Life Sciences Commons^™

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 …

Incorporating Pathway Information Into Feature Selection Towards Better Performed Gene Signatures, Suyan Tian, Chi Wang, Bing Wang

Biostatistics Faculty Publications

To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, …

Dichotomous Scoring Of Tdp-43 Proteinopathy From Specific Brain Regions In 27 Academic Research Centers: Associations With Alzheimer's Disease And Cerebrovascular Disease Pathologies, Yuriko Katsumata, David W. Fardo, Walter A. Kukull, Peter T. Nelson

Biostatistics Faculty Publications

TAR-DNA binding protein 43 (TDP-43) proteinopathy is a common brain pathology in elderly persons, but much remains to be learned about this high-morbidity condition. Published stage-based systems for operationalizing disease severity rely on the involvement (presence/absence) of pathology in specific anatomic regions. To examine the comorbidities associated with TDP-43 pathology in aged individuals, we studied data from the National Alzheimer’s Coordinating Center (NACC) Neuropathology Data Set. Data were analyzed from 929 included subjects with available TDP-43 pathology information, sourced from 27 different American Alzheimer’s Disease Centers (ADCs). Cases with relatively unusual diseases including autopsy-proven frontotemporal lobar degeneration (FTLD-TDP or FTLD-tau) …

Gaw20: Methods And Strategies For The New Frontiers Of Epigenetics And Pharmacogenomics, Nathan L. Tintle, David W. Fardo, Marzia De Andrade, Stella Aslibekyan, Julia N. Bailey, Justo Lorenzo Bermejo, Rita M. Cantor, Saurabh Ghosh, Philip Melton, Xuexua Wang, Jean W. Maccluer, Laura Almasy

Biostatistics Faculty Publications

GAW20 provided a platform for developing and evaluating statistical methods to analyze human lipid-related phenotypes, DNA methylation, and single-nucleotide markers in a study involving a pharmaceutical intervention. In this article, we present an overview of the data sets and the contributions analyzing these data. The data, donated by the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) investigators, included data from 188 families (N = 1105) which included genome-wide DNA methylation data before and after a 3-week treatment with fenofibrate, single-nucleotide polymorphisms, metabolic syndrome components before and after treatment, and a variety of covariates. The contributions from individual …

Treatment And Outcomes Of Non-Small-Cell Lung Cancer Patients With High Comorbidity, Jorge Rios, Rahul Gosain, Bernardo H. L. Goulart, Bin Huang, Margaret N. Oechsli, Jaclyn K. Mcdowell, Quan Chen, Thomas Tucker, Goetz H. Kloecker

Biostatistics Faculty Publications

Background: The life expectancy of untreated non-small-cell lung cancer (NSCLC) is dismal, while treatment for NSCLC improves survival. The presence of comorbidities is thought to play a significant role in the decision to treat or not treat a given patient. We aim to evaluate the association of comorbidities with the survival of patients treated for NSCLC.

Methods: We performed a retrospective study of patients aged ≥66 years with invasive NSCLC between the years 2007 and 2011 in the Surveillance, Epidemiology, and End Results Kentucky Cancer Registry. Comorbidity was measured using the Klabunde Comorbidity Index (KCI), and univariate and multivariate logistic …

Bayesian Prediction Intervals For Assessing P-Value Variability In Prospective Replication Studies, Olga A. Vsevolozhskaya, Gabriel Ruiz, Dmitri Zaykin

Biostatistics Faculty Publications

Increased availability of data and accessibility of computational tools in recent years have created an unprecedented upsurge of scientific studies driven by statistical analysis. Limitations inherent to statistics impose constraints on the reliability of conclusions drawn from data, so misuse of statistical methods is a growing concern. Hypothesis and significance testing, and the accompanying P-values are being scrutinized as representing the most widely applied and abused practices. One line of critique is that P-values are inherently unfit to fulfill their ostensible role as measures of credibility for scientific hypotheses. It has also been suggested that while P-values …

Systems Biology Approach To Late-Onset Alzheimer's Disease Genome-Wide Association Study Identifies Novel Candidate Genes Validated Using Brain Expression Data And Caenorhabditis Elegans Experiments, Shubhabrata Mukherjee, Joshua C. Russell, Daniel T. Carr, Jeremy D. Burgess, Mariet Allen, Daniel J. Serie, Kevin L. Boehme, John S. K. Kauwe, Adam C. Naj, David W. Fardo, Dennis W. Dickson, Thomas J. Montine, Nilufer Ertekin-Taner, Matt R. Kaeberlein, Paul K. Crane

Biostatistics Faculty Publications

Introduction—We sought to determine whether a systems biology approach may identify novel late-onset Alzheimer's disease (LOAD) loci.

Methods—We performed gene-wide association analyses and integrated results with human protein-protein interaction data using network analyses. We performed functional validation on novel genes using a transgenic Caenorhabditis elegans Aβ proteotoxicity model and evaluated novel genes using brain expression data from people with LOAD and other neurodegenerative conditions.

Results—We identified 13 novel candidate LOAD genes outside chromosome 19. Of those, RNA interference knockdowns of the C. elegans orthologs of UBC, NDUFS3, EGR1, and ATP5H were associated with Aβ …

Impact Of Home Visit Capacity On Genetic Association Studies Of Late-Onset Alzheimer's Disease, David W. Fardo, Laura E. Gibbons, Shubhabrata Mukherjee, M. Maria Glymour, Wayne Mccormick, Susan M. Mccurry, James D. Bowen, Eric B. Larson, Paul K. Crane

Biostatistics Faculty Publications

INTRODUCTION—Findings for genetic correlates of late-onset Alzheimer's disease (LOAD) in studies that rely solely on clinic visits may differ from those with capacity to follow participants unable to attend clinic visits.

METHODS—We evaluated previously identified LOAD-risk single nucleotide variants in the prospective Adult Changes in Thought study, comparing hazard ratios (HRs) estimated using the full data set of both in-home and clinic visits (n = 1697) to HRs estimated using only data that were obtained from clinic visits (n = 1308). Models were adjusted for age, sex, principal components to account for ancestry, and additional health indicators.

RESULTS …

Identification Of Prognostic Genes And Gene Sets For Early-Stage Non-Small Cell Lung Cancer Using Bi-Level Selection Methods, Suyan Tian, Chi Wang, Howard H. Chang, Jianguo Sun

Biostatistics Faculty Publications

In contrast to feature selection and gene set analysis, bi-level selection is a process of selecting not only important gene sets but also important genes within those gene sets. Depending on the order of selections, a bi-level selection method can be classified into three categories – forward selection, which first selects relevant gene sets followed by the selection of relevant individual genes; backward selection which takes the reversed order; and simultaneous selection, which performs the two tasks simultaneously usually with the aids of a penalized regression model. To test the existence of subtype-specific prognostic genes for non-small cell lung cancer …

Weighted-Samgsr: Combining Significance Analysis Of Microarray-Gene Set Reduction Algorithm With Pathway Topology-Based Weights To Select Relevant Genes, Suyan Tian, Howard H. Chang, Chi Wang

Biostatistics Faculty Publications

Background: It has been demonstrated that a pathway-based feature selection method that incorporates biological information within pathways during the process of feature selection usually outperforms a gene-based feature selection algorithm in terms of predictive accuracy and stability. Significance analysis of microarray-gene set reduction algorithm (SAMGSR), an extension to a gene set analysis method with further reduction of the selected pathways to their respective core subsets, can be regarded as a pathway-based feature selection method.

Methods: In SAMGSR, whether a gene is selected is mainly determined by its expression difference between the phenotypes, and partially by the number of pathways to …

Impact Of Noise On Molecular Network Inference, Radhakrishnan Nagarajan, Marco Scutari

Biostatistics Faculty Publications

Molecular entities work in concert as a system and mediate phenotypic outcomes and disease states. There has been recent interest in modelling the associations between molecular entities from their observed expression profiles as networks using a battery of algorithms. These networks have proven to be useful abstractions of the underlying pathways and signalling mechanisms. Noise is ubiquitous in molecular data and can have a pronounced effect on the inferred network. Noise can be an outcome of several factors including: inherent stochastic mechanisms at the molecular level, variation in the abundance of molecules, heterogeneity, sensitivity of the biological assay or measurement …