Matching Items (10)
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- All Subjects: Cancer
- Creators: Buetow, Kenneth
Description
Due to artificial selection, dogs have high levels of phenotypic diversity, yet, there appears to be low genetic diversity within individual breeds. Through their domestication from wolves, dogs have gone through a series of population bottlenecks, which has resulted in a reduction in genetic diversity, with a large amount of linkage disequilibrium and the persistence of deleterious mutations. This has led to an increased susceptibility to a multitude of diseases, including cancer. To study the effects of artificial selection and life history characteristics on the risk of cancer mortality, we collected cancer mortality data from four studies as well as the percent of heterozygosity, body size, lifespan and breed group for 201 dog breeds. We also collected specific types of cancer breeds were susceptible to and compared the dog cancer mortality patterns to the patterns observed in other mammals. We found a relationship between cancer mortality rate and heterozygosity, body size, lifespan as well as breed group. Higher levels of heterozygosity were also associated with longer lifespan. These results indicate larger breeds, such as Irish Water Spaniels, Flat-coated Retrievers and Bernese Mountain Dogs, are more susceptible to cancer, with lower heterozygosity and lifespan. These breeds are also more susceptible to sarcomas, as opposed to carcinomas in smaller breeds, such as Miniature Pinschers, Chihuahuas, and Pekingese. Other mammals show that larger and long-lived animals have decreased cancer mortality, however, within dog breeds, the opposite relationship is observed. These relationships could be due to the trade-off between cellular maintenance and growing fast and large, with higher expression of growth factors, such as IGF-1. This study further demonstrates the relationships between cancer mortality, heterozygosity, and life history traits and exhibits dogs as an important model organism for understanding the relationship between genetics and health.
ContributorsBalsley, Cassandra Sierra (Author) / Maley, Carlo (Thesis director) / Wynne, Clive (Committee member) / Tollis, Marc (Committee member) / School of Life Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Bats (order Chiroptera) are the longest lived mammals for their size, with particularly extreme longevity evolving in the family Vespertilionidae, or vesper bats. Because of this, researchers have proposed using bats to study ageing and cancer suppression. Here, we study gene duplications across mammalian genomes and show that, similar to previous findings in elephants, bats have experienced duplications of the tumor suppressor gene TP53, including five genomic copies in the genome of the little brown bat (Myotis lucifugus) and two copies in Brandt's bat (Myotis brandtii). These species can live 37 and 41 years, respectively, despite having an adult body mass of only ~7 grams. We use evolutionary genetics and next generation sequencing approaches to show that positive selection has acted on the TP53 locus across bats, and two recently duplicated TP53 gene copies in the little brown bat are both highly conserved and expressed, suggesting they are functional. We also report an extraordinary genomic copy number expansion of the tumor suppressor gene FBXO31 in the common ancestor of vesper bats which accelerated in the Myotis lineage, leading to 34\u201457 copies and the expression of 20 functional FBXO31 homologs in Brandt's bat. As FBXO31 directs the degradation of MDM2, which is a negative regulator of TP53, we suggest that increased expression of both FBXO31 and TP53 may be related to an enhanced DNA-damage response to genotoxic stress brought on by long lifespans and rapid metabolic rates in bats.
ContributorsSchneider-Utaka, Aika Kunigunda (Author) / Maley, Carlo (Thesis director) / Wilson Sayres, Melissa (Committee member) / Tollis, Marc (Committee member) / School of Life Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
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
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
Agassiz’s desert tortoise (Gopherus agassizii) is a long-lived species native to the Mojave Desert and is listed as threatened under the US Endangered Species Act. To aid conservation efforts for preserving the genetic diversity of this species, we generated a whole genome reference sequence with an annotation based on deep transcriptome sequences of adult skeletal muscle, lung, brain, and blood. The draft genome assembly for G. agassizii has a scaffold N50 length of 252 kbp and a total length of 2.4 Gbp. Genome annotation reveals 20,172 protein-coding genes in the G. agassizii assembly, and that gene structure is more similar to chicken than other turtles. We provide a series of comparative analyses demonstrating (1) that turtles are among the slowest-evolving genome-enabled reptiles, (2) amino acid changes in genes controlling desert tortoise traits such as shell development, longevity and osmoregulation, and (3) fixed variants across the Gopherus species complex in genes related to desert adaptations, including circadian rhythm and innate immune response. This G. agassizii genome reference and annotation is the first such resource for any tortoise, and will serve as a foundation for future analysis of the genetic basis of adaptations to the desert environment, allow for investigation into genomic factors affecting tortoise health, disease and longevity, and serve as a valuable resource for additional studies in this species complex.
Data Availability: All genomic and transcriptomic sequence files are available from the NIH-NCBI BioProject database (accession numbers PRJNA352725, PRJNA352726, and PRJNA281763). All genome assembly, transcriptome assembly, predicted protein, transcript, genome annotation, repeatmasker, phylogenetic trees, .vcf and GO enrichment files are available on Harvard Dataverse (doi:10.7910/DVN/EH2S9K).
ContributorsTollis, Marc (Author) / DeNardo, Dale F (Author) / Cornelius, John A (Author) / Dolby, Greer A (Author) / Edwards, Taylor (Author) / Henen, Brian T. (Author) / Karl, Alice E. (Author) / Murphy, Robert W. (Author) / Kusumi, Kenro (Author)
Created2017-05-31
Description
While only the sixth most common cancer globally, liver cancer is the third most deadly. Despite the importance of accurate diagnosis and effective treatment, standard diagnostic tests for most solid organ neoplasms are not required for the most common type of liver cancer, Hepatocellular Carcinoma (HCC). In addition, major discrepancies in the practices currently in place limits the ability to develop more precise oncological treatment and prognosis. This study aimed to identify biomarkers, with potential to more accurately diagnose how far cancer has advanced within a patient and determine prognosis. It is the hope that pathways provided by this study form the basis for future research into more standardized practices and potential treatment based on specific affected biological processes. The PathOlogist tool was utilized to calculate activity metrics for 1,324 biological pathways in 374 The Cancer Genome Atlas (TCGA) hepatocellular carcinoma donors. Further statistical analysis was done on two datasets, formed to identify grade or stage at time of diagnosis for the activity levels calculated by PathOlogist. The datasets were evaluated individually. Based on the variance and normality of each pathway’s activity levels in the respective data sets analysis of variance, Tukey-Kramer, Kruskal-Wallis, and Mann-Whitney-Wilcox tests were performed, when appropriate, to determine any statistically significant differences in pathway activity levels. Pathways were identified in both stage and grade data analyses that show significant differences in activity levels across designation. While some overlap is seen, there was a significant number of pathways unique to either stage or grade. These pathways are known to affect the cell cycle, cellular transport, disease, immune system, and metabolism regulation.
The biological pathways named by this research depict prospective biomarkers for progression of hepatocellular carcinoma per subdivision within both stage and grade. These findings may be instrumental to new methods of early and more accurate diagnosis. The distinct differences in identified pathways in grade and stage illustrate the need for these new methods to not only look at stage but also grade when determining prognosis. Furthermore, the pathways identified herein have potential to aid in the development of targeted treatment based on the affected biological processes.
ContributorsGarrison, Alyssa Cameron (Author) / Buetow, Kenneth (Thesis advisor) / Hinde, Katie (Committee member) / Wilson, Melissa (Committee member) / Arizona State University (Publisher)
Created2022
Pathways of Distinction Analysis of Liver Cancer Data: Genetic Differences Between Males and Females
Description
The Pathways of Distinction Analysis (PoDA) program calculates relationships between a given group of genes contained within a pathway, and a disease state. It was used here to investigate liver cancer, and to explore how genetic variability may contribute to the different rates of development of the disease in males and females. The goal of the study was to identify germline variation that differs by sex in hepatocellular carcinoma. Using the program, multiple pathways and genes were identified to have significant differences in their relationship to liver cancer in males and females. In animal studies, the genes which were identified using the PoDA analysis have been shown to impact liver cancer, often with different results for males and females. While these genes are often the focus in animal models, they are absent from current Genome Wide Association Studies (GWAS) catalogs for humans. By working to bridge the results of animal studies and human studies, the results help to identify the causes of liver cancer, and more specifically, the reason the disease affects males at much higher rates. The differences in pathways identified to be significant for the two sexes indicate the germline variance may play sex-specific roles in the development of hepatocellular carcinoma. Additionally, these results reinforce the capacity of the PoDA analysis to identify genes that may be missed by more traditional GWAS methods. This study lays the groundwork for further investigations into the identified genes and pathways, and how they behave differently within males and females.
ContributorsOlson, Erik Jon (Author) / Buetow, Kenneth (Thesis advisor) / Wilson, Melissa (Committee member) / Cartwright, Reed (Committee member) / Arizona State University (Publisher)
Created2021
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
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
Evolutionary theory provides a rich framework for understanding cancer dynamics across scales of biological organization. The field of cancer evolution has largely been divided into two domains, comparative oncology - the study of cancer across the tree of life, and tumor evolution. This work provides a theoretical framework to unify these subfields with the intent that an understanding of the evolutionary dynamics driving cancer risk at one scale can inform the understanding of the dynamics on another scale. The evolution of multicellular life and the unique vulnerabilities in the cellular mechanisms that underpin it explain the ubiquity of cancer prevalence across the tree of life. The breakdown in cellular cooperation and communication that were required for multicellular life define the hallmarks of cancer. As divergent life histories drove speciation events, it similarly drove divergences in fundamental cancer risk across species. An understanding of the impact that species’ life history theory has on the underlying network of multicellular cooperation and somatic evolution allows for robust predictions on cross-species cancer risk. A large-scale veterinary cancer database is utilized to validate many of the predictions on cancer risk made from life history evolution. Changing scales to the cellular level, it lays predictions on the fate of somatic mutations and the fitness benefits they confer to neoplastic cells compared to their healthy counterparts. The cancer hallmarks, far more than just a way to unify the many seemingly unique pathologies defined as cancer, is a powerful toolset to understand how specific mutations may change the fitness of somatic cells throughout carcinogenesis and tumor progression. Alongside highlighting the significant advances in evolutionary approaches to cancer across scales, this work provides a lucid confirmation that an understanding of both scales provides the most complete portrait of evolutionary cancer dynamics.
ContributorsCompton, Zachary Taylor (Author) / Maley, Carlo C. (Thesis advisor) / Aktipis, Athena (Committee member) / Buetow, Kenneth (Committee member) / Nedelcu, Aurora (Committee member) / Compton, Carolyn (Committee member) / Arizona State University (Publisher)
Created2023