Bioinformatics Commons^™

Genetic Contributors Of Incident Stroke In 10,700 African Americans With Hypertension: A Meta-Analysis From The Genetics Of Hypertension Associated Treatments And Reasons For Geographic And Racial Differences In Stroke Studies, Nicole D. Armstrong, Vinodh Srinivasasainagendra, Amit Patki, Rikki M. Tanner, Bertha A. Hidalgo, Hemant K. Tiwari, Nita A. Limdi, Ethan M. Lange, Leslie A. Lange, Donna K. Arnett, Marguerite R. Irvin

Epidemiology and Environmental Health Faculty Publications

Background: African Americans (AAs) suffer a higher stroke burden due to hypertension. Identifying genetic contributors to stroke among AAs with hypertension is critical to understanding the genetic basis of the disease, as well as detecting at-risk individuals.

Methods: In a population comprising over 10,700 AAs treated for hypertension from the Genetics of Hypertension Associated Treatments (GenHAT) and Reasons for Geographic and Racial Differences in Stroke (REGARDS) studies, we performed an inverse variance-weighted meta-analysis of incident stroke. Additionally, we tested the predictive accuracy of a polygenic risk score (PRS) derived from a European ancestral population in both GenHAT and REGARDS AAs …

Characterizing Long Covid: Deep Phenotype Of A Complex Condition, Rachel R. Deer, Madeline A. Rock, Nicole Vasilevsky, Leigh Carmody, Halie Rando, Alfred J. Anzalone, Marc D. Basson, Tellen D. Bennett, Timothy Bergquist, Eilis A. Boudreau, Carolyn T. Bramante, James Brian Byrd, Tiffany J. Callahan, Lauren E. Chan, Haitao Chu, Christopher G. Chute, Ben D. Coleman, Hannah E. Davis, Joel Gagnier, Casey S. Greene, Ramakanth Kavuluru

Institute for Biomedical Informatics Faculty Publications

BACKGROUND: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies.

METHODS: The Human Phenotype Ontology …

Verrucous Carcinoma Of The Vulva: Patterns Of Care And Treatment Outcomes., Sara M. Dryden, Leonid B. Reshko, Jeremy T. Gaskins, Scott R. Silva

Faculty Scholarship

Background: Verrucous vulvar carcinoma (VC) is an uncommon and distinct histologic subtype of squamous cell carcinoma (SCC). The available literature on VC is currently limited to case reports and small single institution studies. Aims: The goals of this study were to analyze data from the National Cancer Database (NCDB) to quantitate the incidence of VC and to investigate the effects of patient demographics, tumor characteristics, and treatment regimens on overall survival (OS) in women with verrucous vulvar carcinoma. Methods and results: Patients diagnosed with vulvar SCC or VC between the years of 2004 and 2016 were identified in the NCDB. …

Untargeted Lipidomics Of Non-Small Cell Lung Carcinoma Demonstrates Differentially Abundant Lipid Classes In Cancer Vs. Non-Cancer Tissue, Joshua M. Mitchell, Robert M. Flight, Hunter N. B. Moseley

Molecular and Cellular Biochemistry Faculty Publications

Lung cancer remains the leading cause of cancer death worldwide and non-small cell lung carcinoma (NSCLC) represents 85% of newly diagnosed lung cancers. In this study, we utilized our untargeted assignment tool Small Molecule Isotope Resolved Formula Enumerator (SMIRFE) and ultra-high-resolution Fourier transform mass spectrometry to examine lipid profile differences between paired cancerous and non-cancerous lung tissue samples from 86 patients with suspected stage I or IIA primary NSCLC. Correlation and co-occurrence analysis revealed significant lipid profile differences between cancer and non-cancer samples. Further analysis of machine-learned lipid categories for the differentially abundant molecular formulas identified a high abundance sterol, …

Improved Radiation Expression Profiling In Blood By Sequential Application Of Sensitive And Specific Gene Signatures, Eliseos J. Mucaki, Ben C. Shirley, Peter K. Rogan

Biochemistry Publications

Purpose. Combinations of expressed genes can discriminate radiation-exposed from normal control blood samples by machine learning based signatures (with 8 to 20% misclassification rates). These signatures can quantify therapeutically-relevant as well as accidental radiation exposures. The prodromal symptoms of Acute Radiation Syndrome (ARS) overlap those present in Influenza and Dengue Fever infections. Surprisingly, these human radiation signatures misclassified gene expression profiles of virally infected samples as false positive exposures. The present study investigates these and other confounders, and then mitigates their impact on signature accuracy.

Methods. This study investigated recall by previous and novel radiation signatures independently derived …

Real-World Evaluation Of Universal Germline Screening For Cancer Treatment-Relevant Pharmacogenes, Megan L. Hutchcraft, Nan Lin, Shulin Zhang, Catherine Sears, Kyle Zacholski, Elizabeth A. Belcher, Eric B. Durbin, John L. Villano, Michael J. Cavnar, Susanne M. Arnold, Frederick R. Ueland, Jill M. Kolesar

Pathology and Laboratory Medicine Faculty Publications

The purpose of this study was to determine the frequency of clinically actionable treatment-relevant germline pharmacogenomic variants in patients with cancer and assess the real-world clinical utility of universal screening using whole-exome sequencing in this population. Cancer patients underwent research-grade germline whole-exome sequencing as a component of sequencing for somatic variants. Analysis in a clinical bioinformatics pipeline identified clinically actionable pharmacogenomic variants. Clinical Pharmacogenetics Implementation Consortium guidelines defined clinical actionability. We assessed clinical utility by reviewing electronic health records to determine the frequency of patients receiving pharmacogenomically actionable anti-cancer agents and associated outcomes. This observational study evaluated 291 patients with …

Random Forest-Integrated Analysis In Ad And Late Brain Transcriptome-Wide Data To Identify Disease-Specific Gene Expression, Xinxing Wu, Chong Peng, Peter T. Nelson, Qiang Cheng

Sanders-Brown Center on Aging Faculty Publications

Alzheimer's disease (AD) is a complex neurodegenerative disorder that affects thinking, memory, and behavior. Limbic-predominant age-related TDP-43 encephalopathy (LATE) is a recently identified common neurodegenerative disease that mimics the clinical symptoms of AD. The development of drugs to prevent or treat these neurodegenerative diseases has been slow, partly because the genes associated with these diseases are incompletely understood. A notable hindrance from data analysis perspective is that, usually, the clinical samples for patients and controls are highly imbalanced, thus rendering it challenging to apply most existing machine learning algorithms to directly analyze such datasets. Meeting this data analysis challenge is …

Bone Quality And Fractures In Women With Osteoporosis Treated With Bisphosphonates For 1 To 14 Years, Hartmut H. Malluche, Jin Chen, Florence Lima, Lucas J. Liu, Marie-Claude Monier-Faugere, David A. Pienkowski

Internal Medicine Faculty Publications

Oral bisphosphonates are the primary medication for osteoporosis, but concerns exist regarding potential bone-quality changes or low-energy fractures. This cross-sectional study used artificial intelligence methods to analyze relationships among bisphosphonate treatment duration, a wide variety of bone-quality parameters, and low-energy fractures. Fourier transform infrared spectroscopy and histomorphometry quantified bone-quality parameters in 67 osteoporotic women treated with oral bisphosphonates for 1 to 14 years. Artificial intelligence methods established two models relating bisphosphonate treatment duration to bone-quality changes and to low-energy clinical fractures. The model relating bisphosphonate treatment duration to bone quality demonstrated optimal performance when treatment durations of 1 to 8 …

A Network-Based Approach For Computational Drug Repurposing On Cancer Data, Ann Reba Thomas Alexander

Electronic Theses and Dissertations

In this thesis, we are interested in finding the best drugs that can be repurposed for the disease and able to find the adverse effects such drugs that are FDA-Approved. Developing an effective drug can be a time-consuming and expensive crucible method. Network-based machine learning methods are used for predicting a given drug for A that can be used for B. It aims at finding new indications for already existing drugs and therefore increases the available therapeutic choices at a fraction of the cost of new drug development. The perturbation gene expression data corresponding to the MCF7 cell line was …

Analysis Of High-Risk Pedigrees Identifies 12 Candidate Variants For Alzheimer's Disease, Craig C. Teerlink, Justin B. Miller, Elizabeth L. Vance, Lyndsay A. Staley, Jeffrey Stevens, Justina P. Tavana, Matthew E. Cloward, Madeline L. Page, Louisa Dayton, Alzheimer's Disease Genetics Consortium, Lisa A. Cannon-Albright, John S. K. Kauwe

Institute for Biomedical Informatics Faculty Publications

INTRODUCTION: Analysis of sequence data in high-risk pedigrees is a powerful approach to detect rare predisposition variants.

METHODS: Rare, shared candidate predisposition variants were identified from exome sequencing 19 Alzheimer's disease (AD)-affected cousin pairs selected from high-risk pedigrees. Variants were further prioritized by risk association in various external datasets. Candidate variants emerging from these analyses were tested for co-segregation to additional affected relatives of the original sequenced pedigree members.

RESULTS: AD-affected high-risk cousin pairs contained 564 shared rare variants. Eleven variants spanning 10 genes were prioritized in external datasets: rs201665195 (ABCA7), and rs28933981 (TTR) were previously …

Impact Of Intratumor Heterogeneity And The Tumor Microenvironment In Shaping Tumor Evolution And Response To Therapy, Akash Mitra

Dissertations & Theses (Open Access)

Intratumor heterogeneity (ITH) is a crucial challenge in cancer treatment. The genotypic and phenotypic heterogeneity underlying diverse cancer types leads to subclonal variation, which may result in mixed or failed response to therapy. The heterogeneity at the tumor level, along with the tumor microenvironment (TME), often shapes tumor evolution and ultimately clinical outcome. Given that modern treatment paradigms increasingly expose patients with metastatic disease to multiple treatment modalities through the course of their disease, there exists a need to characterize robust and predictive biomarkers of response to therapy. In order to accurately characterize tumor evolution, we need to account for …

Mucin And Splice Variant Profiles Of Pancreatic Adenocarcinoma Predict Patient Survival And Subtyping, Christopher M. Thompson

Theses & Dissertations

PDAC is a pancreatic epithelial malignancy and demonstrates aggressive progression and bleak patient prognosis. Despite decades of research, the evolution of novel diagnostics and intervention modalities for PDAC is stagnant. This dissertation explores the characteristic aberrant and elevated expression of mucins in PDAC. Beginning with the hypothesis that mucins are associated with disease aggressiveness, analysis of PDAC patient survival in TCGA revealed no associations between single mucin expression and patient survival. This led to the underlying issue of PDAC tumor cellularity since this disease demonstrates variability in the proportion of cancer cells within the tumor. Tumor purity assessed with the …

Reach, Effectiveness, Adoption, And Maintenance Of Mobile Electronic Clinical Decision Support Tools Deployed As Part Of National Quality Improvement Projects, Ellen K. Kerns

Theses & Dissertations

Electronic clinical decision support (ECDS) tools are often developed within quality improvement (QI) projects to increase adherence with the latest clinical practice guidelines. However, the potential reach and maintenance of ECDS use beyond the time and location of their associated project are very limited. Deploying ECDS using a mobile app (mECDS) has shown the potential to be a viable method of overcoming these limitations. However, it is unclear what pattern the reach and adoption of such a tool might follow and what effect this use has on clinical practice. Our team developed an app which contained two different mECDS tools …

Biases And Blind-Spots In Genome-Wide Crispr-Cas9 Knockout Screens, Merve Dede

Dissertations & Theses (Open Access)

Adaptation of the bacterial CRISPR-Cas9 system to mammalian cells revolutionized the field of functional genomics, enabling genome-scale genetic perturbations to study essential genes, whose loss of function results in a severe fitness defect. There are two types of essential genes in a cell. Core essential genes are absolutely required for growth and proliferation in every cell type. On the other hand, context-dependent essential genes become essential in an environmental or genetic context. The concept of context-dependent gene essentiality is particularly important in cancer, since killing cancer cells selectively without harming surrounding healthy tissue remains a major challenge. The toxicity of …

Keap1 Is Required For Artesunate Anticancer Activity In Non-Small-Cell Lung Cancer, Kristen S. Hill, Anthony Mcdowell Jr., J. Robert Mccorkle, Erin Schuler, Sally R. Ellingson, Rina Plattner, Jill M. Kolesar

Pathology and Laboratory Medicine Faculty Publications

Artesunate is the most common treatment for malaria throughout the world. Artesunate has anticancer activity likely through the induction of reactive oxygen species, the same mechanism of action utilized in Plasmodium falciparum infections. Components of the kelch-like ECH-associated protein 1 (KEAP1)/nuclear factor erythroid 2-related factor 2 (NRF2) pathway, which regulates cellular response to oxidative stress, are mutated in approximately 30% of non-small-cell lung cancers (NSCLC); therefore, we tested the hypothesis that KEAP1 is required for artesunate sensitivity in NSCLC. Dose response assays identified A549 cells, which have a G333C-inactivating mutation in KEAP1, as resistant to artesunate, with an IC50 of …

The Mwtab Python Library For Restful Access And Enhanced Quality Control, Deposition, And Curation Of The Metabolomics Workbench Data Repository, Christian D. Powell, Hunter N. B. Moseley

Markey Cancer Center Faculty Publications

The Metabolomics Workbench (MW) is a public scientific data repository consisting of experimental data and metadata from metabolomics studies collected with mass spectroscopy (MS) and nuclear magnetic resonance (NMR) analyses. MW has been constantly evolving; updating its ‘mwTab’ text file format, adding a JavaScript Object Notation (JSON) file format, implementing a REpresentational State Transfer (REST) interface, and nearly quadrupling the number of datasets hosted on the repository within the last three years. In order to keep up with the quickly evolving state of the MW repository, the ‘mwtab’ Python library and package have been continuously updated to mirror the changes …

Deep Active Learning For Classifying Cancer Pathology Reports, Kevin De Angeli, Shang Gao, Mohammed Alawad, Hong‑Jun Yoon, Noah Schaeferkoetter, Xiao‑Cheng Wu, Eric B. Durbin, Jennifer Doherty, Antoinette Stroup, Linda Coyle, Lynne Penberthy, Georgia Tourassi

Kentucky Cancer Registry Faculty Publications

Background: Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model.

Results: We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. …

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 …

Machine Intelligence Identifies Soluble Tnfa As A Therapeutic Target For Spinal Cord Injury, J. R. Huie, A. R. Ferguson, N. Kyritsis, J. Z. Pan, K.-A. Irvine, J. L. Nielson, P. G. Schupp, M. C. Oldham, John C. Gensel, A. Lin, M. R. Segal, R. R. Ratan, J. C. Bresnahan, M. S. Beattie

Spinal Cord and Brain Injury Research Center Faculty Publications

Traumatic spinal cord injury (SCI) produces a complex syndrome that is expressed across multiple endpoints ranging from molecular and cellular changes to functional behavioral deficits. Effective therapeutic strategies for CNS injury are therefore likely to manifest multi-factorial effects across a broad range of biological and functional outcome measures. Thus, multivariate analytic approaches are needed to capture the linkage between biological and neurobehavioral outcomes. Injury-induced neuroinflammation (NI) presents a particularly challenging therapeutic target, since NI is involved in both degeneration and repair. Here, we used big-data integration and large-scale analytics to examine a large dataset of preclinical efficacy tests combining five …