Matching Items (65)
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
Small Cell Carcinoma of the Ovary Hypercalcemic Type (SCCOHT) is a rare and highly aggressive ovarian cancer that affects children and young women at a mean age of 24 years. Most SCCOHT patients are diagnosed at an advanced stage and do not respond to chemotherapy. As a result, more than 75% of patients succumb to their disease within 1-2 years. To provide insights into the biological, diagnostic, and therapeutic vulnerabilities of this deadly cancer, a comprehensive characterization of 22 SCCOHT cases and 2 SCCOHT cell lines using microarray and next-generation sequencing technologies was performed. Following histological examination, tumor DNA and RNA were extracted and used for array comparative genomic hybridization and gene expression microarray analyses. In agreement with previous reports, SCCOHT presented consistently diploid profiles with few copy number aberrations. Gene expression analysis showed SCCOHT tumors have a unique gene expression profile unlike that of most common epithelial ovarian carcinomas. Dysregulated cell cycle control, DNA repair, DNA damage-response, nucleosome assembly, neurogenesis and nervous system development were all characteristic of SCCOHT tumors. Sequencing of DNA from SCCOHT patients and cell lines revealed germline and somatic inactivating mutations in the SWI/SNF chromatin-remodeling gene SMARCA4 in 79% (19/24) of SCCOHT patients in addition to SMARCA4 protein loss in 84% (16/19) of SCCOHT tumors, but in only 0.4% (2/485) of other primary ovarian tumors. Ongoing studies are now focusing on identifying treatments for SCCOHT based on therapeutic vulnerabilities conferred by ubiquitous inactivating mutations in SMARCA4 in addition to gene and protein expression data. Our characterization of the molecular landscape of SCCOHT and the breakthrough identification of inactivating SMARCA4 mutations in almost all cases of SCCOHT offers the first significant insight into the molecular pathogenesis of this disease. The loss of SMARCA4 protein is a highly sensitive and specific marker of the disease, highlighting its potential role as a diagnostic marker, and offers the opportunity for genetic testing of family members at risk. Outstanding questions remain about the role of SMARCA4 loss in the biology, histogenesis, diagnosis, and treatment of SCCOHT.
ContributorsRamos, Pilar (Author) / Anderson, Karen (Thesis advisor) / Trent, Jeffrey (Committee member) / Kusumi, Kenro (Committee member) / Lake, Douglas (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
Vaccination remains one of the most effective means for preventing infectious diseases. During viral infection, activated CD8 T cells differentiate into cytotoxic effector cells that directly kill infected cells and produce anti-viral cytokines. Further T cell differentiation results in a population of memory CD8 T cells that have the ability to self-renew and rapidly proliferate into effector cells during secondary infections. However during persistent viral infection, T cell differentiation is disrupted due to sustained antigen stimulation resulting in a loss of T cell effector function. Despite the development of vaccines for a wide range of viral diseases, efficacious vaccines for persistent viral infections have been challenging to design. Immunization against virus T cell epitopes has been proposed as an alternative vaccination strategy for persistent viral infections, such as HIV. However, vaccines that selectively engage T cell responses can result in inappropriate immune responses that increase, rather than prevent, disease. Quantitative models of virus infection and immune response were used to investigate how virus and immune system variables influence pathogenic versus protective T cell responses generated during persistent viral infection. It was determined that an intermediate precursor frequency of virus-specific memory CD8 T cells prior to LCMV infection resulted in maximum T cell mediated pathology. Increased pathology was independent of antigen sensitivity or the diversity of TCR in the CD8 T cell response, but was dependent on CD8 T cell production of TNF and the magnitude of initial virus exposure. The threshold for exhaustion of responding CD8 T cells ultimately influences the precursor frequency that causes enhanced disease.In addition, viral infection can occur in the context of co-infection by heterologous pathogens that modulate immune responses and/or disease. Co-infection of two unrelated viruses in their natural host, Ectromelia virus (ECTV) and Lymphocytic Choriomeningitis virus (LCMV) infection in mice, were studied. ECTV infection can be a lethal infection in mice due in part to the blockade of antiviral cytokines, including Type I Interferons (IFN-I). It was determined that ECTV/LCMV co-infection results in decreased ECTV viral load and amelioration of ECTV-induced disease, presumably due to IFN-I induction by LCMV. However, immune responses to LCMV in ECTV co-infected mice were also lower compared to mice infected with LCMV alone and biased toward effector-memory cell generation. Thus, providing evidence for bi-directional effects of viral co-infection that modulate disease and immunity. Together the results suggest heterogeneity in T cell responses during vaccination with viral vectors may be in part due to heterologous virus infection or vaccine usage and that TNF-blockade may be useful for minimizing pathology while maintaining protection during virus infection. Lastly, quantitative mathematical models of virus and T cell immunity can be useful to generate predictions regarding which molecular and cellular pathways mediate T cell protection versus pathology.
ContributorsMcAfee, Megan (Author) / Blattman, Joseph N (Thesis advisor) / Anderson, Karen (Committee member) / Jacobs, Bertram (Committee member) / Hogue, Brenda (Committee member) / Arizona State University (Publisher)
Created2015
Description
Osteosarcoma is the most common bone cancer in children and adolescents. Patients with metastatic osteosarcoma are typically refractory to treatment. Numerous lines of evidence suggest that cytotoxic T-lymphocytes (CTL) limit the development of metastatic osteosarcoma. I have investigated the role of Programmed Death Receptor-1 (PD-1) in limiting the efficacy of immune mediated control of metastatic osteosarcoma. I show that human metastatic, but not primary, osteosarcoma tumors express the ligand for PD-1 (PD-L1) and that tumor infiltrating CTL express PD-1, suggesting this pathway may limit CTL control of metastatic osteosarcoma in patients. PD-L1 is also expressed on the K7M2 osteosarcoma tumor cell line that establishes metastases in mice, and PD-1 is expressed on tumor infiltrating CTL during disease progression. Blockade of PD-1/PD-L1 interactions dramatically improves the function of osteosarcoma-reactive CTL in vitro and in vivo, and results in decreased tumor burden and increased survival in the K7M2 mouse model of metastatic osteosarcoma. My results suggest that blockade of PD-1/PD-L1 interactions in patients with metastatic osteosarcoma should be pursued as a therapeutic strategy. However, PD-1/PD-L1 blockade treated mice still succumb to disease due to selection of PD-L1 mAb resistant tumor cells via up-regulation of other co-inhibitory T cell receptors. Combinational α-CTLA-4 and α-PD-L1 blockade treated mice were able to completely eradicate metastatic osteosarcoma, and generate immunity to disease. These results suggest that blockade of PD-1/PD-L1 interactions in patients with metastatic osteosarcoma, although improves survival, may lead to tumor resistance, requiring combinational immunotherapies to combat and eradicate disease.
ContributorsLussier, Danielle (Author) / Blattman, Joseph N. (Thesis advisor) / Anderson, Karen (Committee member) / Goldstein, Elliott (Committee member) / Lake, Douglas (Committee member) / Arizona State University (Publisher)
Created2015
Description
V(D)J recombination is responsible for generating an enormous repertoire of immunoglobulins and T cell receptors, therefore it is a centerpiece to the formation of the adaptive immune system. The V(D)J recombination process proceeds through two steps, site-specific cleavage at RSS (Recombination Signal Sequence) site mediated by the RAG recombinase (RAG1/2) and the subsequent imprecise resolution of the DNA ends, which is carried out by the ubiquitous non-homologous end joining pathway (NHEJ). The V(D)J recombination reaction is obliged to be tightly controlled under all circumstances, as it involves generations of DNA double strand breaks, which are considered the most dangerous lesion to a cell. Multifaceted regulatory mechanisms have been evolved to create great diversity of the antigen receptor repertoire while ensuring genome stability. The RAG-mediated cleavage reaction is stringently regulated at both the pre-cleavage stage and the post-cleavage stage. Specifically, RAG1/2 first forms a pre-cleavage complex assembled at the boarder of RSS and coding flank, which ensures the appropriate DNA targeting. Subsequently, this complex initiates site-specific cleavage, generating two types of double stranded DNA breaks, hairpin-ended coding ends (HP-CEs) and blunt signal ends (SEs). After the cleavage, RAG1/2 proteins bind and retain the recombination ends to form post-cleavage complexes (PCC), which collaborates with the NHEJ machinery for appropriate transfer of recombination ends to NHEJ for proper end resolution. However, little is known about the molecular basis of this collaboration, partly attributed to the lack of sensitive assays to reveal the interaction of PCC with HP-CEs. Here, for the first time, by using two complementary fluorescence-based techniques, fluorescence anisotropy and fluorescence resonance energy transfer (FRET), I managed to monitor the RAG1/2-catalyzed cleavage reaction in real time, from the pre-cleavage to the post-cleavage stages. By examining the dynamic fluorescence changes during the RAG-mediated cleavage reactions, and by manipulating the reaction conditions, I was able to characterize some fundamental properties of RAG-DNA interactions before and after cleavage. Firstly, Mg2+, known as a physiological cofactor at the excision step, also promotes the HP-CEs retention in the RAG complex after cleavage. Secondly, the structure of pre-cleavage complex may affect the subsequent collaborations with NHEJ for end resolution. Thirdly, the non-core region of RAG2 may have differential influences on the PCC retention of HP-CEs and SEs. Furthermore, I also provide the first evidence of RAG1-mediated regulation of RAG2. Our study provides important insights into the multilayered regulatory mechanisms, in modulating recombination events in developing lymphocytes and paves the way for possible development of detection and diagnotic markers for defective recombination events that are often associated immunodeficiency and/or lymphoid malignancy.
ContributorsWang, Guannan (Author) / Chang, Yung (Thesis advisor) / Levitus, Marcia (Committee member) / Misra, Rajeev (Committee member) / Anderson, Karen (Committee member) / Arizona State University (Publisher)
Created2012
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
Breast cancer is the leading cause of cancer-related deaths of women in the united states. Traditionally, Breast cancer is predominantly treated by a combination of surgery, chemotherapy, and radiation therapy. However, due to the significant negative side effects associated with these traditional treatments, there has been substantial efforts to develop alternative therapies to treat cancer. One such alternative therapy is a peptide-based therapeutic cancer vaccine. Therapeutic cancer vaccines enhance an individual's immune response to a specific tumor. They are capable of doing this through artificial activation of tumor specific CTLs (Cytotoxic T Lymphocytes). However, in order to artificially activate tumor specific CTLs, a patient must be treated with immunogenic epitopes derived from their specific cancer type. We have identified that the tumor associated antigen, TPD52, is an ideal target for a therapeutic cancer vaccine. This designation was due to the overexpression of TPD52 in a variety of different cancer types. In order to start the development of a therapeutic cancer vaccine for TPD52-related cancers, we have devised a two-step strategy. First, we plan to create a list of potential TPD52 epitopes by using epitope binding and processing prediction tools. Second, we plan to attempt to experimentally identify MHC class I TPD52 epitopes in vitro. We identified 942 potential 9 and 10 amino acid epitopes for the HLAs A1, A2, A3, A11, A24, B07, B27, B35, B44. These epitopes were predicted by using a combination of 3 binding prediction tools and 2 processing prediction tools. From these 942 potential epitopes, we selected the top 50 epitopes ranked by a combination of binding and processing scores. Due to the promiscuity of some predicted epitopes for multiple HLAs, we ordered 38 synthetic epitopes from the list of the top 50 epitope. We also performed a frequency analysis of the TPD52 protein sequence and identified 3 high volume regions of high epitope production. After the epitope predictions were completed, we proceeded to attempt to experimentally detected presented TPD52 epitopes. First, we successful transduced parental K562 cells with TPD52. After transduction, we started the optimization process for the immunoprecipitation protocol. The optimization of the immunoprecipitation protocol proved to be more difficult than originally believed and was the main reason that we were unable to progress past the transduction of the parental cells. However, we believe that we have identified the issues and will be able to complete the experiment in the coming months.
ContributorsWilson, Eric Andrew (Author) / Anderson, Karen (Thesis director) / Borges, Chad (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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 project was to identify proteins associated with the migration and invasion of non-transformed MCF10A mammary epithelial cells with ectopically expressed missense mutations in p53. Because of the prevalence of TP53 missense mutations in basal-like and triple-negative breast cancer tumors, understanding the effect of TP53 mutations on the phenotypic expression of human mammary epithelial cells may offer new therapeutic targets for those currently lacking in treatment options. As such, MCF10A mammary epithelial cells ectopically overexpressing structural mutations (G245S, H179R, R175H, Y163C, Y220C, and Y234C) and DNA-binding mutations (R248Q, R248W, R273C, and R273H) in the DNA-binding domain were selected for use in this project. Overexpression of p53 in the mutant cell lines was confirmed by western blot and q-PCR analysis targeting the V5 epitope tag present in the pLenti4 vector used to transduce TP53 into the mutant cell lines. Characterization of the invasion and migration phenotypes resulting from the overexpression of p53 in the mutant cell lines was achieved using transwell invasion and migration assays with Boyden chambers. Statistical analysis showed that three cell lines—DNA-contact mutants R248W and R273C and structural mutant Y220C—were consistently more migratory and invasive and demonstrated a relationship between the migration and invasion properties of the mutant cell lines. Two families of proteins were then explored: those involved in the Epithelial-Mesenchymal Transition (EMT) and matrix metalloproteinases (MMPs). Results of q-PCR and immunofluorescence analysis of epithelial marker E-cadherin and mesenchymal proteins Slug and Vimentin did not show a clear relationship between mRNA and protein expression levels with the migration and invasiveness phenotypes observed in the transwell studies. Results of western blotting, q-PCR, and zymography of MMP-2 and MMP-9 also did not show any consistent results indicating a definite relationship between MMPs and the overall invasiveness of the cells. Finally, two drugs were tested as possible treatments inhibiting invasiveness: ebselen and SBI-183. These drugs were tested on only the most invasive of the MCF10A p53 mutant cell lines (R248W, R273C, and Y220C). Results of invasion assay following 30 μM treatment with ebselen and SBI-183 showed that ebselen does not inhibit invasiveness; SBI-183, however, did inhibit invasiveness in all three cell lines tested. As such, SBI-183 will be an important compound to study in the future as a treatment that could potentially serve to benefit triple-negative or basal-like breast cancer patients who currently lack therapeutic treatment options.
ContributorsZhang, Kathie Q (Author) / LaBaer, Joshua (Thesis director) / Anderson, Karen (Committee member) / Gonzalez, Laura (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Department of Chemistry and Biochemistry (Contributor)
Created2015-05