title: Modeling measurement error in tumor characterization studies.
abstract: BACKGROUND
  Etiologic studies of cancer increasingly use molecular features such as gene expression, DNA methylation and sequence mutation to subclassify the cancer type. In large population-based studies, the tumor tissues available for study are archival specimens that provide variable amounts of amplifiable DNA for molecular analysis. As molecular features measured from small amounts of tumor DNA are inherently noisy, we propose a novel approach to improve statistical efficiency when comparing groups of samples. We illustrate the phenomenon using the MethyLight technology, applying our proposed analysis to compare MLH1 DNA methylation levels in males and females studied in the Colon Cancer Family Registry.
  
  
  RESULTS
  We introduce two methods for computing empirical weights to model heteroscedasticity that is caused by sampling variable quantities of DNA for molecular analysis. In a simulation study, we show that using these weights in a linear regression model is more powerful for identifying differentially methylated loci than standard regression analysis. The increase in power depends on the underlying relationship between variation in outcome measure and input DNA quantity in the study samples.
  
  
  CONCLUSIONS
  Tumor characteristics measured from small amounts of tumor DNA are inherently noisy. We propose a statistical analysis that accounts for the measurement error due to sampling variation of the molecular feature and show how it can improve the power to detect differential characteristics between patient groups.
authors.affiliation: Department of Preventive Medicine, USC Keck School of Medicine, Los Angeles, California 90089-9176, USA.
authors.forename: Cyril
authors.initials: C
authors.lastname: Rakovski
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Weisenberger
authors.forename: Paul
authors.initials: P
authors.lastname: Marjoram
authors.forename: Peter W
authors.initials: PW
authors.lastname: Laird
authors.forename: Kimberly D
authors.initials: KD
authors.lastname: Siegmund
mesh_terms: D048868:Adaptor Proteins, Signal Transducing / Q000235:genetics*; D020087:Alu Elements; D003110:Colonic Neoplasms / Q000235:genetics*; D003198:Computer Simulation; D019175:DNA Methylation; D004252:DNA Mutational Analysis; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D008297:Male; D000070957:MutL Protein Homolog 1; D009687:Nuclear Proteins / Q000235:genetics*; D012044:Regression Analysis
chemical_list: D048868:Adaptor Proteins, Signal Transducing; C501486:MLH1 protein, human; D009687:Nuclear Proteins; D000070957:MutL Protein Homolog 1