Recent Submissions

  • Genomic Predictions in Uterine Cancers

    Tran, Lynn Kim Hoang; Center for Biotechnology and Genomic Medicine (Augusta University, 2020-05)
    Introduction: Current uterine cancer classification provides suboptimal treatment stratification and often groups together patients with significant differences in survival outcome and/or response. We used transcriptomic information to devise genomic scores for improved prediction of uterine cancer patient outcomes and validated these scores in our institutional cohorts. Project 1: In an early iteration of our gene signature discovery pipeline, we developed USC73, a genomic score for uterine serous carcinoma patients, which grouped patients into a low score (lower 66.7 percentile), good prognosis group and a high score (upper 33.3 percentile), poor prognosis group (5-year overall survival: 83.3% and 13.3%, respectively). USC73 predicts survival independently of stage, and can be combined with stage for further resolution of patient survival. Poor survivors have faster-growing tumors and lower rates of complete response to primary therapy. Project 2: We applied our pipeline to uterine endometrioid carcinoma, the most common histotype of uterine cancer, and developed UEC_IGS, an immune gene score that separates early stage patients into a high lymphocytic infiltration, good prognosis group (IGS 1) and a low lymphocytic infiltration, poor prognosis group (IGS 2). UEC_IGS predicts overall survival independent of grade and treatment. IGS 1 patients have higher levels of CD8+ tumor infiltrating lymphocytes (TILs), more CD45RO+/CD3+ memory T cells, and lower levels of FOXP3+ Tregs compared to IGS 2. Conclusion: Using transcriptomic data, we can reliably stratify uterine cancer patients into good and poor survival groups. This information can be used to facilitate recruitment of only poor prognosis patients into clinical trials, mitigating some heterogeneity in patient response and allowing clinicians to better identify treatments for patients who will not survive on the current therapy. Additionally, biological functions (e.g. cellular proliferation or immune infiltration) are associated with each genomic score, and these can serve as potential pathways to target for improving the outcome of poor survival groups.

    Tran, Paul; Center for Biotechnology and Genomic Medicine (Augusta University, 2020-04)
    Introduction: A major aim of modern medicine is to translate basic genomics findings using machine learning and other data analysis methods into clinical tests for improving patient care. Herein, I applied machine learning methods to publicly available genetic and genomic data to address three clinical problems in cancer and type 1 diabetes (T1D) research. Project 1: Cancer classification mostly depends on the anatomic pathology workforce; hence, diagnosis is slow, stepwise, and prone to errors and systemic bias. Using a transcriptome-based cancer classification method, I reconciled the 18% disagreement rate between histology and mutation-based classifier for brain cancer. Project 2: I applied the same transcriptome-based classification method to lung adenocarcinoma and identified 3 novel subgroups comprising ~30% of lung adenocarcinoma. Project 3: The estimated genetic heritability of T1D is up to 80%. Identifying those most genetically susceptible to T1D can lead to reduction of the number of islet autoimmunity cases and the number diabetic ketoacidosis episodes. I developed a genetic risk prediction model using neural networks which performs better than currently published methods. I applied model interpretation methods to the neural network and identified important genetic drivers for characterizing T1D molecular subgroups. Conclusion: These projects are small steps in translating genomic medicine projects to clinical applications but represent a future with more objective and automated tools to aid in clinical decision making.
  • A novel subnetwork based analysis reveals shared pathways in T-cell mediated autoimmunity

    Pabla, Simarjot Singh; Center for Biotechnology and Genomic Medicine (2016-03)
    Thymocyte auto-reactivity is an underlying theme of several autoimmune disorders. The precise role of auto-reactive T cells in the initiation and subsequent progression of autoimmune disorders has been studied extensively. However, these disease specific studies ignore pathways that may be in common to several T cell mediated autoimmune pathologies. This can be attributed in part to the shortcomings of traditional gene list based gene expression studies. Here we report a novel method to identify unifying gene expression changes in several autoimmune diseases. In order to uncover pathologically important pathways common to T-cell mediated autoimmune disorders, we used human gene expression data from Multiple Sclerosis, Rheumatoid Arthritis, Juvenile Idiopathic Arthritis and Sjögren’s syndrome. Unlike traditional gene expression analysis, we used jointly active connected subnetwork enrichment to identify subnetworks for each disorder, followed by topological network alignment, which led to identification of shared pathways. We report four pathways shared in these disorders, which include DNA damage response, gonadotropin, innate and adaptive immunity pathways. Importantly, our method did not reveal any common pathways in unrelated diseases. In order to experimentally validate our findings, RNA sequencing of mRNA isolated from salivary glands excised from a murine model of Sjögren’s syndrome was performed. High similarities were observed between Human T-cell mediated autoimmune disorders and Sjögren’s murine model. Collectively, these studies have identified a shared landscape of pathologically significant pathways, including DNA damage response, gonadotropin, innate and adaptive immunity in autoimmune disorders and provide a new methodology to identify common alterations in diseases with similar underlying etiologies.
  • The Autoimmune Regulator (Aire) Confers Immunosuppressive Properties to Dendritic Cells

    Eisenman, Daniel; Center for Biotechnology and Genomic Medicine (2007-05)
    The Autoimmune regulator (Aire) is a transcription factor that controls expression of self antigens by thymic epithelium and it plays a critical role in the deletion of autoreactive thymocytes and prevention of autoimmunity. Recent studies have reported Aire expression in dendritic cells (DC) located in spleen and lymph nodes, suggesting a role for Aire in extra-thymic tolerance induction. Molecular and functional studies conducted in this dissertation revealed that Aire induction in bone marrow derived DC results in expression of immunosuppressive cytokines and decreased expression of co-stimulatory molecules. Similar results were also obtained from lenti-virus-mediated Aire overexpression in the DC2.4 dendritic cell line. It was further shown that DC from Aire'7' mice exhibited greater antigen presenting function both in vitro and in vivo. These DC were more potent stimulators of T cell proliferation leading to increased IL-2 and IFNy production. These studies suggest that Aire7' DC may play a role in exacerbating the autoimmunity seen in Aire7' mice. DC over-expressing Aire were shown to suppress activation and proliferation of naive T cells and promote activation-induced cell death of activated T cells. Furthermore, we demonstrate that Aire also controls transcription of tissue-specific antigens in DC. These results, together, suggest that Aire plays an important role in the tolerogenic function of DC.
  • Large Scale Gene Expression Analysis Reveals Insight into Pathways Related to Type 1 Diabetes and Associated Complications

    Carey, Colleen M.; Center for Biotechnology and Genomic Medicine (2013-08)
    Type 1 Diabetes (T1D) is a chronic inflammatory disease resulting from complex interactions between susceptibility genes, the environment, and the immune system, ultimately leading to the destruction o f pancreatic islet cells and insulin deficiency. Previous studies have examined the series o f molecular, cellular, and protein changes occurring within subsets of individuals and how these are associated with particular disease states. Genome wide association studies have revealed a large number o f genetic susceptibility intervals including those implicated in disease pathogenesis, the identification o f various markers for risk assessment, the classification o f disease or complications, and finally markers for monitoring therapies for disease. However, none of these studies to date is without seriously limitations. First, although microarray based gene expression profiling is a powerful tool in discovery; results must be validated by alternate techniques. Second, due to the inherent heterogeneity of the human population large sample sizes in each group must be used in order to handle the expected large expression variations among individual subject. Third, for accurate normalization of Real-Time PCR expression data appropriate reference genes must be selected. We proposed a large scale gene expression validation study to address the limitations of previous studies. Validation studies were performed using high throughput Real-Time RT-PCR on peripheral blood mononuclear cells (PBMCs) o f 928 individuals with T1D and 922 individuals as antibody negative (AbN) controls, recruited through the Prospective Assessment in Newborns of Diabetes Autoimmunity (PANDA) study. This dissertation work validated the gene expression changes among 28 genes shown to have differential expression in T1D patients as compared to controls. These genes were selected based on their function, role in inflammatory or the immune response, and any previously documented reference to a role in T1D. Our aims were to 1) identify gene expression changes which may be occurring specifically in diabetic complications, and 2) identify gene expression changes which may result in an increased state o f oxidative stress in the diabetic state. For validation studies, we divided the 28 genes into two subsets based on related function to ask whether any gene expression signatures could be associated with diabetes, diabetic complications, or oxidative stress in the diabetic state. Our studies revealed genes that are involved in inflammation, immune regulation, and antigen processing and presentation are significantly altered in the PBMCs o f T1D patients. Eight genes (S100A8, S100A9, MNDA, SELL, TGFB1, PSMB3, CD74, and IL12A) were shown to have higher expression, with three genes (GNLY, PSMA4, and SMAD7) having lower expression, in T1D when compared to controls. The data also suggested that inflammatory mediators secreted mainly by myeloid cells are implicated in T1D and its complications (Odds ratios OR = 1.3-2.6, adjusted P value= 0.005- 1.08 x 10 8), and particularly in those patients with nephropathy (OR=4.8-7.9, adjusted P value < 0.005). Validation studies also revealed nine genes (LAT2, MAPK1, APOBEC3B, SOD2, NDUFB3, STK40, PRKD2, ITGB2, and COX7B) with higher expression in T1D. These genes are involved in general pathways of inflammation and immune response; however SOD2, NDUFB3, and COX7B (OR=l.l-1.27, adjusted P value= 0.007-0.47) are functionally involved in the mechanisms o f the mitochondria and may play a role in the increased state of oxidative stress seen in T1D. In these studies we have validated and confirmed the gene expression differences between T1D and control subjects initially suggested by microarray. Our experimental design has addressed each of the limitations posed by earlier studies in the largest scale study to date on gene expression profiles in human T1D. We have demonstrated that gene expression is significantly different between autoantibody negative (AbN) controls and T1D patients without any complications. Genes implicated in immune function (S100A8, S100A9, MNDA, IL12A), immune regulation and promotion (TGFB1, SELL), antigen processing and presentation (CD74, PSMB3), and mitochondrial function (SOD2, NDUFB3, COX7B) have higher expression in T1D and support the notion that chronic inflammation and cellular oxidative stress contribute to the development of T1D and associated complications. The understanding gained from our results implies a translational potential for the use o f gene expression profiles in the classification o f at risk individuals for both T1D and complication. Further, our understanding into the role that the immune system plays in cellular oxidative stress leading to the diabetic state may serve to provide prevention therapies however there remains much to be learned before this is attainable.