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- All Subjects: Cancer
- Creators: Maley, Carlo
- Creators: School of Mathematical and Statistical Sciences
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
Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.
ContributorsAnderies, Barrett James (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Stepien, Tracy (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Despite the 40-year war on cancer, very limited progress has been made in developing a cure for the disease. This failure has prompted the reevaluation of the causes and development of cancer. One resulting model, coined the atavistic model of cancer, posits that cancer is a default phenotype of the cells of multicellular organisms which arises when the cell is subjected to an unusual amount of stress. Since this default phenotype is similar across cell types and even organisms, it seems it must be an evolutionarily ancestral phenotype. We take a phylostratigraphical approach, but systematically add species divergence time data to estimate gene ages numerically and use these ages to investigate the ages of genes involved in cancer. We find that ancient disease-recessive cancer genes are significantly enriched for DNA repair and SOS activity, which seems to imply that a core component of cancer development is not the regulation of growth, but the regulation of mutation. Verification of this finding could drastically improve cancer treatment and prevention.
ContributorsOrr, Adam James (Author) / Davies, Paul (Thesis director) / Bussey, Kimberly (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Chemistry and Biochemistry (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
Catastrophe events occur rather infrequently, but upon their occurrence, can lead to colossal losses for insurance companies. Due to their size and volatility, catastrophe losses are often treated separately from other insurance losses. In fact, many property and casualty insurance companies feature a department or team which focuses solely on modeling catastrophes. Setting reserves for catastrophe losses is difficult due to their unpredictable and often long-tailed nature. Determining loss development factors (LDFs) to estimate the ultimate loss amounts for catastrophe events is one method for setting reserves. In an attempt to aid Company XYZ set more accurate reserves, the research conducted focuses on estimating LDFs for catastrophes which have already occurred and have been settled. Furthermore, the research describes the process used to build a linear model in R to estimate LDFs for Company XYZ's closed catastrophe claims from 2001 \u2014 2016. This linear model was used to predict a catastrophe's LDFs based on the age in weeks of the catastrophe during the first year. Back testing was also performed, as was the comparison between the estimated ultimate losses and actual losses. Future research consideration was proposed.
ContributorsSwoverland, Robert Bo (Author) / Milovanovic, Jelena (Thesis director) / Zicarelli, John (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Magnetic resonance imaging (MRI) data of metastatic brain cancer patients at the Barrow Neurological Institute sparked interest in the radiology department due to the possibility that tumor size distributions might mimic a power law or an exponential distribution. In order to consider the question regarding the growth trends of metastatic brain tumors, this thesis analyzes the volume measurements of the tumor sizes from the BNI data and attempts to explain such size distributions through mathematical models. More specifically, a basic stochastic cellular automaton model is used and has three-dimensional results that show similar size distributions of those of the BNI data. Results of the models are investigated using the likelihood ratio test suggesting that, when the tumor volumes are measured based on assuming tumor sphericity, the tumor size distributions significantly mimic the power law over an exponential distribution.
ContributorsFreed, Rebecca (Co-author) / Snopko, Morgan (Co-author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / WPC Graduate Programs (Contributor) / School of Accountancy (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
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
Glioblastoma Multiforme (GBM) is an aggressive and deadly form of brain cancer with a median survival time of about a year with treatment. Due to the aggressive nature of these tumors and the tendency of gliomas to follow white matter tracks in the brain, each tumor mass has a unique growth pattern. Consequently it is difficult for neurosurgeons to anticipate where the tumor will spread in the brain, making treatment planning difficult. Archival patient data including MRI scans depicting the progress of tumors have been helpful in developing a model to predict Glioblastoma proliferation, but limited scans per patient make the tumor growth rate difficult to determine. Furthermore, patient treatment between scan points can significantly compound the challenge of accurately predicting the tumor growth. A partnership with Barrow Neurological Institute has allowed murine studies to be conducted in order to closely observe tumor growth and potentially improve the current model to more closely resemble intermittent stages of GBM growth without treatment effects.
ContributorsSnyder, Lena Haley (Author) / Kostelich, Eric (Thesis director) / Frakes, David (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
Cancer rates in our nearest relatives are largely unknown. Comparison of human cancer rates with other primates should help us to understand the nature of our susceptibilities to cancer. Data from deceased primates was gathered from 3 institutions, the Duke Lemur Center, San Diego Zoo, and Jungle Friends primate sanctuary. This data contained over 400 unique individuals across 45 species with information on cancer incidence and mortality. Cancer incidence ranged from 0-71% and cancer mortality ranged from 0-67%. We used weighted phylogenetic regressions to test for an association between life history variables (specifically body mass and lifespan) and cancer incidence as well as mortality. Cancer incidence did not correlate with both body mass and lifespan (p>.05) however, cancer mortality did (p<.05). However, it is uncertain if the variables can be used as reliable predictors of cancer, because the data come from different organizations. This analysis presents cancer incidence rates and cancer mortality rates in species where it was previously unknown, and in some primate species, is surprisingly high. Microcebus murinus(grey mouse lemur) appear to be particularly vulnerable to cancer, mostly lymphomas. Further studies will be required to determine the causes of these vulnerabilities.
ContributorsWalker, William Charles (Author) / Maley, Carlo (Thesis director) / Boddy, Amy (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
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
Description
Over time, tumor treatment resistance inadvertently develops when androgen de-privation therapy (ADT) is applied to metastasized prostate cancer (PCa). To combat tumor resistance, while reducing the harsh side effects of hormone therapy, the clinician may opt to cyclically alternates the patient’s treatment on and off. This method,known as intermittent ADT, is an alternative to continuous ADT that improves the patient’s quality of life while testosterone levels recover between cycles. In this paper,we explore the response of intermittent ADT to metastasized prostate cancer by employing a previously clinical data validated mathematical model to new clinical data from patients undergoing Abiraterone therapy. This cell quota model, a system of ordinary differential equations constructed using Droop’s nutrient limiting theory, assumes the tumor comprises of castration-sensitive (CS) and castration-resistant (CR)cancer sub-populations. The two sub-populations rely on varying levels of intracellular androgen for growth, death and transformation. Due to the complexity of the model,we carry out sensitivity analyses to study the effect of certain parameters on their outputs, and to increase the identifiability of each patient’s unique parameter set. The model’s forecasting results show consistent accuracy for patients with sufficient data,which means the model could give useful information in practice, especially to decide whether an additional round of treatment would be effective.
ContributorsBennett, Justin Klark (Author) / Kuang, Yang (Thesis director) / Kostelich, Eric (Committee member) / Phan, Tin (Committee member) / School of Mathematical and Statistical Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05