Bioinformatics Commons^™

The Use Of Prognostic Markers To Predict Disease Progression And Clinical Outcome In Monoclonal Gammopathy Of Undetermined Significance, Smouldering Multiple Myeloma And Multiple Myeloma., Róisín C. Mcmonagle

International Undergraduate Journal of Health Sciences

Multiple Myeloma (MM) is an incurable plasma cell malignancy with a complex and incompletely understood molecular pathogenesis. Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smouldering Multiple Myeloma (SMM) precede MM, with variable risks and rates of disease progression. The continuing high relapse and death rate in MM cases has prompted research into more accurate prognostic markers to predict progression from MGUS and SMM to MM, as well as identify MM cases with aggressive disease, in order to begin early, targeted and effective therapeutic intervention. Many studies have focused on utilising current markers more effectively, including M-protein, serum-free light chain ratio, …

Real World Clinicopathologic Observations Of Patients With Metastatic Solid Tumors Receiving Immune Checkpoint Inhibitor Therapy: Analysis From Kentucky Cancer Registry, Aasems Jacob, Jianrong Wu, Jill M. Kolesar, Eric B. Durbin, Aju Mathew, Susanne Arnold, Aman Chauhan

Biostatistics Faculty Publications

The state of Kentucky has the highest cancer incidence and mortality in the United States. High‐risk populations such as this are often underrepresented in clinical trials. The study aims to do a comprehensive analysis of molecular landscape of metastatic cancers among these patients with detailed evaluation of factors affecting response and outcomes to immune checkpoint inhibitor (ICI) therapy. We performed a retrospective analysis of metastatic solid tumor patients who received ICI and underwent molecular profiling at our institution.

Sixty nine patients with metastatic solid tumors who received ICI were included in the study. Prevalence of smoking and secondhand tobacco exposure …

Identification Of Prognostic Cancer Biomarkers Through The Application Of Rna-Seq Technologies And Bioinformatics, Nathan Wong

McKelvey School of Engineering Theses & Dissertations

MicroRNAs (miRNAs) are short single-stranded RNAs that function as the guide sequence of the post-transcriptional regulatory process known as the RNA-induced silencing complex (RISC), which targets mRNA sequences for degradation through complementary binding to the guide miRNA. Changes in miRNA expression have been reported as correlated with numerous biological processes, including embryonic development, cellular differentiation, and disease manifestation. In the latter case, dysregulation has been observed in response to infection by human papillomavirus (HPV), which has also been established as both oncogenic in cervical cancers and oropharyngeal cancers and favorable for overall patient survival after tumor formation. The identification of …

Spectral Gene Set Enrichment (Sgse), H Robert Frost, Zhigang Li, Jason H. Moore

Dartmouth Scholarship

Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes …

Dysregulation Of Micrornas In Blood As Biomarkers For Diagnosing Prostate Cancer, Rhonda W. Daniel

Theses and Dissertations

Prostate cancer is the most common noncutaneous cancer among men, yet current diagnostic methods are insufficient and more reliable diagnostic markers need to be developed. The answer that can bridge this gap and enable more efficient diagnoses may lie in microRNAs. These small, single stranded RNA molecules impact protein expression at the translational level and regulate important cellular pathways. Dysregulation of these small RNA molecules can have tumorigenic effects on cells and lead to many types of cancers.

Currently the Prostate-Stimulating Antigen (PSA) is used as a diagnostic marker for prostate cancer. However, many factors can elevate PSA levels such …

Statistical Methods For Proteomic Biomarker Discovery Based On Feature Extraction Or Functional Modeling Approaches, Jeffrey S. Morris

Jeffrey S. Morris

In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational …

Integrative Bayesian Analysis Of High-Dimensional Multi-Platform Genomics Data, Wenting Wang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris, Bradley M. Broom, Ganiraju C. Manyam, Kim-Anh Do

Jeffrey S. Morris

Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current integration approaches that treat the data are limited in that they do not consider the fundamental biological relationships that exist among the data from platforms.

Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses a hierarchical modeling technique to combine the data obtained from multiple platforms …