title: The impact of the number of cores on tissue microarray studies investigating             prostate cancer biomarkers.
abstract: Most tissue microarray studies have used a single 0.6-mm tissue core per             donor tissue. It has been suggested that multiple cores per donor can increase             the representativity of tissue microarray studies. To estimate the potential benefit             of multiple cores, we analyzed Ki67 and p53 in triplet cores taken from three             different areas of 3,261 prostate cancer tissue blocks. Both p53 and Ki67 labeling             index were linked to advanced tumor stage (p<0.0001 each), Gleason score (p<0.0001),             and early PSA recurrence (p<0.0001) independently of whether the 3 tissue spots             were analyzed separately or combined for a consensus result. The rate of positive             findings increased with the amount of analyzed tissue. The average Ki67 labeling             index was higher in tumors with 3 interpretable spots (5.3±5.6) as compared to             two (4.1±4.7) or one interpretable spot (4.1±4.2, p<0.0001). For p53, tumors             with three interpretable spots were positive in 3.8% of cases, and tumors with             1 or 2 interpretable spots in 1.9% only (p=0.003). These data demonstrate that             using multiple cores in a tissue microarray does not necessarily increase the             ability to identify associations of biomarkers with tumor phenotype and prognosis             but has always the disadvantage of additional work and tissue requirements. Multiple             cores may even lead to statistical problems if unequal amounts of tissue are analyzed             per tumor.
authors.affiliation: Institute of Pathology, University Medical Center, Hamburg-Eppendorf,                     Germany.
authors.forename: Pierre
authors.initials: P
authors.lastname: Tennstedt
authors.forename: Patricia
authors.initials: P
authors.lastname: Köster
authors.forename: Andreas
authors.initials: A
authors.lastname: Brüchmann
authors.forename: Martina
authors.initials: M
authors.lastname: Mirlacher
authors.forename: Alexander
authors.initials: A
authors.lastname: Haese
authors.forename: Thomas
authors.initials: T
authors.lastname: Steuber
authors.forename: Guido
authors.initials: G
authors.lastname: Sauter
authors.forename: Hartwig
authors.initials: H
authors.lastname: Huland
authors.forename: Markus
authors.initials: M
authors.lastname: Graefen
authors.forename: Thorsten
authors.initials: T
authors.lastname: Schlomm
authors.forename: Sarah
authors.initials: S
authors.lastname: Minner
authors.forename: Ronald
authors.initials: R
authors.lastname: Simon
mesh_terms: D000368:Aged; D014408:Biomarkers, Tumor / Q000032:analysis* / Q000096:biosynthesis; D006801:Humans; D007150:Immunohistochemistry; D019394:Ki-67 Antigen / Q000032:analysis* / Q000096:biosynthesis; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D011471:Prostatic Neoplasms / Q000737:chemistry* / Q000378:metabolism / Q000473:pathology; D040081:Protein Array Analysis / Q000379:methods; D016159:Tumor Suppressor Protein p53 / Q000032:analysis* / Q000096:biosynthesis
chemical_list: D014408:Biomarkers, Tumor; D019394:Ki-67 Antigen; C495901:TP53 protein, human; D016159:Tumor Suppressor Protein p53