Matching Items (122)
Description
Duchenne muscular dystrophy (DMD) is a lethal, X-linked disease characterized by progressive muscle degeneration. The condition is driven by out-of-frame mutations in the dystrophin gene, and the absence of a functional dystrophin protein ultimately leads to instability of the sarcolemma, skeletal muscle necrosis, and atrophy. While the structural changes that occur in dystrophic muscle are well characterized, resulting changes in muscle-specific gene expression that take place in dystrophin’s absence remain largely uncharacterized, as they are potentially obscured by the characteristic chronic inflammation in dystrophin deficient muscle.
The conservation of the dystrophin gene across metazoans suggests that both vertebrate and invertebrate model systems can provide valuable contributions to the understanding of DMD initiation and progression. Specifically, the invertebrate C. elegans possesses a dystrophin protein ortholog, dys-1, and a mild inflammatory response that is inactive in the muscle, allowing for the characterization of transcriptome rearrangements affecting disease progression independently of inflammation. Furthermore, C. elegans do not possess a satellite cell equivalent, meaning muscle regeneration does not occur. This makes C. elegans unique in that they allow for the study of dystrophin deficiencies without muscle regeneration that may obscure detection of subtle but consequential changes in gene expression.
I hypothesize that gaining a comprehensive definition of both the structural and signaling roles of dystrophin in C. elegans will improve the community’s understanding of the progression of DMD as a whole. To address this hypothesis, I have performed a phylogenetic analysis on the conservation of each member of the dystrophin associated protein complex (DAPC) across 10 species, established an in vivo system to identify muscle-specific changes in gene expression in the dystrophin-deficient C. elegans, and performed a functional analysis to test the biological significance of changes in gene expression identified in my sequencing results. The results from this study indicate that in C. elegans, dystrophin may have a signaling role early in development, and its absence may activate compensatory mechanisms that counteract disease progression. Furthermore, these findings allow for the identification of transcriptome changes that potentially serve as both independent drivers of disease and potential therapeutic targets for the treatment of DMD.
The conservation of the dystrophin gene across metazoans suggests that both vertebrate and invertebrate model systems can provide valuable contributions to the understanding of DMD initiation and progression. Specifically, the invertebrate C. elegans possesses a dystrophin protein ortholog, dys-1, and a mild inflammatory response that is inactive in the muscle, allowing for the characterization of transcriptome rearrangements affecting disease progression independently of inflammation. Furthermore, C. elegans do not possess a satellite cell equivalent, meaning muscle regeneration does not occur. This makes C. elegans unique in that they allow for the study of dystrophin deficiencies without muscle regeneration that may obscure detection of subtle but consequential changes in gene expression.
I hypothesize that gaining a comprehensive definition of both the structural and signaling roles of dystrophin in C. elegans will improve the community’s understanding of the progression of DMD as a whole. To address this hypothesis, I have performed a phylogenetic analysis on the conservation of each member of the dystrophin associated protein complex (DAPC) across 10 species, established an in vivo system to identify muscle-specific changes in gene expression in the dystrophin-deficient C. elegans, and performed a functional analysis to test the biological significance of changes in gene expression identified in my sequencing results. The results from this study indicate that in C. elegans, dystrophin may have a signaling role early in development, and its absence may activate compensatory mechanisms that counteract disease progression. Furthermore, these findings allow for the identification of transcriptome changes that potentially serve as both independent drivers of disease and potential therapeutic targets for the treatment of DMD.
ContributorsHrach, Heather (Author) / Mangone, Marco (Thesis advisor) / LaBaer, Joshua (Committee member) / Newbern, Jason (Committee member) / Rawls, Jeffery (Committee member) / Arizona State University (Publisher)
Created2020
Description
Proteins are a large collection of biomolecules that orchestrate the vital
cellular processes of life. The last decade has witnessed dramatic advances in the
field of proteomics, which broadly include characterizing the composition, structure,
functions, interactions, and modifications of numerous proteins in biological systems,
and elucidating how the miscellaneous components collectively contribute to the
phenotypes associated with various disorders. Such large-scale proteomics studies
have steadily gained momentum with the evolution of diverse high-throughput
technologies. This work illustrates the development of novel high-throughput
proteomics platforms and their applications in translational and structural biology. In
Chapter 1, nucleic acid programmable protein arrays displaying the human
proteomes were applied to immunoprofiling of paired serum and cerebrospinal fluid
samples from patients with Alzheimer’s disease. This high-throughput
immunoproteomic approach allows us to investigate the global antibody responses
associated with Alzheimer’s disease and potentially identify the diagnostic
autoantibody biomarkers. In Chapter 2, a versatile proteomic pipeline based on the
baculovirus-insect cell expression system was established to enable high-throughput
gene cloning, protein production, in vivo crystallization and sample preparation for Xray diffraction. In conjunction with the advanced crystallography methods, this endto-end pipeline promises to substantially facilitate the protein structural
determination. In Chapter 3, modified nucleic acid programmable protein arrays
were developed and used for probing protein-protein interactions at the proteome
level. From the perspective of biomarker discovery, structural proteomics, and
protein interaction networks, this work demonstrated the power of high-throughput
proteomics technologies in myriad applications for proteome-scale structural,
functional, and biomedical research.
cellular processes of life. The last decade has witnessed dramatic advances in the
field of proteomics, which broadly include characterizing the composition, structure,
functions, interactions, and modifications of numerous proteins in biological systems,
and elucidating how the miscellaneous components collectively contribute to the
phenotypes associated with various disorders. Such large-scale proteomics studies
have steadily gained momentum with the evolution of diverse high-throughput
technologies. This work illustrates the development of novel high-throughput
proteomics platforms and their applications in translational and structural biology. In
Chapter 1, nucleic acid programmable protein arrays displaying the human
proteomes were applied to immunoprofiling of paired serum and cerebrospinal fluid
samples from patients with Alzheimer’s disease. This high-throughput
immunoproteomic approach allows us to investigate the global antibody responses
associated with Alzheimer’s disease and potentially identify the diagnostic
autoantibody biomarkers. In Chapter 2, a versatile proteomic pipeline based on the
baculovirus-insect cell expression system was established to enable high-throughput
gene cloning, protein production, in vivo crystallization and sample preparation for Xray diffraction. In conjunction with the advanced crystallography methods, this endto-end pipeline promises to substantially facilitate the protein structural
determination. In Chapter 3, modified nucleic acid programmable protein arrays
were developed and used for probing protein-protein interactions at the proteome
level. From the perspective of biomarker discovery, structural proteomics, and
protein interaction networks, this work demonstrated the power of high-throughput
proteomics technologies in myriad applications for proteome-scale structural,
functional, and biomedical research.
ContributorsTang, Yanyang (Author) / LaBaer, Joshua (Thesis advisor) / Anderson, Karen S (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2020
Description
Eosinophils are innate immune cells that are most commonly associated with parasite infection and allergic responses. Recent studies, though, have identified eosinophils as cells with diverse effector functions at baseline and in disease. Eosinophils in specific tissue immune environments are proposed to promote unique and specific effector functions, suggesting these cells have the capacity to differentiate into unique subtypes. The studies here focus on defining these subtypes using functional, molecular, and genetic analysis as well as using novel techniques to image these subtypes in situ.
To characterized these subtypes, an in vitro cytokine induced type 1 (E1) and type 2 (E2) eosinophil model was developed that display features and functions of eosinophils found in vivo. For example, E1 eosinophils secrete type 1 mediators (e.g., IL-12, CXCL9 and CXCL10), express iNOS and express increased levels of the surface molecules PDL1 and MHC-I. Conversely, E2 eosinophils release type 2 mediators (e.g., IL4, IL13, CCL17, and CCL22), degranulate and express increased surface molecules CD11b, ST2 and Siglec-F. Completion of differential expression analysis of RNAseq on these subtypes revealed 500 and 655 unique genes were upregulated in E1 and E2 eosinophils, respectively. Functional enrichment studies showed interferon regulatory factor (IRF) transcription factors were uniquely regulated in both mouse and human E1 and E2 eosinophils. These subtypes are sensitive to their environment, modulating their IRF and cell surface expression when stimulated with opposing cytokines, suggesting plasticity.
To identify and study these subtypes in situ, chromogenic and fluorescent eosinophil-specific immunostaining protocols were developed. Methods were created and optimized, here, to identify eosinophils by their granule proteins in formalin fixed mouse tissues. Yet, eosinophil-specific antibodies alone are not enough to identify and study the complex interactions eosinophil subtypes perform within a tissue. Therefore, as part of this thesis, a novel highly-multiplexed immunohistochemistry technique was developed utilizing cleavable linkers to address these concerns. This technique is capable of analyzing up to 22 markers within a single biopsy with single-cell resolution. With this approach, eosinophil subtypes can be studied in situ in routine patient biopsies.
To characterized these subtypes, an in vitro cytokine induced type 1 (E1) and type 2 (E2) eosinophil model was developed that display features and functions of eosinophils found in vivo. For example, E1 eosinophils secrete type 1 mediators (e.g., IL-12, CXCL9 and CXCL10), express iNOS and express increased levels of the surface molecules PDL1 and MHC-I. Conversely, E2 eosinophils release type 2 mediators (e.g., IL4, IL13, CCL17, and CCL22), degranulate and express increased surface molecules CD11b, ST2 and Siglec-F. Completion of differential expression analysis of RNAseq on these subtypes revealed 500 and 655 unique genes were upregulated in E1 and E2 eosinophils, respectively. Functional enrichment studies showed interferon regulatory factor (IRF) transcription factors were uniquely regulated in both mouse and human E1 and E2 eosinophils. These subtypes are sensitive to their environment, modulating their IRF and cell surface expression when stimulated with opposing cytokines, suggesting plasticity.
To identify and study these subtypes in situ, chromogenic and fluorescent eosinophil-specific immunostaining protocols were developed. Methods were created and optimized, here, to identify eosinophils by their granule proteins in formalin fixed mouse tissues. Yet, eosinophil-specific antibodies alone are not enough to identify and study the complex interactions eosinophil subtypes perform within a tissue. Therefore, as part of this thesis, a novel highly-multiplexed immunohistochemistry technique was developed utilizing cleavable linkers to address these concerns. This technique is capable of analyzing up to 22 markers within a single biopsy with single-cell resolution. With this approach, eosinophil subtypes can be studied in situ in routine patient biopsies.
ContributorsNAZAROFF, CHRISTOPHER D. (Author) / Guo, Jia (Thesis advisor) / Rank, Matthew A (Thesis advisor) / LaBaer, Joshua (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2020
Description
Solid tumors advance from benign stage to a deadly metastatic state due to the complex interaction between cancer cells and tumor microenvironment (TME) including stromal cells and extracellular matrix (ECM). Multiple studies have demonstrated that ECM dysregulation is one of the critical hallmarks of cancer progression leading to formation of a desmoplastic microenvironment that participates in tumor progression. Cancer associated fibroblasts (CAFs) are the predominant stromal cell type that participates in desmoplasia by depositing matrix proteins and increasing ECM stiffness. Although the influence of matrix stiffness on enhanced tumorigenicity has been well studied, the biological understanding about the dynamic changes in ECM architecture and the role of cancer-stromal cell interaction on ECM remodeling is still limited.
In this dissertation, the primary goal was to develop a comprehensive cellular and molecular level understanding of ECM remodeling due to the interaction of breast tumor cells and CAFs. To that end, a novel three-dimensional (3D) high-density tumor-stroma model was fabricated in which breast tumor cells (MDA-MB-231 and MCF7) were spatially organized surrounded by CAF-embedded collagen-I hydrogel (Aim 1). Further the platform was integrated with atomic force microscopy to assess the dynamic changes in ECM composition and stiffness during active tumor invasion. The results established an essential role of crosstalk between breast tumor cells and CAFs in ECM remodeling. The studies were further extended by dissecting the mode of interaction between tumor cells and CAFs followed by characterization of the role of various tumor secreted factors on ECM remodeling (Aim 2). The results for the first time established a critical role of paracrine signaling between breast tumor cells and CAFs in modulating biophysical properties of ECM. More in-depth analysis highlighted the role of tumor secreted cytokines, specifically PDGF-AA/BB, on CAF-induced desmoplasia. In aim 3, the platform was further utilized to test the synergistic influence of anti-fibrotic drug (tranilast) in conjugation with chemotherapeutic drug (Doxorubicin) on desmoplasia and tumor progression in the presence of CAFs. Overall this dissertation provided an in-depth understanding on the impact of breast cancer-stromal cell interaction in modulating biophysical properties of the ECM and identified the crucial role of tumor secreted cytokines including PDGF-AA/BB on desmoplasia.
In this dissertation, the primary goal was to develop a comprehensive cellular and molecular level understanding of ECM remodeling due to the interaction of breast tumor cells and CAFs. To that end, a novel three-dimensional (3D) high-density tumor-stroma model was fabricated in which breast tumor cells (MDA-MB-231 and MCF7) were spatially organized surrounded by CAF-embedded collagen-I hydrogel (Aim 1). Further the platform was integrated with atomic force microscopy to assess the dynamic changes in ECM composition and stiffness during active tumor invasion. The results established an essential role of crosstalk between breast tumor cells and CAFs in ECM remodeling. The studies were further extended by dissecting the mode of interaction between tumor cells and CAFs followed by characterization of the role of various tumor secreted factors on ECM remodeling (Aim 2). The results for the first time established a critical role of paracrine signaling between breast tumor cells and CAFs in modulating biophysical properties of ECM. More in-depth analysis highlighted the role of tumor secreted cytokines, specifically PDGF-AA/BB, on CAF-induced desmoplasia. In aim 3, the platform was further utilized to test the synergistic influence of anti-fibrotic drug (tranilast) in conjugation with chemotherapeutic drug (Doxorubicin) on desmoplasia and tumor progression in the presence of CAFs. Overall this dissertation provided an in-depth understanding on the impact of breast cancer-stromal cell interaction in modulating biophysical properties of the ECM and identified the crucial role of tumor secreted cytokines including PDGF-AA/BB on desmoplasia.
ContributorsSaini, Harpinder (Author) / Nikkhah, Mehdi (Thesis advisor) / Ros, Robert (Committee member) / LaBaer, Joshua (Committee member) / Kodibagkar, Vikram (Committee member) / Ebrahimkhani, Mohammad (Committee member) / Arizona State University (Publisher)
Created2019
Description
Phenotypic and molecular profiling demonstrates a high degree of heterogeneity in the breast tumors. TP53 tumor suppressor is mutated in 30% of all breast tumors and the mutation frequency in basal-like subtype is as high as 80% and co-exists with several other somatic mutations in different genes. It was hypothesized that tumor heterogeneity is a result of a combination of neo-morphic functions of specific TP53 driver mutations and distinct co-mutations or the co-drivers for each type of TP53 mutation. The 10 most common p53 missense mutant proteins found in breast cancer patients were ectopically expressed in normal-like mammary epithelial cells and phenotypes associated with various hallmarks of cancer examined. Supporting the hypothesis, a wide spectrum of phenotypic changes in cell survival, resistance to apoptosis and anoikis, cell migration, invasion and polarity was observed in the mutants compared to wildtype p53 expressing cells. The missense mutants R248W, R273C and Y220C were most aggressive. Integrated analysis of ChIP and RNA seq showed distinct promoter binding profiles of the p53 mutant proteins different than wildtype p53, implying altered transcriptional activity of mutant p53 proteins and the phenotypic heterogeneity of tumors. Enrichment and model-based pathway analyses revealed dysregulated adherens junction and focal adhesion pathways associated with the aggressive p53 mutants. As several somatic mutations co-appear with mutant TP53, we performed a functional assay to fish out the relevant collaborating driver mutations, the co-drivers. When PTEN was deleted by CRISPR-Cas9 in non-invasive p53-Y234C mutant cell, an increase in cell invasion was observed justifying the concept of co-drivers. A genome wide CRISPR library-based screen on p53-Y234C and R273C cells identified separate candidate co-driver mutations that promoted cell invasion. The top candidates included several mutated genes in breast cancer patients harboring TP53 mutations and were associated with cytoskeletal and apoptosis resistance pathways. Overall, the combined approach of molecular profiling and functional genomics screen highlighted distinct sets of co-driver mutations that can lead to heterogeneous phenotypes and promote aggressiveness in cells with different TP53 mutation background, which can guide development of novel targeted therapies.
ContributorsPal, Anasuya (Author) / LaBaer, Joshua (Thesis advisor) / Roberson, Robert (Committee member) / Van Horn, Wade (Committee member) / Maley, Carlo (Committee member) / Arizona State University (Publisher)
Created2019
Description
This thesis describes the development, characterization, and application of new biomedical technologies developed around the photoacoustic effect. The photoacoustic effect is defined as optical absorption-based generation of ultrasound and provides the foundation for a unique method of imaging and molecular detection. The range of applications of the photoacoustic effect have not yet been fully explored. Photoacoustic endoscopy (PAE) has emerged as a minimally invasive tool for imaging internal organs and tissues. One of the main themes of this dissertation involves the first reported dual-intrauterine photoacoustic and ultrasound deep-tissue imaging endoscope. This device was designed to enable physicians at the point-of-care to better elucidate overall gynecological health, by imaging the lining of the human uterus. Intrauterine photoacoustic endoscopy is made possible due to the small diameter of the endoscope (3mm), which allows for complete, 360-degree organ analysis from within the uterine cavity. In certain biomedical applications, however, further minimization is necessary. Sufficiently small diameter endoscopes may allow for the possibility of applying PAE in new areas. To further miniaturize the diameter of our endoscopes, alternative imaging probe designs were investigated. The proposed PAE architecture utilizes a hollow optical waveguide to allow for concentric guiding of both light and sound. This enables imaging depths of up to several millimeters into animal tissue while maintaining an outer diameter of roughly 1mm. In the final focus of this dissertation, these waveguides are further investigated for use in micropipette electrodes, common in the field of single cell electrophysiology. Pulsed light is coupled with these electrodes providing real-time photoacoustic feedback, useful in navigation towards intended targets. Lastly, fluorescence can be generated and collected at the micropipette aperture by utilizing an intra-electrode tapered optical fiber. This allows for a targeted robotic approach to labeled neurons that is independent of microscopy.
ContributorsMiranda, Christopher (Author) / Smith, Barbara S. (Thesis advisor) / Kodibagkar, Vikram (Committee member) / LaBaer, Joshua (Committee member) / Frakes, David (Committee member) / Barkley, Joel (Committee member) / Arizona State University (Publisher)
Created2021
Description
Elucidation of Antigen-Antibody (Ag-Ab) interactions is critical to the understanding of humoral immune responses to pathogenic infection. B cells are crucial components of the immune system that generate highly specific antibodies, such as IgG, towards epitopes on antigens. Serum IgG molecules carry specific molecular recognition information concerning the antigens that initiated their production. If one could read it, this information can be used to predict B cell epitopes on target antigens in order to design effective epitope driven vaccines, therapies and serological assays. Immunosignature technology captures the specific information content of serum IgG from infected and uninfected individuals on high density microarrays containing ~105 nearly random peptide sequences. Although the sequences of the peptides are chosen to evenly cover amino acid sequence space, the pattern of serum IgG binding to the array contains a consistent signature associated with each specific disease (e.g., Valley fever, influenza) among many individuals. Here, the disease specific but agnostic behavior of the technology has been explored by profiling molecular recognition information for five pathogens causing life threatening infectious diseases (e.g. DENV, WNV, HCV, HBV, and T.cruzi). This was done by models developed using a machine learning algorithm to model the sequence dependence of the humoral immune responses as measured by the peptide arrays. It was shown that the disease specific binding information could be accurately related to the peptide sequences used on the array by the machine learning (ML) models. Importantly, it was demonstrated that the ML models could identify or predict known linear epitopes on antigens of the four viruses. Moreover, the models identified potential novel linear epitopes on antigens of the four viruses (each has 4-10 proteins in the proteome) and of T.cruzi (a eukaryotic parasite which has over 12,000 proteins in its proteome). Finally, the predicted epitopes were tested in serum IgG binding assays such as ELISAs. Unfortunately, the assay results were inconsistent due to problems with peptide/surface interactions. In a separate study for the development of antibody recruiting molecules (ARMs) to combat microbial infections, 10 peptides from the high density peptide arrays were tested in IgG binding assays using sera of healthy individuals to find a set of antibody binding termini (ABT, a ligand that binds to a variable region of the IgG). It was concluded that one peptide (peptide 7) may be used as a potential ABT. Overall, these findings demonstrate the applications of the immunosignature technology ranging from developing tools to predict linear epitopes on pathogens of small to large proteomes to the identification of an ABT for ARMs.
ContributorsCHOWDHURY, ROBAYET (Author) / Woodbury, Neal (Thesis advisor) / LaBaer, Joshua (Committee member) / Sulc, Petr (Committee member) / Arizona State University (Publisher)
Created2020
Description
Next-generation sequencing is a powerful tool for detecting genetic variation. How-ever, it is also error-prone, with error rates that are much larger than mutation rates.
This can make mutation detection difficult; and while increasing sequencing depth
can often help, sequence-specific errors and other non-random biases cannot be de-
tected by increased depth. The problem of accurate genotyping is exacerbated when
there is not a reference genome or other auxiliary information available.
I explore several methods for sensitively detecting mutations in non-model or-
ganisms using an example Eucalyptus melliodora individual. I use the structure of
the tree to find bounds on its somatic mutation rate and evaluate several algorithms
for variant calling. I find that conventional methods are suitable if the genome of a
close relative can be adapted to the study organism. However, with structured data,
a likelihood framework that is aware of this structure is more accurate. I use the
techniques developed here to evaluate a reference-free variant calling algorithm.
I also use this data to evaluate a k-mer based base quality score recalibrator
(KBBQ), a tool I developed to recalibrate base quality scores attached to sequencing
data. Base quality scores can help detect errors in sequencing reads, but are often
inaccurate. The most popular method for correcting this issue requires a known
set of variant sites, which is unavailable in most cases. I simulate data and show
that errors in this set of variant sites can cause calibration errors. I then show that
KBBQ accurately recalibrates base quality scores while requiring no reference or other
information and performs as well as other methods.
Finally, I use the Eucalyptus data to investigate the impact of quality score calibra-
tion on the quality of output variant calls and show that improved base quality score
calibration increases the sensitivity and reduces the false positive rate of a variant
calling algorithm.
This can make mutation detection difficult; and while increasing sequencing depth
can often help, sequence-specific errors and other non-random biases cannot be de-
tected by increased depth. The problem of accurate genotyping is exacerbated when
there is not a reference genome or other auxiliary information available.
I explore several methods for sensitively detecting mutations in non-model or-
ganisms using an example Eucalyptus melliodora individual. I use the structure of
the tree to find bounds on its somatic mutation rate and evaluate several algorithms
for variant calling. I find that conventional methods are suitable if the genome of a
close relative can be adapted to the study organism. However, with structured data,
a likelihood framework that is aware of this structure is more accurate. I use the
techniques developed here to evaluate a reference-free variant calling algorithm.
I also use this data to evaluate a k-mer based base quality score recalibrator
(KBBQ), a tool I developed to recalibrate base quality scores attached to sequencing
data. Base quality scores can help detect errors in sequencing reads, but are often
inaccurate. The most popular method for correcting this issue requires a known
set of variant sites, which is unavailable in most cases. I simulate data and show
that errors in this set of variant sites can cause calibration errors. I then show that
KBBQ accurately recalibrates base quality scores while requiring no reference or other
information and performs as well as other methods.
Finally, I use the Eucalyptus data to investigate the impact of quality score calibra-
tion on the quality of output variant calls and show that improved base quality score
calibration increases the sensitivity and reduces the false positive rate of a variant
calling algorithm.
ContributorsOrr, Adam James (Author) / Cartwright, Reed (Thesis advisor) / Wilson, Melissa (Committee member) / Kusumi, Kenro (Committee member) / Taylor, Jesse (Committee member) / Pfeifer, Susanne (Committee member) / Arizona State University (Publisher)
Created2020
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
This dissertation presents three novel algorithms with real-world applications to genomic oncology. While the methodologies presented here were all developed to overcome various challenges associated with the adoption of high throughput genomic data in clinical oncology, they can be used in other domains as well. First, a network informed feature ranking algorithm is presented, which shows a significant increase in ability to select true predictive features from simulated data sets when compared to other state of the art graphical feature ranking methods. The methodology also shows an increased ability to predict pathological complete response to preoperative chemotherapy from genomic sequencing data of breast cancer patients utilizing domain knowledge from protein-protein interaction networks.
Second, an algorithm that overcomes population biases inherent in the use of a human reference genome developed primarily from European populations is presented to classify microsatellite instability (MSI) status from next-generation-sequencing (NGS) data. The methodology significantly increases the accuracy of MSI status prediction in African and African American ancestries.
Finally, a single variable model is presented to capture the bimodality inherent in genomic data stemming from heterogeneous diseases. This model shows improvements over other parametric models in the measurements of receiver-operator characteristic (ROC) curves for bimodal data. The model is used to estimate ROC curves for heterogeneous biomarkers in a dataset containing breast cancer and cancer-free specimen.
ContributorsSaul, Michelle (Author) / Dinu, Valentin (Thesis advisor) / Liu, Li (Committee member) / Wang, Junwen (Committee member) / Arizona State University (Publisher)
Created2021