title: Mining tissue microarray data to uncover combinations of biomarker expression patterns that improve intermediate staging and grading of clear cell renal cell cancer.
abstract: OBJECTIVE
  Tumor stage and nuclear grade are the most important prognostic parameters of clear cell renal cell carcinoma (ccRCC). The progression risk of ccRCC remains difficult to predict particularly for tumors with organ-confined stage and intermediate differentiation grade. Elucidating molecular pathways deregulated in ccRCC may point to novel prognostic parameters that facilitate planning of therapeutic approaches.
  
  
  METHODS
  Using tissue microarrays, expression patterns of 15 different proteins were evaluated in over 800 ccRCC patients to analyze pathways reported to be physiologically controlled by the tumor suppressors von Hippel-Lindau protein and phosphatase and tensin homologue (PTEN). Tumor staging and grading were improved by performing variable selection using Cox regression and a recursive bootstrap elimination scheme.
  
  
  RESULTS
  Patients with pT2 and pT3 tumors that were p27 and CAIX positive had a better outcome than those with all remaining marker combinations. A prolonged survival among patients with intermediate grade (grade 2) correlated with both nuclear p27 and cytoplasmic PTEN expression, as well as with inactive, nonphosphorylated ribosomal protein S6. By applying graphical log-linear modeling for over 700 ccRCC for which the molecular parameters were available, only a weak conditional dependence existed between the expression of p27, PTEN, CAIX, and p-S6, suggesting that the dysregulation of several independent pathways are crucial for tumor progression.
  
  
  CONCLUSIONS
  The use of recursive bootstrap elimination, as well as graphical log-linear modeling for comprehensive tissue microarray (TMA) data analysis allows the unraveling of complex molecular contexts and may improve predictive evaluations for patients with advanced renal cancer.
authors.affiliation: Department of Urology, University Hospital Zurich, Center for Systems Physiology and Metabolic Diseases, ETH Zurich, and Institute of Surgical Pathology, Zurich, Switzerland.
authors.forename: Corinne
authors.initials: C
authors.lastname: Dahinden
authors.forename: Barbara
authors.initials: B
authors.lastname: Ingold
authors.forename: Peter
authors.initials: P
authors.lastname: Wild
authors.forename: Gunther
authors.initials: G
authors.lastname: Boysen
authors.forename: Van-Duc
authors.initials: VD
authors.lastname: Luu
authors.forename: Matteo
authors.initials: M
authors.lastname: Montani
authors.forename: Glen
authors.initials: G
authors.lastname: Kristiansen
authors.forename: Tullio
authors.initials: T
authors.lastname: Sulser
authors.forename: Peter
authors.initials: P
authors.lastname: Bühlmann
authors.forename: Holger
authors.initials: H
authors.lastname: Moch
authors.forename: Peter
authors.initials: P
authors.lastname: Schraml
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000032:analysis*; D002292:Carcinoma, Renal Cell / Q000378:metabolism* / Q000401:mortality / Q000473:pathology; D018450:Disease Progression; D005260:Female; D006801:Humans; D007680:Kidney Neoplasms / Q000378:metabolism* / Q000401:mortality / Q000473:pathology; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D009363:Neoplasm Proteins / Q000032:analysis*; D009367:Neoplasm Staging; D010641:Phenotype; D011379:Prognosis; D040081:Protein Array Analysis
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins