BioSpace.com

Biotech and Pharmaceutical
News & Jobs
Search the Site
 
   
Biotechnology and Pharmaceutical Channel Medical Device and Diagnostics Channel Clinical Research Channel BioSpace Collaborative    Job Seekers:  Register | Login          Employers:  Register | Login  

NEWSLETTERS
Free Newsletters
Archive
My Subscriptions

NEWS
News by Subject
News by Disease
News by Date
PLoS
Search News
Post Your News
JoVE

CAREER NETWORK
Job Seeker Login
Most Recent Jobs
Browse Biotech Jobs
Search Jobs
Post Resume
Career Fairs
Career Resources
For Employers

HOTBEDS
Regional News
US & Canada
  Biotech Bay
  Biotech Beach
  Genetown
  Pharm Country
  BioCapital
  BioMidwest
  Bio NC
  BioForest
  Southern Pharm
  BioCanada East
  US Device
Europe
Asia

DIVERSITY

INVESTOR
Market Summary
News
IPOs

PROFILES
Company Profiles

START UPS
Companies
Events

INTELLIGENCE
Research Store

INDUSTRY EVENTS
Biotech Events
Post an Event
RESOURCES
Real Estate
Business Opportunities

PLoS By Category | Recent PLoS Articles
Hematology - Immunology - Mathematics - Oncology

A Strategy for Full Interrogation of Prognostic Gene Expression Patterns: Exploring the Biology of Diffuse Large B Cell Lymphoma
Published: Thursday, August 04, 2011
Author: Lisa M. Rimsza et al.

by Lisa M. Rimsza, Joseph M. Unger, Margaret E. Tome, Michael L. LeBlanc

Background

Gene expression profiling yields quantitative data on gene expression used to create prognostic models that accurately predict patient outcome in diffuse large B cell lymphoma (DLBCL). Often, data are analyzed with genes classified by whether they fall above or below the median expression level. We sought to determine whether examining multiple cut-points might be a more powerful technique to investigate the association of gene expression with outcome.

Methodology/Principal Findings

We explored gene expression profiling data using variable cut-point analysis for 36 genes with reported prognostic value in DLBCL. We plotted two-group survival logrank test statistics against corresponding cut-points of the gene expression levels and smooth estimates of the hazard ratio of death versus gene expression levels. To facilitate comparisons we also standardized the expression of each of the genes by the fraction of patients that would be identified by any cut-point. A multiple comparison adjusted permutation p-value identified 3 different patterns of significance: 1) genes with significant cut-point points below the median, whose loss is associated with poor outcome (e.g. HLA-DR); 2) genes with significant cut-points above the median, whose over-expression is associated with poor outcome (e.g. CCND2); and 3) genes with significant cut-points on either side of the median, (e.g. extracellular molecules such as FN1).

Conclusions/Significance

Variable cut-point analysis with permutation p-value calculation can be used to identify significant genes that would not otherwise be identified with median cut-points and may suggest biological patterns of gene effects.

  More...

 

//-->