An Immunohistochemical Algorithm for Ovarian Carcinoma Typing

Martin Köbel, Kurosh Rahimi, Peter F. Rambau, Christopher Naugler, Cécile Le Page, Liliane Meunier, Manon de Ladurantaye, Sandra Lee, Samuel Leung, Ellen L Goode, Susan J. Ramus, Joseph W. Carlson, Xiaodong Li, Carol A. Ewanowich, Linda E. Kelemen, Barbara Vanderhyden, Diane Provencher, David Huntsman, Cheng Han Lee, C. B. GilksAnne Marie Mes Masson

Research output: Contribution to journalArticle

52 Citations (Scopus)

Abstract

There are 5 major histotypes of ovarian carcinomas. Diagnostic typing criteria have evolved over time, and past cohorts may be misclassified by current standards. Our objective was to reclassify the recently assembled Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type cohorts using immunohistochemical (IHC) biomarkers and to develop an IHC algorithm for ovarian carcinoma histotyping. A total of 1626 ovarian carcinoma samples from the Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type were subjected to a reclassification by comparing the original with the predicted histotype. Histotype prediction was derived from a nominal logistic regression modeling using a previously reclassified cohort (N=784) with the binary input of 8 IHC markers. Cases with discordant original or predicted histotypes were subjected to arbitration. After reclassification, 1762 cases from all cohorts were subjected to prediction models (χ Automatic Interaction Detection, recursive partitioning, and nominal logistic regression) with a variable IHC marker input. The histologic type was confirmed in 1521/1626 (93.5%) cases of the Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type cohorts. The highest misclassification occurred in the endometrioid type, where most of the changes involved reclassification from endometrioid to high-grade serous carcinoma, which was additionally supported by mutational data and outcome. Using the reclassified histotype as the endpoint, a 4-marker prediction model correctly classified 88%, a 6-marker 91%, and an 8-marker 93% of the 1762 cases. This study provides statistically validated, inexpensive IHC algorithms, which have versatile applications in research, clinical practice, and clinical trials.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially. http://creativecommons.org/licenses/by-nc-nd/4.0.

Original languageEnglish (US)
JournalInternational Journal of Gynecological Pathology
DOIs
StateAccepted/In press - Mar 11 2016

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Alberta
Carcinoma
Licensure
Logistic Models
Neoplasms
Negotiating
Biomarkers
Clinical Trials
Research

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Obstetrics and Gynecology

Cite this

Köbel, M., Rahimi, K., Rambau, P. F., Naugler, C., Le Page, C., Meunier, L., ... Mes Masson, A. M. (Accepted/In press). An Immunohistochemical Algorithm for Ovarian Carcinoma Typing. International Journal of Gynecological Pathology. https://doi.org/10.1097/PGP.0000000000000274

An Immunohistochemical Algorithm for Ovarian Carcinoma Typing. / Köbel, Martin; Rahimi, Kurosh; Rambau, Peter F.; Naugler, Christopher; Le Page, Cécile; Meunier, Liliane; de Ladurantaye, Manon; Lee, Sandra; Leung, Samuel; Goode, Ellen L; Ramus, Susan J.; Carlson, Joseph W.; Li, Xiaodong; Ewanowich, Carol A.; Kelemen, Linda E.; Vanderhyden, Barbara; Provencher, Diane; Huntsman, David; Lee, Cheng Han; Gilks, C. B.; Mes Masson, Anne Marie.

In: International Journal of Gynecological Pathology, 11.03.2016.

Research output: Contribution to journalArticle

Köbel, M, Rahimi, K, Rambau, PF, Naugler, C, Le Page, C, Meunier, L, de Ladurantaye, M, Lee, S, Leung, S, Goode, EL, Ramus, SJ, Carlson, JW, Li, X, Ewanowich, CA, Kelemen, LE, Vanderhyden, B, Provencher, D, Huntsman, D, Lee, CH, Gilks, CB & Mes Masson, AM 2016, 'An Immunohistochemical Algorithm for Ovarian Carcinoma Typing', International Journal of Gynecological Pathology. https://doi.org/10.1097/PGP.0000000000000274
Köbel, Martin ; Rahimi, Kurosh ; Rambau, Peter F. ; Naugler, Christopher ; Le Page, Cécile ; Meunier, Liliane ; de Ladurantaye, Manon ; Lee, Sandra ; Leung, Samuel ; Goode, Ellen L ; Ramus, Susan J. ; Carlson, Joseph W. ; Li, Xiaodong ; Ewanowich, Carol A. ; Kelemen, Linda E. ; Vanderhyden, Barbara ; Provencher, Diane ; Huntsman, David ; Lee, Cheng Han ; Gilks, C. B. ; Mes Masson, Anne Marie. / An Immunohistochemical Algorithm for Ovarian Carcinoma Typing. In: International Journal of Gynecological Pathology. 2016.
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