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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

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
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While specific resistance mechanisms to targeted inhibitors in BRAF-mutant cutaneous melanoma have been identified, surprisingly little is known about the rate at which resistance develops under different treatment options. There is increasing evidence that resistance arises from pre-existing clones rather than from de novo mutations, but there remains the need

While specific resistance mechanisms to targeted inhibitors in BRAF-mutant cutaneous melanoma have been identified, surprisingly little is known about the rate at which resistance develops under different treatment options. There is increasing evidence that resistance arises from pre-existing clones rather than from de novo mutations, but there remains the need for a better understanding of how different drugs affect the fitness of clones within a tumor population and promote or delay the emergence of resistance. To this end, we have developed an assay that defines the in vitro rate of adaptation by analyzing the progressive change in sensitivity of a melanoma cell line to different treatments. We performed a proof-of-theory experiment based on the hypothesis that drugs that cause cell death (cytotoxic) impose a higher selection pressure for drug-resistant clones than drugs that cause cell-cycle arrest (cytostatic drugs), thereby resulting in a faster rate of adaptation. We tested this hypothesis by continuously treating the BRAFV600E melanoma cell line A375 with the cytotoxic MEK inhibitor E6201 and the cytostatic MEK inhibitor trametinib, both of which are known to be effective in the setting of constitutive oncogenic signaling driven by the BRAF mutation. While the identification of confounding factors prevented the direct comparison between E6201-treated and trametinib-treated cells, we observed that E6201-treated cells demonstrate decreased drug sensitivity compared to vehicle-treated cells as early as 18 days after treatment begins. We were able to quantify this rate of divergence at 2.6% per passage by measuring the increase over time in average viability difference between drug-treated and vehicle-treated cells within a DDR analysis. We argue that this value correlates to the rate of adaptation. Furthermore, this study includes efforts to establish a barcoded cell line to allow for individual clonal tracking and efforts to identify synergistic and antagonist drug combinations for use in future experiments. Ultimately, we describe here a novel system capable of quantifying adaptation rate in cancer cells undergoing treatment, and we anticipate that this assay will prove helpful in identifying treatment options that circumvent or delay resistance through future hypothesis-driven experiments.
ContributorsDe Luca, Valerie Jean (Author) / Wilson Sayres, Melissa (Thesis director) / Trent, Jeff (Committee member) / Hendricks, William (Committee member) / School of Molecular Sciences (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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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

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
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One of the largest problems facing modern medicine is drug resistance. Many classes of drugs can be rendered ineffective if their target is able to acquire beneficial mutations. While this is an excellent showcase of the power of evolution, it necessitates the development of increasingly stronger drugs to combat resistant

One of the largest problems facing modern medicine is drug resistance. Many classes of drugs can be rendered ineffective if their target is able to acquire beneficial mutations. While this is an excellent showcase of the power of evolution, it necessitates the development of increasingly stronger drugs to combat resistant pathogens. Not only is this strategy costly and time consuming, it is also unsustainable. To contend with this problem, many multi-drug treatment strategies are being explored. Previous studies have shown that resistance to some drug combinations is not possible, for example, resistance to a common antifungal drug, fluconazole, seems impossible in the presence of radicicol. We believe that in order to understand the viability of multi-drug strategies in combating drug resistance, we must understand the full spectrum of resistance mutations that an organism can develop, not just the most common ones. It is possible that rare mutations exist that are resistant to both drugs. Knowing the frequency of such mutations is important for making predictions about how problematic they will be when multi-drug strategies are used to treat human disease. This experiment aims to expand on previous research on the evolution of drug resistance in S. cerevisiae by using molecular barcodes to track ~100,000 evolving lineages simultaneously. The barcoded cells were evolved with serial transfers for seven weeks (200 generations) in three concentrations of the antifungal Fluconazole, three concentrations of the Hsp90 inhibitor Radicicol, and in four combinations of Fluconazole and Radicicol. Sequencing data was used to track barcode frequencies over the course of the evolution, allowing us to observe resistant lineages as they rise and quantify differences in resistance evolution across the different conditions. We were able to successfully observe over 100,000 replicates simultaneously, revealing many adaptive lineages in all conditions. Our results also show clear differences across drug concentrations and combinations, with the highest drug concentrations exhibiting distinct behaviors.

ContributorsApodaca, Samuel (Author) / Geiler-Samerotte, Kerry (Thesis director) / Schmidlin, Kara (Committee member) / Huijben, Silvie (Committee member) / School of Life Sciences (Contributor) / School of Molecular Sciences (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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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

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
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Description
Different populations of evolved E.coli and their ancestors were grown in a variety of single amino acid environments to determine their ability to use that amino acid as a carbon source. Some evolved lines were able to grow in amino acids that their ancestors weren't able to. The source of

Different populations of evolved E.coli and their ancestors were grown in a variety of single amino acid environments to determine their ability to use that amino acid as a carbon source. Some evolved lines were able to grow in amino acids that their ancestors weren't able to. The source of this change in amino acid growth was investigated by testing uptake, searching for candidate mutations, and comparing growth rates of populations with and without certain mutations.
ContributorsKing, Lily (Author) / Ho, Wei-Chin (Thesis director) / Lynch, Michael (Committee member) / Barrett, The Honors College (Contributor) / School of Molecular Sciences (Contributor)
Created2022-05