BACKGROUND: Tissue microarrays (TMAs) allow high-throughput evaluation of protein expression from archived tissue samples. We identified characteristics specific to ovarian cancer that may influence TMA interpretation. METHODS: TMAs were constructed using triplicate core samples from 174 epithelial ovarian cancers. Stains for p53, Ki-67, estrogen receptor-alpha, progesterone receptor, Her-2, WT-1, cytokeratin 7, and cytokeratin 20 were evaluated by intraclass correlation coefficients, Spearman correlation coefficients, the effect of sample age, and tumor histology on the ability to score the cores, and inter-rater reliability. RESULTS: The interclass correlation coefficient and the mean Spearman correlation coefficients among 3 cores were > or = 0.91 and 0.87, respectively. Tissue age and tumor histology were not predictive of an inability to evaluate stains, but borderline tumors had a 2 to 4-fold increase in the risk of having uninterpretable cores over invasive tumors. There was moderate to substantial concordance between the two pathologists for estrogen receptor-alpha [Cohen's Kappa (kappa), 0.79] and Ki-67 (kappa, 0.52). The prevalence of positive staining cells by histologic type was comparable with previous studies. CONCLUSION: TMA is a valid method for evaluating antigen expression in invasive ovarian cancer but should be used with caution for borderline tumors. We suggest several methods of quality control based on intercore comparisons and show that some antigens may be affected by age of the samples.
BACKGROUND: Tissue microarrays (TMAs) allow high-throughput evaluation of protein expression from archived tissue samples. We identified characteristics specific to ovarian cancer that may influence TMA interpretation. METHODS:TMAs were constructed using triplicate core samples from 174 epithelial ovarian cancers. Stains for p53, Ki-67, estrogen receptor-alpha, progesterone receptor, Her-2, WT-1, cytokeratin 7, and cytokeratin 20 were evaluated by intraclass correlation coefficients, Spearman correlation coefficients, the effect of sample age, and tumor histology on the ability to score the cores, and inter-rater reliability. RESULTS: The interclass correlation coefficient and the mean Spearman correlation coefficients among 3 cores were > or = 0.91 and 0.87, respectively. Tissue age and tumor histology were not predictive of an inability to evaluate stains, but borderline tumors had a 2 to 4-fold increase in the risk of having uninterpretable cores over invasive tumors. There was moderate to substantial concordance between the two pathologists for estrogen receptor-alpha [Cohen's Kappa (kappa), 0.79] and Ki-67 (kappa, 0.52). The prevalence of positive staining cells by histologic type was comparable with previous studies. CONCLUSION:TMA is a valid method for evaluating antigen expression in invasive ovarian cancer but should be used with caution for borderline tumors. We suggest several methods of quality control based on intercore comparisons and show that some antigens may be affected by age of the samples.
Authors: P Muti; H L Bradlow; A Micheli; V Krogh; J L Freudenheim; H J Schünemann; M Stanulla; J Yang; D W Sepkovic; M Trevisan; F Berrino Journal: Epidemiology Date: 2000-11 Impact factor: 4.822
Authors: Daniel G Rosen; Xuelin Huang; Michael T Deavers; Anais Malpica; Elvio G Silva; Jinsong Liu Journal: Mod Pathol Date: 2004-07 Impact factor: 7.842
Authors: Estrid V S Høgdall; Lise Christensen; Claus K Høgdall; Jan Blaakaer; Simon Gayther; Ian J Jacobs; Ib Jarle Christensen; Susanne K Kjaer Journal: Oncol Rep Date: 2007-11 Impact factor: 3.906
Authors: Holly R Harris; Megan S Rice; Amy L Shafrir; Elizabeth M Poole; Mamta Gupta; Jonathan L Hecht; Kathryn L Terry; Shelley S Tworoger Journal: Cancer Epidemiol Biomarkers Prev Date: 2017-11-13 Impact factor: 4.254
Authors: Amy L Shafrir; Megan S Rice; Mamta Gupta; Kathryn L Terry; Bernard A Rosner; Rulla M Tamimi; Jonathan L Hecht; Shelley S Tworoger Journal: Gynecol Oncol Date: 2016-10-05 Impact factor: 5.482
Authors: Jeanette E Eckel-Passow; Christine M Lohse; Yuri Sheinin; Paul L Crispen; Christopher J Krco; Eugene D Kwon Journal: Diagn Pathol Date: 2010-07-07 Impact factor: 2.644
Authors: Domenico Coppola; Santo V Nicosia; Andrea Doty; Thomas A Sellers; Ji-Hyun Lee; Jimmy Fulp; Zachary Thompson; Sanja Galeb; John McLaughlin; Steven A Narod; Joellen Schildkraut; Tuya Pal Journal: Anticancer Res Date: 2012-11 Impact factor: 2.480
Authors: Jonathan L Hecht; Joanne Kotsopoulos; Susan E Hankinson; Shelley S Tworoger Journal: Cancer Epidemiol Biomarkers Prev Date: 2009-04-21 Impact factor: 4.254
Authors: Mollie E Barnard; Alexander Pyden; Megan S Rice; Miguel Linares; Shelley S Tworoger; Brooke E Howitt; Emily E Meserve; Jonathan L Hecht Journal: Gynecol Oncol Date: 2018-07-09 Impact factor: 5.482
Authors: Joanne Kotsopoulos; Jonathan L Hecht; Jonathan D Marotti; Linda E Kelemen; Shelley S Tworoger Journal: Int J Cancer Date: 2010-05-01 Impact factor: 7.396
Authors: Mollie E Barnard; Jonathan L Hecht; Megan S Rice; Mamta Gupta; Holly R Harris; A Heather Eliassen; Bernard A Rosner; Kathryn L Terry; Shelley S Tworoger Journal: Cancer Epidemiol Biomarkers Prev Date: 2018-10-30 Impact factor: 4.254