title: Imaging the clear cell renal cell carcinoma proteome.
abstract: OBJECTIVE
  A key barrier to identifying tissue biomarkers of clear cell renal cell carcinoma is the heterogeneity of protein expression in tissue. However, by providing spectra for every 0.05 mm(2) area of tissue, imaging mass spectrometry reveals the spatial distribution of peptides. We determined whether this approach could be used to identify and map protein signatures of clear cell renal cell carcinoma.
  
  
  METHODS
  We constructed 2 tissue microarrays with 2 cores each of matched tumor and normal tissue from the nephrectomy specimens of 70 patients with clear cell renal cell carcinoma. Samples were analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. In each tissue microarray peptide signatures were identified that differentiated cancer from normal tissue. The signatures were then cross validated. Mass spectrometry/mass spectrometry sequencing was performed to determine the identity of select, differentially expressed peptides. Immunohistochemistry was used for validation.
  
  
  RESULTS
  In each tissue microarray peptide signatures were identified that had 94.7% to 98.5% classification accuracy for each 0.05 mm(2) spot (spectrum) and 96.9% to 100% accuracy for each tissue core. Cross validation across tissue microarrays revealed a classification accuracy of 82.6% to 84.7% for each spot and 88.9% to 92.4% for each core. We identified vimentin, histone 2A.X and α-enolase as proteins with greater expression in cancer tissue. This was validated by immunohistochemistry.
  
  
  CONCLUSIONS
  Imaging mass spectrometry identified and mapped specific peptides that accurately distinguished malignant from normal renal tissue. This demonstrates its potential as a novel, high throughput approach to clear cell renal cell carcinoma biomarker discovery. Given the multiple pathways and known heterogeneity involved in tumors such as clear cell renal cell carcinoma, multiple peptide signatures that maintain their spatial relationships may outperform traditional protein biomarkers.
authors.affiliation: Department of Urologic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee. Electronic address: tomorgan@med.umich.edu.
authors.forename: Todd M
authors.initials: TM
authors.lastname: Morgan
authors.forename: Erin H
authors.initials: EH
authors.lastname: Seeley
authors.forename: Oluwole
authors.initials: O
authors.lastname: Fadare
authors.forename: Richard M
authors.initials: RM
authors.lastname: Caprioli
authors.forename: Peter E
authors.initials: PE
authors.lastname: Clark
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis* / Q000235:genetics; D002292:Carcinoma, Renal Cell / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism; D005260:Female; D006801:Humans; D007150:Immunohistochemistry; D007680:Kidney Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism; D008297:Male; D020543:Proteome / Q000235:genetics* / Q000378:metabolism; D040901:Proteomics / Q000379:methods; D015203:Reproducibility of Results; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*; D053719:Tandem Mass Spectrometry / Q000379:methods*
chemical_list: D014408:Biomarkers, Tumor; D020543:Proteome