Martin Köbel1, Steve E Kalloger, Sandra Lee, Máire A Duggan, Linda E Kelemen, Leah Prentice, Kimberly R Kalli, Brooke L Fridley, Daniel W Visscher, Gary L Keeney, Robert A Vierkant, Julie M Cunningham, Christine Chow, Roberta B Ness, Kirsten Moysich, Robert Edwards, Francesmary Modugno, Clareann Bunker, Eva L Wozniak, Elizabeth Benjamin, Simon A Gayther, Aleksandra Gentry-Maharaj, Usha Menon, C Blake Gilks, David G Huntsman, Susan J Ramus, Ellen L Goode. 1. Authors' Affiliations: Department of Pathology and Laboratory Medicine; Department of Population Health Research, Alberta Health Services-Cancer Care and Department of Medical Genetics, University of Calgary, Calgary, Alberta; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada; Department of Medical Oncology; Division of Anatomic Pathology, Department of Laboratory Medicine and Pathology; Division of Biomedical Statistics and Informatics, Department of Health Science Research; Division of Experimental Pathology and Laboratory Medicine, Department of Laboratory Medicine and Pathology; Division of Epidemiology, Department of Health Science Research, Mayo Clinic, Rochester, Minnesota; Biostatistics and Informatics Shared Resource, University of Kansas Medical Center, Kansas City, Kansas; University of Texas School of Public Health, Houston, Texas; Roswell Park Cancer Institute, Buffalo, New York, New York; Women's Cancer Research Center, Magee-Womens Research Institute, University of Pittsburgh Cancer Institute; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania; Gynaecological Cancer Research Centre, Women's Cancer, UCL EGA Institute for Women's Health; Department of Pathology, Cancer Institute, UCL, London, United Kingdom; and Department of Preventive Medicine, Keck School of Medicine, USC/Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California.
Abstract
BACKGROUND: Ovarian carcinoma is composed of five major histologic types, which associate with outcome and predict therapeutic response. Our aim was to evaluate histologic type assessments across the centers participating in the Ovarian Tumor Tissue Analysis (OTTA) consortium using an immunohistochemical (IHC) prediction model. METHODS: Tissue microarrays (TMA) and clinical data were available for 524 pathologically confirmed ovarian carcinomas. Centralized IHC was conducted for ARID1A, CDKN2A, DKK1, HNF1B, MDM2, PGR, TP53, TFF3, VIM, and WT1, and three histologic type assessments were compared: the original pathologic type, an IHC-based calculated type (termed TB_COSPv2), and a WT1-assisted TMA core review. RESULTS: The concordance between TB_COSPv2 type and original type was 73%. Applying WT1-assisted core review, the remaining 27% discordant cases subdivided into unclassifiable (6%), TB_COSPv2 error (6%), and original type error (15%). The largest discordant subgroup was classified as endometrioid carcinoma by original type and as high-grade serous carcinoma (HGSC) by TB_COSPv2. When TB_COSPv2 classification was used, the difference in overall survival of endometrioid carcinoma compared with HGSC became significant [RR 0.60; 95% confidence interval (CI), 0.37-0.93; P = 0.021], consistent with previous reports. In addition, 71 cases with unclear original type could be histologically classified by TB_COSPv2. CONCLUSIONS: Research cohorts, particularly those across different centers within consortia, show significant variability in original histologic type diagnosis. Our IHC-based reclassification produced more homogeneous types with respect to outcome than original type. IMPACT: Biomarker-based classification of ovarian carcinomas is feasible, improves comparability of results across research studies, and can reclassify cases which lack reliable original pathology.
BACKGROUND:Ovarian carcinoma is composed of five major histologic types, which associate with outcome and predict therapeutic response. Our aim was to evaluate histologic type assessments across the centers participating in the Ovarian Tumor Tissue Analysis (OTTA) consortium using an immunohistochemical (IHC) prediction model. METHODS: Tissue microarrays (TMA) and clinical data were available for 524 pathologically confirmed ovarian carcinomas. Centralized IHC was conducted for ARID1A, CDKN2A, DKK1, HNF1B, MDM2, PGR, TP53, TFF3, VIM, and WT1, and three histologic type assessments were compared: the original pathologic type, an IHC-based calculated type (termed TB_COSPv2), and a WT1-assisted TMA core review. RESULTS: The concordance between TB_COSPv2 type and original type was 73%. Applying WT1-assisted core review, the remaining 27% discordant cases subdivided into unclassifiable (6%), TB_COSPv2 error (6%), and original type error (15%). The largest discordant subgroup was classified as endometrioid carcinoma by original type and as high-grade serous carcinoma (HGSC) by TB_COSPv2. When TB_COSPv2 classification was used, the difference in overall survival of endometrioid carcinoma compared with HGSC became significant [RR 0.60; 95% confidence interval (CI), 0.37-0.93; P = 0.021], consistent with previous reports. In addition, 71 cases with unclear original type could be histologically classified by TB_COSPv2. CONCLUSIONS: Research cohorts, particularly those across different centers within consortia, show significant variability in original histologic type diagnosis. Our IHC-based reclassification produced more homogeneous types with respect to outcome than original type. IMPACT: Biomarker-based classification of ovarian carcinomas is feasible, improves comparability of results across research studies, and can reclassify cases which lack reliable original pathology.
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