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- All Subjects: Cancer
- Creators: School of Life Sciences
- Creators: Kuang, Yang
- Resource Type: Text
- Status: Published
Description
In a 2004 paper, John Nagy raised the possibility of the existence of a hypertumor \emph{i.e.}, a focus of aggressively reproducing parenchyma cells that invade part or all of a tumor. His model used a system of nonlinear ordinary differential equations to find a suitable set of conditions for which these hypertumors exist. Here that model is expanded by transforming it into a system of nonlinear partial differential equations with diffusion, advection, and a free boundary condition to represent a radially symmetric tumor growth. Two strains of parenchymal cells are incorporated; one forming almost the entirety of the tumor while the much more aggressive strain
appears in a smaller region inside of the tumor. Simulations show that if the aggressive strain focuses its efforts on proliferating and does not contribute to angiogenesis signaling when in a hypoxic state, a hypertumor will form. More importantly, this resultant aggressive tumor is paradoxically prone to extinction and hypothesize is the cause of necrosis in many vascularized tumors.
appears in a smaller region inside of the tumor. Simulations show that if the aggressive strain focuses its efforts on proliferating and does not contribute to angiogenesis signaling when in a hypoxic state, a hypertumor will form. More importantly, this resultant aggressive tumor is paradoxically prone to extinction and hypothesize is the cause of necrosis in many vascularized tumors.
ContributorsAlvarez, Roberto L (Author) / Milner, Fabio A (Thesis advisor) / Nagy, John D. (Committee member) / Kuang, Yang (Committee member) / Thieme, Horst (Committee member) / Mahalov, Alex (Committee member) / Smith, Hal (Committee member) / Arizona State University (Publisher)
Created2014
Description
The phycologist, M. R. Droop, studied vitamin B12 limitation in the flagellate Monochrysis lutheri and concluded that its specific growth rate depended on the concentration of the vitamin within the cell; i.e. the cell quota of the vitamin B12. The Droop model provides a mathematical expression to link growth rate to the intracellular concentration of a limiting nutrient. Although the Droop model has been an important modeling tool in ecology, it has only recently been applied to study cancer biology. Cancer cells live in an ecological setting, interacting and competing with normal and other cancerous cells for nutrients and space, and evolving and adapting to their environment. Here, the Droop equation is used to model three cancers.
First, prostate cancer is modeled, where androgen is considered the limiting nutrient since most tumors depend on androgen for proliferation and survival. The model's accuracy for predicting the biomarker for patients on intermittent androgen deprivation therapy is tested by comparing the simulation results to clinical data as well as to an existing simpler model. The results suggest that a simpler model may be more beneficial for a predictive use, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting.
Next, two chronic myeloid leukemia models are compared that consider Imatinib treatment, a drug that inhibits the constitutively active tyrosine kinase BCR-ABL. Both models describe the competition of leukemic and normal cells, however the first model also describes intracellular dynamics by considering BCR-ABL as the limiting nutrient. Using clinical data, the differences in estimated parameters between the models and the capacity for each model to predict drug resistance are analyzed.
Last, a simple model is presented that considers ovarian tumor growth and tumor induced angiogenesis, subject to on and off anti-angiogenesis treatment. In this environment, the cell quota represents the intracellular concentration of necessary nutrients provided through blood supply. Mathematical analysis of the model is presented and model simulation results are compared to pre-clinical data. This simple model is able to fit both on- and off-treatment data using the same biologically relevant parameters.
First, prostate cancer is modeled, where androgen is considered the limiting nutrient since most tumors depend on androgen for proliferation and survival. The model's accuracy for predicting the biomarker for patients on intermittent androgen deprivation therapy is tested by comparing the simulation results to clinical data as well as to an existing simpler model. The results suggest that a simpler model may be more beneficial for a predictive use, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting.
Next, two chronic myeloid leukemia models are compared that consider Imatinib treatment, a drug that inhibits the constitutively active tyrosine kinase BCR-ABL. Both models describe the competition of leukemic and normal cells, however the first model also describes intracellular dynamics by considering BCR-ABL as the limiting nutrient. Using clinical data, the differences in estimated parameters between the models and the capacity for each model to predict drug resistance are analyzed.
Last, a simple model is presented that considers ovarian tumor growth and tumor induced angiogenesis, subject to on and off anti-angiogenesis treatment. In this environment, the cell quota represents the intracellular concentration of necessary nutrients provided through blood supply. Mathematical analysis of the model is presented and model simulation results are compared to pre-clinical data. This simple model is able to fit both on- and off-treatment data using the same biologically relevant parameters.
ContributorsEverett, Rebecca Anne (Author) / Kuang, Yang (Thesis advisor) / Nagy, John (Committee member) / Milner, Fabio (Committee member) / Crook, Sharon (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Arizona State University (Publisher)
Created2015
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
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
Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality in the USA and throughout the world. Two phenotypes that promote this deadly outcome are the invasive potential of NSCLC and the emergence of therapeutic resistance in this disease. There is an unmet clinical need to understand the mechanisms that govern NSCLC cell invasion and therapeutic resistance, and to target these phenotypes towards abating the dismal five-year survival of NSCLC. The expression of the tumor necrosis factor receptor superfamily, member 12A (TNFRSF12A; Fn14) correlates with poor patient survival and invasiveness in many tumor types including NSCLC. We hypothesize that suppression of Fn14 will inhibit NSCLC cell motility and reduce cell viability. Here we demonstrate that atorvastatin calcium treatment reduces Fn14 expression in NSCLC cell lines. Prior to Fn14 protein suppression, atorvastatin calcium modulated the expression of the Fn14 modulators P-ERK1/2 and P-NF-κβ. Atorvastatin calcium treatment inhibited the migratory capacity in H1975, H2030 and H1993 cells by at least 55%. When chemotactic migration in H2030 cells was induced by the Fn14 ligand TNF-like weak inducer of apoptosis (TWEAK) treatment, atorvastatin calcium successfully negated any stimulatory effects. Inversely, treatment of NSCLC cells with cholesterol resulted in a statistically significant increase in migration. Depletion of Fn14 expression via siRNA suppressed the migratory effect of cholesterol. Finally, atorvastatin calcium treatment sensitized cells to radiation treatment, reducing cell survival. These data suggest that atorvastatin calcium may inhibit NSCLC invasiveness through a mechanism involving Fn14, and may be a novel therapeutic target in NSCLC tumors expressing Fn14.
ContributorsCornes, Victoria Elisabeth (Author) / Stout, Valerie (Thesis director) / Whitsett, Timothy (Committee member) / Carson, Vashti (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2015-05
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
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 poses a significant burden on the global health system and represents a leading cause of death worldwide. For late-stage cancers, the traditional treatments of chemotherapy, radiation, and surgery are not always viable, and they can pose unnecessary health risks to the patients. New immunotherapies, such as adoptive cell transfer, are being developed and refined to treat such cancers. T cell immunotherapies in particular, where a patient’s T cell lymphocytes are isolated and amplified to be re-infused into the patient or where human cell lines are engineered to express T cell receptors for the recognition of common cancer antigens, are being expanded on because for some cancers, they could be the only option. Constructing an optimal pipeline for cloning and expression of antigen-specific TCRs has significant bearing on the efficacy of engineered cell lines for ACT. Adoptive T cell transfer, while making great strides, has to overcome a diverse T cell repertoire – cloning and expressing antigen-specific TCRs can mediate this understanding. Having identified the high frequency FluM1-specific TCR sequences in stimulated donor PBMCs, it was hypothesized that the antigen-specific TCR could be reconstructed via Gateway cloning methods and tested for expression and functionality. Establishing this pipeline would confirm an ability to properly pair and express the heterodimeric chains. In the context of downstream applications, neoantigens would be used to stimulate T cells, the α and β chains would be paired via single-cell or bulk methods, and instead of Gateway cloning, the CDR3 hypervariable regions α and β chains alone would be co-expressed using Golden Gate assembly methods.
ContributorsHirneise, Gabrielle Rachel (Author) / Anderson, Karen (Thesis director) / Mason, Hugh (Committee member) / Hariadi, Hugh (Committee member) / School of Life Sciences (Contributor, Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Programmed cell death ligand-1 (PD-L1) is an overexpressed protein on many tumor cell types. PD-L1 is involved in normal immune regulation, playing an important role in self-tolerance and controlling autoimmunity. However, ligation of PD-L1 to PD-1 on activated T cells leads to tumor-mediated T cell suppression. Inhibiting the PD-1/PD-L1 pathway has emerged as an effective target for anti-tumor immunotherapies. Monoclonal antibodies (mAbs) targeting tumor-associated antigens such as PD-L1 have proven to be effective checkpoint blockades, improving therapeutic outcomes for cancer patients and receiving FDA approval as first line therapies for some cancers. A single chain variable fragment (scFv) is composed of the variable heavy and light chain regions of a mAb, connected by a flexible linker. We hypothesized that scFv proteins based on the published anti-PD-L1 monoclonal antibody sequences of atezolizumab and avelumab would bind to cell surface PD-L1. Four single chain variable fragments (scFvs) were constructed based on the sequences of these mAbs. PCR was used to assemble, construct, and amplify DNA fragments encoding the scFvs which were subsequently ligated into a eukaryotic expression vector. Mammalian cells were transfected with the scFv and scFv-IgG plasmids. The scFvs were tested for binding to PD-L1 on tumor cell lysates by western blot and to whole tumor cells by staining and flow cytometry analysis. DNA sequence analysis demonstrated that the scFv constructs were successfully amplified and cloned into the expression vectors and recombinant scFvs were produced. The binding capabilities of the scFvs constucts to PD-L1 protein were confirmed by western blot and flow cytometry analysis. This lead to the idea of constructing a CAR T cell engineered to target PD-L1, providing a possible adoptive T cell immunotherapy.
ContributorsPfeffer, Kirsten M. (Author) / Lake, Douglas (Thesis director) / Ho, Thai (Committee member) / Hastings, Karen (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05