sciences (Contributor) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

In the middle of the COVID-19 epidemic, flaws in the SARS-CoV-2 diagnostic
test were identified by the impending supply shortages of nasopharyngeal swabs and nucleic acid isolation and purification kits. The ASU Biodesign Clinical Testing Lab (ABCTL), which converted from a research lab to SARS-CoV-2 testing lab, was not an exception

In the middle of the COVID-19 epidemic, flaws in the SARS-CoV-2 diagnostic
test were identified by the impending supply shortages of nasopharyngeal swabs and nucleic acid isolation and purification kits. The ASU Biodesign Clinical Testing Lab (ABCTL), which converted from a research lab to SARS-CoV-2 testing lab, was not an exception to these shortages, but the consequences were greater due to its significant testing load in the state of Arizona. In response to the shortages, researchers at The Department of Epidemiology of Microbial Diseases, at the Yale School of Public Health created SalivaDirect method, which is an epidemic effective test, that accounts for limitations of materials, accessibility to specialized lab equipment, time per test, and cost per test. SalivaDirect simplified the diagnostic process by collecting samples via saliva and skipping the nucleic acid extraction and purification, and did it in a way that resulted in a highly sensitive limit of detection of 6-12 SARS-CoV-2 copies/μL with a minimal decrease in positive test agreement.

ContributorsBreshears, Scott (Co-author) / Anderson, Laura (Co-author) / Majhail, Kajol (Co-author) / Raun, Ellen (Co-author) / Smetanick, Jennifer (Co-author) / Compton, Carolyn (Thesis director) / Magee, Mitch (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

The ASU Biodesign Clinical Testing Laboratory began in March 2020 after the severe acute respiratory syndrome, coronavirus 2, began spreading throughout the world. ASU worked towards implementing  its own efficient way of testing for the virus, in order to assist the university but also keep the communities around it safe.

The ASU Biodesign Clinical Testing Laboratory began in March 2020 after the severe acute respiratory syndrome, coronavirus 2, began spreading throughout the world. ASU worked towards implementing  its own efficient way of testing for the virus, in order to assist the university but also keep the communities around it safe. By developing its own strategy for COVID-19 testing, ASU was on the forefront of research by developing new ways to test for the virus. This process began when research labs at ASU were quickly converted into clinical testing laboratories, which used saliva testing to develop swift COVID-19 diagnostic tests for the Arizona community. The lab developed more accurate and time efficient results, while also converting Nasopharyngeal tests to saliva tests. Not only did this allow for fewer amounts of resources required, but more individuals were able to get tested at faster rates. The ASU Biodesign Clinical Testing Laboratory (ABCTL) was able to accomplish this through the adaptation of previous machines and personnel to fit the testing needs of the community. In the future, the ABCTL will continue to adapt to the ever-changing needs of the community in regards to the unprecedented COVID-19 pandemic. The research collected throughout the past year following the breakout of the COVID-19 pandemic is a reflection of the impressive strategy ASU has created to keep its communities safe, while continuously working towards improving not only the testing sites and functions, but also the ways in which an institution approaches and manages an unfortunate impact on diverse communities.

ContributorsMajhail, Kajol (Co-author) / Smetanick, Jennifer (Co-author) / Anderson, Laura (Co-author) / Ruan, Ellen (Co-author) / Shears, Scott (Co-author) / Compton, Carolyn (Thesis director) / Magee, Mitch (Committee member) / School of Life Sciences (Contributor) / School of Human Evolution & Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

This thesis project is part of a larger collaboration documenting the history of the ASU Biodesign Clinical Testing Laboratory (ABCTL). There are many different aspects that need to be considered when transforming to a clinical testing laboratory. This includes the different types of tests performed in the laboratory. In addition

This thesis project is part of a larger collaboration documenting the history of the ASU Biodesign Clinical Testing Laboratory (ABCTL). There are many different aspects that need to be considered when transforming to a clinical testing laboratory. This includes the different types of tests performed in the laboratory. In addition to the diagnostic polymerase chain reaction (PCR) test that is performed detecting the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), antibody testing is also performed in clinical laboratories. Antibody testing is used to detect a previous infection. Antibodies are produced as part of the immune response against SARS-CoV-2. There are many different forms of antibody tests and their sensitives and specificities have been examined and reviewed in the literature. Antibody testing can be used to determine the seroprevalence of the disease which can inform policy decisions regarding public health strategies. The results from antibody testing can also be used for creating new therapeutics like vaccines. The ABCTL recognizes the shifting need of the community to begin testing for previous infections of SARS-CoV-2 and is developing new forms of antibody testing that can meet them.

ContributorsRuan, Ellen (Co-author) / Smetanick, Jennifer (Co-author) / Majhail, Kajol (Co-author) / Anderson, Laura (Co-author) / Breshears, Scott (Co-author) / Compton, Carolyn (Thesis director) / Magee, Mitch (Committee member) / School of Life Sciences (Contributor, Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05