title: Tissue microarrays: one size does not fit all.
abstract: BACKGROUND
  Although tissue microarrays (TMAs) are commonly employed in clinical and basic-science research, there are no guidelines for evaluating the appropriateness of a TMA for a given biomarker and tumor type. Furthermore, TMA performance across multiple biomarkers has not been systematically explored.
  
  
  METHODS
  A simulated TMA with between 1 and 10 cores was designed to study tumor expression of 6 biomarkers with varied expression patterns (B7-H1, B7-H3, survivin, Ki-67, CAIX, and IMP3) using 100 patients with clear cell renal cell carcinoma (RCC). We evaluated agreement between whole tissue section and TMA immunohistochemical biomarker quantification to assess how many TMA cores are necessary to adequately represent RCC whole tissue section expression. Additionally, we evaluated associations of whole tissue section and TMA expression with RCC-specific death.
  
  
  RESULTS
  The number of simulated TMA cores necessary to adequately represent whole tissue section quantification is biomarker specific. Although 2-3 cores appeared adequate for B7-H3, Ki-67, CAIX, and IMP3, even as many as 10 cores resulted in poor agreement for B7-H1 and survivin compared to RCC whole tissue sections. While whole tissue section B7-H1 was significantly associated with RCC-specific death, no significant associations were detected using as many as 10 TMA cores, suggesting that TMAs can result in false-negative findings if the TMA is not optimally designed.
  
  
  CONCLUSIONS
  Prior to TMA analysis, the number of TMA cores necessary to accurately represent biomarker expression on whole tissue sections should be established as there is not a one-size-fits-all TMA. We illustrate the use of a simulated TMA as a cost-effective tool for this purpose.
authors.affiliation: Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota, USA. eckelpassow.jeanette@mayo.edu
authors.forename: Jeanette E
authors.initials: JE
authors.lastname: Eckel-Passow
authors.forename: Christine M
authors.initials: CM
authors.lastname: Lohse
authors.forename: Yuri
authors.initials: Y
authors.lastname: Sheinin
authors.forename: Paul L
authors.initials: PL
authors.lastname: Crispen
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Krco
authors.forename: Eugene D
authors.initials: ED
authors.lastname: Kwon
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis*; D001706:Biopsy; D002292:Carcinoma, Renal Cell / Q000737:chemistry* / Q000175:diagnosis / Q000401:mortality / Q000473:pathology / Q000601:surgery; D016009:Chi-Square Distribution; D005188:False Negative Reactions; D005260:Female; D006801:Humans; D007150:Immunohistochemistry / Q000592:standards; D007680:Kidney Neoplasms / Q000737:chemistry* / Q000175:diagnosis / Q000401:mortality / Q000473:pathology / Q000601:surgery; D008297:Male; D008910:Minnesota; D009367:Neoplasm Staging; D009392:Nephrectomy; D015588:Observer Variation; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012042:Registries; D015203:Reproducibility of Results; D016019:Survival Analysis; D046888:Tissue Array Analysis / Q000592:standards; D016896:Treatment Outcome
chemical_list: D014408:Biomarkers, Tumor