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School of Biological Sciences

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Publication Search Results

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    Differential Gene Co-Expression Network Characteristics Of Cancer
    (Georgia Institute of Technology, 2022-12-13) Arshad, Zainab
    The transformation from a healthy state to a disease state in cancer is dictated in large part by structural and regulatory abnormalities in genes. While the molecular features underlying this transition have been investigated for some time, allowing groundbreaking advancements in cancer research, a majority of these efforts are focused on mutational and expression changes of individual genes. The recent advancement of network-based analytic methods affords an additional route through which disease pathophysiology and biologic regulation can be investigated. Furthermore, with the development of high-throughput technologies and the availability of large biobanks, gene interaction changes, and their functional consequences can be reliably interpreted from a systemic perspective, in a context specific manner. Towards this end, my research investigates gene co-expression changes, derived from transcriptomic case-control data, that underlie cancer onset and progression relative to healthy tissue. For the first study, global network changes associated with cancers of nine different tissues of origin were investigated. Network complexity generally dropped in the transition from normal precursor tissues to corresponding primary tumors, whereas cross-tissue cancer network similarity overall increased in early-stage cancers followed by a subsequent loss in similarity as tumors reacquire cancer-specific network complexity in late-stage cancers. In addition, gene-gene connections remaining stable through cancer development were found enriched for ‘‘housekeeping’’ gene functions, whereas newly acquired interactions were associated with established cancer-promoting functions. For the second study, gene-network characteristics of the molecular subtypes (Luminal A, Luminal B and Basal) of Breast Cancer (BC) were outlined based on a comparative analysis relative to precursor normal breast tissue. Basal was identified as the most highly connected yet dissimilar subtype to normal control. We discovered eight extensively connected network modules acquired in Basal BCs that harbored 19 genes found significantly associated with survival and encoding cancer hallmark functions including regulation of cell proliferation and motility, as well as neural pathways that have not been previously associated with basal BCs. Finally, the consensus approach of network construction for an unbiased differential analysis of gene co-expression networks used in these studies was published as a step-by-step protocol. Altogether, this thesis highlights gene-network changes characteristic of individual cancer types, molecular subtypes and disease stages that informs their diverse progression patterns and clinical outcomes. Furthermore, it underscores the importance and demonstrates the utility of gene co-expression networks in identifying key genes, gene interactions and functional characteristics of cancers that maybe undiscovered by standard molecular analysis approaches.
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    Global dysregulation of gene expression and tumorigenesis: Data science for cancer
    (Georgia Institute of Technology, 2019-09-03) Clayton, Evan
    Dysregulation of gene expression is a hallmark of cancer. Broadly speaking, my research is focused on the changes in gene expression that characterize the transition from normal to cancerous states, i.e. tumorigenesis. To study such changes, I performed integrated analysis of next generation sequencing data for matched normal and primary tumor samples from hundreds of patients across numerous different cancer types. By analyzing this sequencing data, I have been able to explore the global landscape of transcriptional reprogramming in cancer and discover how changes in the regulation of gene expression may be implicated in tumorigenesis. My thesis is focused on four specific areas of transcriptional reprogramming in cancer: (1) changes in the expression and activity of transposable elements (TEs), (2) changes in alternative splicing induced by TEs, (3) allele-specific expression of tumor suppressor genes (TSGs), and (4) gene expression changes that are implicated in cancer drug response. TEs are known to be uniformly overexpressed in cancer, suggesting a possible role for their activity in tumorigenesis. I discovered a class of long interspersed nuclear elements (the LINE-1 family) with elevated levels of expression and activity in three different cancer types, and I showed examples where cancer-specific LINE-1 insertions disrupt enhancers, leading to the down-regulation of TSGs. TEs are also implicated in the creation of novel splicing isoforms, and aberrant alternative splicing has been associated with tumorigenesis for a number of different cancers. Integrated analysis of genome sequence and transcriptome data revealed thousands of TE-generated alternative splice events genome-wide, including close to 5,000 events distributed among cancer associated genes. I explored the functional implications of specific cases of isoform switching, whereby TE-induced isoforms of cancer associated genes show elevated levels of relative expression in tumor samples. A closer look at TSG expression in matched normal and tumor samples indicated that functionally important changes in patterns of allele-specific expression in individuals heterozygous for loss-of-function TSG alleles is a significant factor in cancer onset/progression. These results identified a variety of molecular mechanisms that contribute to the observed changes in allele-specific expression patterns in cancer with allele-specific alternative splicing mediated by anti-sense RNA emerging as a predominant factor. Furthermore, analysis of the genomic variation for world-wide human populations demonstrates that loss-of-function TSG alleles are segregating at remarkedly high frequencies implying that a significant fraction of otherwise healthy individuals may be pre-disposed to developing cancer. For the final study of my thesis research, I applied the gene expression data from primary tumor samples to build predictive models of cancer drug response for two common chemotherapeutics: 5-Fluorouracil and Gemcitabine. My gene expression based models predict whether patients will respond to individual therapies with up to 86% accuracy. The genes that I found to be most informative for predicting drug response were enriched in well-known cancer signaling pathways highlighting their potential significance in prognosis of chemotherapy.