title: Analyzing patterns of staining in immunohistochemical studies: application to a study of prostate cancer recurrence.
abstract: BACKGROUND
  Immunohistochemical studies use antibodies to stain tissues with the goal of quantifying protein expression. However, protein expression is often heterogeneous resulting in variable degrees and patterns of staining. This problem is particularly acute in prostate cancer, where tumors are infiltrative and heterogeneous in nature. In this article, we introduce analytic approaches that explicitly consider both the frequency and intensity of tissue staining.
  
  
  METHODS
  Compositional data analysis is a technique used to analyze vectors of unit-sum proportions, such as those obtained from soil sample studies or species abundance surveys. We summarized specimen staining patterns by the proportion of cells staining at mild, moderate, and intense levels and used compositional data analysis to summarize and compare the resulting staining profiles.
  
  
  RESULTS
  In a study of Syndecan-1 staining patterns among 44 localized prostate cancer cases with Gleason score 7 disease, compositional data analysis did not detect a statistically significant difference between the staining patterns in recurrent (n = 22) versus nonrecurrent (n = 22) patients. Results indicated only modest increases in the proportion of cells staining at a moderate intensity in the recurrent group. In contrast, an analysis that compared quantitative scores across groups indicated a (borderline) significant increase in staining in the recurrent group (P = 0.05, t test).
  
  
  CONCLUSIONS
  Compositional data analysis offers a novel analytic approach for immunohistochemical studies, providing greater insight into differences in staining patterns between groups, but possibly lower statistical power than existing, score-based methods. When appropriate, we recommend conducting a compositional data analysis in addition to a standard score-based analysis.
authors.affiliation: Translational and Outcomes Research, Fred Hutchinson Cancer Research Center, Mailstop M2-B230, 1100 Fairview Avenue North, P.O. Box 19024, Seattle, WA 98109, USA. retzioni@fhcrc.org
authors.forename: Ruth
authors.initials: R
authors.lastname: Etzioni
authors.forename: Sarah
authors.initials: S
authors.lastname: Hawley
authors.forename: Dean
authors.initials: D
authors.lastname: Billheimer
authors.forename: Lawrence D
authors.initials: LD
authors.lastname: True
authors.forename: Beatrice
authors.initials: B
authors.lastname: Knudsen
mesh_terms: D014408:Biomarkers, Tumor / Q000276:immunology; D003198:Computer Simulation; D006801:Humans; D007150:Immunohistochemistry / Q000379:methods*; D008297:Male; D008562:Membrane Glycoproteins / Q000276:immunology; D015233:Models, Statistical; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis* / Q000276:immunology; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000276:immunology / Q000473:pathology; D011509:Proteoglycans / Q000276:immunology; D012044:Regression Analysis; D013194:Staining and Labeling; D053668:Syndecan-1; D053667:Syndecans
chemical_list: D014408:Biomarkers, Tumor; D008562:Membrane Glycoproteins; D011509:Proteoglycans; C508241:SDC1 protein, human; D053668:Syndecan-1; D053667:Syndecans