Matching Items (33)
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- All Subjects: Cancer
- Creators: School of Molecular Sciences
- Creators: Buetow, Kenneth
- Creators: Computer Science and Engineering Program
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
Description
Cancer rates vary between people, between cultures, and between tissue types, driven by clinically relevant distinctions in the risk factors that lead to different cancer types. Despite the importance of cancer location in human health, little is known about tissue-specific cancers in non-human animals. We can gain significant insight into how evolutionary history has shaped mechanisms of cancer suppression by examining how life history traits impact cancer susceptibility across species. Here, we perform multi-level analysis to test how species-level life history strategies are associated with differences in neoplasia prevalence, and apply this to mammary neoplasia within mammals. We propose that the same patterns of cancer prevalence that have been reported across species will be maintained at the tissue-specific level. We used a combination of factor analysis and phylogenetic regression on 13 life history traits across 90 mammalian species to determine the correlation between a life history trait and how it relates to mammary neoplasia prevalence. The factor analysis presented ways to calculate quantifiable underlying factors that contribute to covariance of entangled life history variables. A greater risk of mammary neoplasia was found to be correlated most significantly with shorter gestation length. With this analysis, a framework is provided for how different life history modalities can influence cancer vulnerability. Additionally, statistical methods developed for this project present a framework for future comparative oncology studies and have the potential for many diverse applications.
ContributorsFox, Morgan Shane (Author) / Maley, Carlo C. (Thesis director) / Boddy, Amy (Committee member) / Compton, Zachary (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The goal of this project was to design and create a genetic construct that would allow for <br/>tumor growth to be induced in the center of the wing imaginal disc of Drosophila larvae, the <br/>R85E08 domain, using a heat shock. The resulting transgene would be combined with other <br/>transgenes in a single fly that would allow for simultaneous expression of the oncogene and, in <br/>the surrounding cells, other genes of interest. This system would help establish Drosophila as a <br/>more versatile and reliable model organism for cancer research. Furthermore, pilot studies were <br/>performed, using elements of the final proposed system, to determine if tumor growth is possible <br/>in the center of the disc, which oncogene produces the best results, and if oncogene expression <br/>induced later in development causes tumor growth. Three different candidate genes were <br/>investigated: RasV12, PvrACT, and Avli.
ContributorsSt Peter, John Daniel (Author) / Harris, Rob (Thesis director) / Varsani, Arvind (Committee member) / School of Molecular Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Current studies in Multiple Myeloma suggest that patient tumors and cell lines cluster separately based on gene expression profiles. Hyperdiploid patients are also extremely underrepresented in established human myeloma cell lines (HMCLs). This suggests that the average HMCL model system does not accurately represent the average myeloma patient. To investigate this question we performed a combined CNA and SNV evolutionary comparison between four myeloma tumors and their established HMCLs (JMW-1, VP-6, KAS-6/1-KAS-6/2 and KP-6). We identified copy number changes shared between the tumors and their cell lines (mean of 74 events - 59%), those unique to patients (mean of 21.25 events - 17%), and those only in the cell lines (mean of 30.75 events \u2014 24%). A relapse sample from the JMW-1 patient showed 58% similarity to the primary diagnostic tumor. These data suggest that, on the level of copy number abnormalities, HMCLs show equal levels of evolutionary divergence as that observed within patients. By exome sequencing, patient tumors were 71% similar to their representative HMCLs, with ~12.5% and ~16.5% of SNVs unique to the tumors and HMCLs respectively. The HMCLs studied appear highly representative of the patient from which they were derived, with most differences associated with an enrichment of sub-populations present in the primary tumor. Additionally, our analysis of the KP-6 aCGH data showed that the patient's hyperdiploid karyotype was maintained in its respective HMCL. This discovery confirms the establishment and validation of a novel and potentially clinically relevant hyperdiploid HMCL that could provide a major advance in our ability to understand the pathogenesis and progression of this prominent patient population.
ContributorsBenard, Brooks Avery (Author) / Keats, Jonathan (Thesis director) / Anderson, Karen (Committee member) / Jelinek, Diane (Committee member) / School of Molecular Sciences (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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
Description
Cancer is a disease that occurs in many and perhaps all multicellular organisms. Current research is looking at how different life history characteristics among species could influence cancer rates. Because somatic maintenance is an important component of a species' life history, we hypothesize the same ecological forces shaping the life history of a species should also determine its cancer susceptibility. By looking at varying life histories, potential evolutionary trends could be used to explain differing cancer rates. Life history theory could be an important framework for understanding cancer vulnerabilities with different trade-offs between life history traits and cancer defenses. Birds have diverse life history strategies that could explain differences in cancer suppression. Peto's paradox is the observation that cancer rates do not typically increase with body size and longevity despite an increased number of cell divisions over the animal's lifetime that ought to be carcinogenic. Here we show how Peto’s paradox is negatively correlated for cancer within the clade, Aves. That is, larger, long-lived birds get more cancer than smaller, short-lived birds (p=0.0001; r2= 0.024). Sexual dimorphism in both plumage color and size differ among Aves species. We hypothesized that this could lead to a difference in cancer rates due to the amount of time and energy sexual dimorphism takes away from somatic maintenance. We tested for an association between a variety of life history traits and cancer, including reproductive potential, growth rate, incubation, mating systems, and sexual dimorphism in both color and size. We found male birds get less cancer than female birds (9.8% vs. 11.1%, p=0.0058).
ContributorsDolan, Jordyn Nicole (Author) / Maley, Carlo (Thesis director) / Harris, Valerie (Committee member) / Boddy, Amy (Committee member) / School of Molecular Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Tumor-stroma interactions significantly influence cancer cell metastasis and disease progression. These interactions partly comprise crosstalk between tumor and stromal fibroblasts, but the key molecular mechanisms within the crosstalk governing cancer invasion are still unclear. Here we develop a 3D in vitro organotypic microfluidic to model tumor-stroma interaction by mimicking the spatial organization of the tumor microenvironment on a chip. We co-culture breast cancer and patient-derived fibroblast cells in 3D tumor and stroma regions respectively and combine functional assessments, including cancer cell migration, with transcriptome profiling to unveil the molecular influence of tumor-stroma crosstalk on invasion. This led to the observation that cancer associated fibroblasts enhanced invasion in 3D by inducing the expression of a novel gene of interest, GPNMB, in breast cancer cells resulting in increased migration speed. Importantly, knockdown of GPNMB blunted the influence of CAFs on enhancing cancer invasion. Overall, these results demonstrate the ability of our model to recapitulate patient specific tumor microenvironment to investigate cellular and molecular consequences of tumor-stroma interactions.
ContributorsBarrientos, Eric Salvador (Author) / Nikkhah, Mehdi (Thesis director) / Veldhuizen, Jaime (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Trichoplax adhaerens (Placozoa) is the simplest multicellular animal to be described. This organism lacks nervous tissue, muscle tissue and organs, and is composed of only five cell types organized into three layers. Placozoa are gaining popularity as a model organism due to their simple make-up and completely sequenced genome. The complete sequencing of this organism’s genome has revealed the presence of important genes in cancer such as TP53 and MDM2 genes. Along with the presence of these genes, there are also additional pathways commonly deregulated in cancer that are well conserved in this organism. T. adhaerens are able to survive exposure to 160Gy and even 240Gy of X-ray radiation. Though small dark bodies form within the main body, they tend to extrude those masses, and continue to reproduce afterwards. After exposure to both grades of radiation, there was a greater increase in the apparent population size of the treated population than the control population. There was also a greater decrease in surface area of the organisms exposed to 160Gy than the control organisms. This increase in population and decrease in surface area of the treated organisms could be due to the extruded bodies. We hypothesize that the observed extrusion is a novel cancer defense mechanism for ridding the animal of damaged or mutated cells. This hypothesis should be tested through longitudinal observation and genetic analysis of the extruded bodies.
ContributorsYi, Avalon (Author) / Fortunato, Angelo (Thesis director) / Maley, Carlo (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
Immunotherapy is an effective treatment for cancer which enables the patient's immune system to recognize tumor cells as pathogens. In order to design an individualized treatment, the t cell receptors (TCR) which bind to a tumor's unique antigens need to be determined. We created a convolutional neural network to predict the binding affinity between a given TCR and antigen to enable this.
ContributorsCai, Michael Ray (Author) / Lee, Heewook (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
Description
Developments in structural biology has led to advancements in drug design and vaccine development. By better understanding the macromolecular structure, rational choices can be made to improve factors in such as binding affinity, while reducing promiscuity and off-target interactions, improving the medicines of tomorrow. The majority of diseases have a macromolecular basis where rational drug development can make a large impact. Two challenging protein targets of different medical relevance have been investigated at different stages of determining their structures with the ultimate goal of advancing in drug development. The first protein target is the CapBCA membrane protein complex, a virulence factor from the bacterium Francisella tularensis and the causative agent of tularemia and classified as a potential bioterrorism weapon by the United States. Purification of the individual protein targets from the CapBCA complex is a key and challenging step that has been, so far, a limiting factor towards the structure determination of the whole complex. Here, the purification protocols for the CapB and CapC subunits have been establish, which will allow us to progress towards biophysical and structural studies. The second protein target investigated in this thesis is the catalytically active Taspase1. Taspase1 functions as a non-oncogene addiction protease that coordinates cancer cell proliferation and apoptosis and has been found to be overexpressed in many primary human cancers. Here the structure is presented to 3.04A with the goal of rational drug design of Taspase1 inhibitors. Development of Taspase1 inhibitors has no completion in the drug discovery arena and would function as a new anti-cancer therapeutic. Solving the structures of medically relevant proteins such as these is critical towards rapidly developing treatments and prevention of old and new diseases.
ContributorsJernigan, Rebecca J. (Author) / Fromme, Petra (Thesis director) / Hansen, Debra T. (Committee member) / Martin-Garcia, Jose M. (Committee member) / School of Molecular Sciences (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Cancer is the second leading cause of death in the United States. Cancer is a serious, complex disease which causes cells to grow uncontrollably, causing millions of deaths per year [1]. Cancer is usually caused by a combination of environmental variables and biological pathways. The pathways have a very robust structure normally, but are altered because of cancer, resulting in a loss of connectivity between pathways. In order detect these pathways, a PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) was created, which measures the relative rankings of the genes in each pathway. Applying this algorithm will allow us to figure out what pathways differed significantly in areas with cancer and areas without cancer. This would allow scientists to focus on specific pathways in order to learn more about the cancer and find more effective ways to treat it. So far, analysis using PoTRA has been successfully conducted on hepatocellular carcinoma (HCC) and its subtypes, resulting in all significant pathways found being cancer-associated. Now, using the TCGA data stored in Google Cloud's BigQuery, we created a pipeline to apply PoTRA to other cancer data sets and see how well it cross-applies to other cancers. The results show that even though some modification may need to be made to adapt to other datasets, many significant pathways were found for both HCC and breast cancer.
ContributorsMahesh, Sunny Nishant (Author) / Valentin, Dinu (Thesis director) / Liu, Li (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05