Matching Items (6)
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- All Subjects: Cancer
- Creators: Buetow, Kenneth
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Status: Published
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
Pathway analysis helps researchers gain insight into the biology behind gene expression-based data. By applying this data to known biological pathways, we can learn about mutations or other changes in cellular function, such as those seen in cancer. There are many tools that can be used to analyze pathways; however, it can be difficult to find and learn about the which tool is optimal for use in a certain experiment. This thesis aims to comprehensively review four tools, Cytoscape, PaxtoolsR, PathOlogist, and Reactome, and their role in pathway analysis. This is done by applying a known microarray data set to each tool and testing their different functions. The functions of these programs will then be analyzed to determine their roles in learning about biology and assisting new researchers with their experiments. It was found that each tools holds a very unique and important role in pathway analysis. Visualization pathways have the role of exploring individual pathways and interpreting genomic results. Quantification pathways use statistical tests to determine pathway significance. Together one can find pathways of interest and then explore areas of interest.
ContributorsRehling, Thomas Evan (Author) / Buetow, Kenneth (Thesis director) / Wilson, Melissa (Committee member) / School of Life Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Big Data Network Analysis of Genetic Variation and Gene Expression in Individuals with Breast Cancer
Description
The advent of big data analytics tools and frameworks has allowed for a plethora of new approaches to research and analysis, making data sets that were previously too large or complex more accessible and providing methods to collect, store, and investigate non-traditional data. These tools are starting to be applied in more creative ways, and are being used to improve upon traditional computation methods through distributed computing. Statistical analysis of expression quantitative trait loci (eQTL) data has classically been performed using the open source tool PLINK - which runs on high performance computing (HPC) systems. However, progress has been made in running the statistical analysis in the ecosystem of the big data framework Hadoop, resulting in decreased run time, reduced storage footprint, reduced job micromanagement and increased data accessibility. Now that the data can be more readily manipulated, analyzed and accessed, there are opportunities to use the modularity and power of Hadoop to further process the data. This project focuses on adding a component to the data pipeline that will perform graph analysis on the data. This will provide more insight into the relation between various genetic differences in individuals with breast cancer, and the resulting variation - if any - in gene expression. Further, the investigation will look to see if there is anything to be garnered from a perspective shift; applying tools used in classical networking contexts (such as the Internet) to genetically derived networks.
ContributorsRandall, Jacob Christopher (Author) / Buetow, Kenneth (Thesis director) / Meuth, Ryan (Committee member) / Almalih, Sara (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Medulloblastoma is the most common pediatric brain cancer and accounts for 20% of all pediatric brain tumors. Upon diagnosis, patients undergo tumor-resection surgery followed by intense chemotherapy and cerebrospinal irradiation (CSI) regimens. CSI therapy is highly toxic and poorly tolerated in pediatric patients and is known to cause long-term neurocognitive, endocrine, and developmental deficits that often diminish the quality of life for medulloblastoma patients. The development of targeted therapies is necessary for both increasing the chance of survival and reducing treatment-related morbidities. A potential therapeutic target of interest in medulloblastoma is the polyamine biosynthesis pathway. Polyamines are metabolites present in every living organism and are essential for cellular processes such as growth, survival, and differentiation. Recent studies have shown that polyamine production is dysregulated in several cancers, including brain cancers, and have highlighted polyamine biosynthesis as a potential cancer growth dependency. Dysregulated polyamine metabolism has also been linked to several oncogenic drivers, including the WNT, SHH, and MYC signaling pathways that characterize genetically distinct medulloblastoma subgroups. One way to target polyamine biosynthesis is through the inhibition of the rate-limiting enzyme ornithine decarboxylase with difluoromethylornithine (DFMO), an analog of the polyamine precursor ornithine. DFMO is well-tolerated in pediatric populations and exerts minimal toxicities, as shown through neuroblastoma clinical trials, and is a therapy of interest for medulloblastoma. While DFMO has been tested clinically in multiple cancers, few in vitro studies have been performed to understand the exact mechanisms of anti-proliferation and cytotoxicity. Our study screened two immortalized medulloblastoma cell lines, DAOY (SHH) and D283 (non-WNT/non-SHH), and three patient-derived medulloblastoma cell lines, SL00024 (SHH), SL00668 (non-WNT/non-SHH), SL00870 (Unknown subgroup), for DFMO sensitivity and profiled the immortalized medulloblastoma cell line metabolome to understand the interactions between inhibition of polyamine metabolism with other essential metabolic processes and tumor cell growth. We found that medulloblastoma cell lines are sensitive to DFMO and the adaptive response to DFMO in medulloblastoma may be caused by increased oxidative stress and free radical scavenging. Our study hopes to inform the use of DFMO as an anti-cancer therapy in medulloblastoma by understanding the drug’s single-agent anti-proliferative mechanisms.
ContributorsFain, Caitlyn (Author) / Buetow, Kenneth (Thesis director) / Pirrotte, Patrick (Committee member) / Pathak, Khyati (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / School of Life Sciences (Contributor)
Created2024-05
Description
Hepatocellular carcinoma (HCC) is the most common type of liver cancer and has been shown to have genetic factors that contribute to cancer susceptibility. These genetic factors can be studied using Genome-Wide association studies (GWAS), which allow for the assessment of associations between specific biologic markers. Through GWAS, associations can be analyzed to identify genetic components that contribute to the onset of HCC. This study uses an extended version of Pathways of Distinction analysis (PoDA) to identify the subset of SNPs within the Antigen Presentation and Processing Pathway that distinguish cases from controls. Further analysis was performed to explore SNP-SNP association differences between HCC cases and controls using R-squared values and p-values. Three SNPs show significant inter-SNP associations in both HCC cases and controls. Additionally, 4 SNPs showed significant SNP-SNP associations exclusively in the control data set, possibly suggesting that control pathways have a greater degree of genetic regulation and robustness that is lost in carcinogenesis. This result suggests that these SNP associations may contribute to HCC susceptibility.
ContributorsAghili, Ardesher Joshua (Author) / Buetow, Kenneth (Thesis director) / Wilson Sayres, Melissa (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Cancer is a disease in which abnormal cells divide uncontrollably and destroy body tissue, and currently plagues today’s world. Carcinomas are cancers derived from epithelial cells and include breast and prostate cancer. Breast cancer is a type of carcinoma that forms in breast tissue cells. The tumor cells can be further categorized after testing the cells for the presence of certain molecules. Hormone receptor positive breast cancer includes the tumor cells with receptors that respond to the steroid hormones, estrogen and progesterone, or the peptide hormone, HER2. These forms of cancer respond well to chemotherapy and endocrine therapy. On the other hand, triple negative breast cancer (TNBC) is characterized by the lack of hormone receptor expression and tends to have a worse prognosis in women. Prostate cancer forms in the cells of the prostate gland and has been attributed to mutations in androgen receptor ligand specificity. In a subset of triple negative breast cancer, genetic expression profiling has found a luminal androgen receptor that is dependent on androgen signaling. TNBC has also been found to respond well to enzalutamide, a an androgen receptor inhibitor. As the gene of the androgen receptor, AR, is located on the X chromosome and expressed in a variety of tissues, the responsiveness of TNBC to androgen receptor inhibition could be due to the differential usage of isoforms - different gene mRNA transcripts that produce different proteins. Thus, this study analyzed differential gene expression and differential isoform usage between TNBC cancers – that do and do not express the androgen receptor – and prostate cancer in order to better understand the underlying mechanism behind the effectiveness of androgen receptor inhibition in TNBC. Through the analysis of differential gene expression between the TNBC AR+ and AR- conditions, it was found that seven genes are significantly differentially expressed between the two types of tissues. Genes of significance are AR and EN1, which was found to be a potential prognostic marker in a subtype of TNBC. While some genes are differentially expressed between the TNBC AR+ and AR- tissues, the differences in isoform expression between the two tissues do not reflect the difference in gene expression. We discovered 11 genes that exhibited significant isoform switching between AR+ and AR- TNBC and have been found to contribute to cancer characteristics. The genes CLIC1 and RGS5 have been found to help the rapid, uncontrolled growth of cancer cells. HSD11B2, IRAK1, and COL1Al have been found to contribute to general cancer characteristics and metastasis in breast cancer. PSMA7 has been found to play a role in androgen receptor activation. Finally, SIDT1 and GLYATL1 are both associated with breast and prostate cancers. Overall, through the analysis of differential isoform usage between AR+ and AR- samples, we uncovered differences that were not detected by a gene level differential expression analysis. Thus, future work will focus on analyzing differential gene and isoform expression across all types of breast cancer and prostate cancer to better understand the responsiveness of TNBC to androgen receptor inhibition.
ContributorsDeshpande, Anagha J (Author) / Wilson-Sayres, Melissa (Thesis director) / Buetow, Kenneth (Committee member) / Natri, Heini (Committee member) / School of Human Evolution & Social Change (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05