Medicine and Health Sciences Commons^™

Top2a And Ezh2 Provide Early Detection Of An Aggressive Prostate Cancer Subgroup., David P. Labbé, Christopher J. Sweeney, Myles Brown, Phillip Galbo, Spencer Rosario, Kristine M. Wadosky, Sheng-Yu Ku, Martin Sjöström, Mohammed Alshalalfa, Nicholas Erho, Elai Davicioni, R. Jeffrey Karnes, Edward M. Schaeffer, Robert B. Jenkins, Robert B. Den, Ashley E. Ross, Michaela Bowden, Ying Huang, Kathryn P. Gray, Felix Y. Feng, Daniel E. Spratt, David W. Goodrich, Kevin H. Eng, Leigh Ellis

Department of Radiation Oncology Faculty Papers

Purpose: Current clinical parameters do not stratify indolent from aggressive prostate cancer. Aggressive prostate cancer, defined by the progression from localized disease to metastasis, is responsible for the majority of prostate cancer–associated mortality. Recent gene expression profiling has proven successful in predicting the outcome of prostate cancer patients; however, they have yet to provide targeted therapy approaches that could inhibit a patient's progression to metastatic disease. Experimental Design: We have interrogated a total of seven primary prostate cancer cohorts (n = 1,900), two metastatic castration-resistant prostate cancer datasets (n = 293), and one prospective cohort (n = 1,385) to assess …

Network-Based Stratification Analysis Of 13 Major Cancer Types Using Mutations In Panels Of Cancer Genes., Xue Zhong, Hushan Yang, Shuyang Zhao, Yu Shyr, Bingshan Li

Department of Medical Oncology Faculty Papers

BACKGROUND: Cancers are complex diseases with heterogeneous genetic causes and clinical outcomes. It is critical to classify patients into subtypes and associate the subtypes with clinical outcomes for better prognosis and treatment. Large-scale studies have comprehensively identified somatic mutations across multiple tumor types, providing rich datasets for classifying patients based on genomic mutations. One challenge associated with this task is that mutations are rarely shared across patients. Network-based stratification (NBS) approaches have been proposed to overcome this challenge and used to classify tumors based on exome-level mutations. In routine research and clinical applications, however, usually only a small panel of …

Genetic Heterogeneity Of Pseudoxanthoma Elasticum: The Chinese Signature Profile Of Abcc6 And Enpp1 Mutations., Liang Jin, Qiujie Jiang, Zhengsheng Wu, Changxia Shao, Yong Zhou, Luting Yang, Jouni Uitto, Gang Wang

Department of Dermatology and Cutaneous Biology Faculty Papers

Pseudoxanthoma elasticum (PXE), an autosomal recessive disorder characterized by ectopic mineralization, is caused by mutations in the ABCC6 gene. We examined clinically 29 Chinese PXE patients from unrelated families, so far the largest cohort of Asian PXE patients. In a subset of 22 patients, we sequenced ABCC6 and another candidate gene, ENPP1, and conducted pathogenicity analyses for each variant. We identified a total of 17 distinct mutations in ABCC6, 15 of them being, to our knowledge, previously unreported, including 5 frameshift and 10 missense variants. In addition, a missense mutation in combination with a recurrent nonsense mutation in ENPP1 was …

Regulation Of A Duplicated Locus: Drosophila Sloppy Paired Is Replete With Functionally Overlapping Enhancers., Miki Fujioka, James B Jaynes

Department of Biochemistry and Molecular Biology Faculty Papers

In order to investigate regulation and redundancy within the sloppy paired (slp) locus, we analyzed 30 kilobases of DNA encompassing the tandem, coordinately regulated slp1 and slp2 transcription units. We found a remarkable array of stripe enhancers with overlapping activities surrounding the slp1 transcription unit, and, unexpectedly, glial cell enhancers surrounding slp2. The slp stripe regulatory region generates 7 stripes at blastoderm, and later 14 stripes that persist throughout embryogenesis. Phylogenetic analysis among drosophilids suggests that the multiplicity of stripe enhancers did not evolve through recent duplication. Most of the direct integration among cis-regulatory modules appears to be simply additive, …

Microarray-Based Analysis Of Differential Gene Expression Between Infective And Noninfective Larvae Of Strongyloides Stercoralis., Roshan Ramanathan, Sudhir Varma, José M C Ribeiro, Timothy G Myers, Thomas J Nolan, David Abraham, James B Lok, Thomas B Nutman

Department of Microbiology and Immunology Faculty Papers

BACKGROUND: Differences between noninfective first-stage (L1) and infective third-stage (L3i) larvae of parasitic nematode Strongyloides stercoralis at the molecular level are relatively uncharacterized. DNA microarrays were developed and utilized for this purpose.

METHODS AND FINDINGS: Oligonucleotide hybridization probes for the array were designed to bind 3,571 putative mRNA transcripts predicted by analysis of 11,335 expressed sequence tags (ESTs) obtained as part of the Nematode EST project. RNA obtained from S. stercoralis L3i and L1 was co-hybridized to each array after labeling the individual samples with different fluorescent tags. Bioinformatic predictions of gene function were developed using a novel cDNA Annotation …

Mitochondrial Genome Sequence Analysis: A Custom Bioinformatics Pipeline Substantially Improves Affymetrix Mitochip V2.0 Call Rate And Accuracy., Hongbo M Xie, Juan C Perin, Theodore G Schurr, Matthew C Dulik, Sergey I Zhadanov, Joseph A Baur, Michael P King, Emily Place, Colleen Clarke, Michael Grauer, Jonathan Schug, Avni Santani, Anthony Albano, Cecilia Kim, Vincent Procaccio, Hakon Hakonarson, Xiaowu Gai, Marni J Falk

Department of Biochemistry and Molecular Biology Faculty Papers

BACKGROUND: Mitochondrial genome sequence analysis is critical to the diagnostic evaluation of mitochondrial disease. Existing methodologies differ widely in throughput, complexity, cost efficiency, and sensitivity of heteroplasmy detection. Affymetrix MitoChip v2.0, which uses a sequencing-by-genotyping technology, allows potentially accurate and high-throughput sequencing of the entire human mitochondrial genome to be completed in a cost-effective fashion. However, the relatively low call rate achieved using existing software tools has limited the wide adoption of this platform for either clinical or research applications. Here, we report the design and development of a custom bioinformatics software pipeline that achieves a much improved call rate …

Survival Associated Pathway Identification With Group Lp Penalized Global Auc Maximization., Zhenqiu Liu, Laurence S Magder, Terry Hyslop, Li Mao

Department of Pharmacology and Experimental Therapeutics Faculty Papers

It has been demonstrated that genes in a cell do not act independently. They interact with one another to complete certain biological processes or to implement certain molecular functions. How to incorporate biological pathways or functional groups into the model and identify survival associated gene pathways is still a challenging problem. In this paper, we propose a novel iterative gradient based method for survival analysis with group Lp penalized global AUC summary maximization. Unlike LASSO, Lp (p < 1) (with its special implementation entitled adaptive LASSO) is asymptotic unbiased and has oracle properties 1. We first extend Lp for individual gene identification to group Lp penalty for pathway selection, and then develop a novel iterative gradient algorithm for penalized global AUC summary maximization (IGGAUCS). This method incorporates the genetic pathways into global AUC summary maximization and identifies survival associated pathways instead of individual genes. The tuning parameters are determined using 10-fold cross validation with training data only. The prediction performance is evaluated using test data. We apply the proposed method to survival outcome analysis with gene expression profile and identify multiple pathways simultaneously. Experimental results with simulation and gene expression data demonstrate that the proposed procedures can be used for identifying important biological pathways that are related to survival phenotype and for building a parsimonious model for predicting the survival times.