Literature DB >> 21131918

Calculator for ovarian carcinoma subtype prediction.

Steve E Kalloger1, Martin Köbel, Samuel Leung, Erika Mehl, Dongxia Gao, Krista M Marcon, Christine Chow, Blaise A Clarke, David G Huntsman, C Blake Gilks.   

Abstract

With the emerging evidence that the five major ovarian carcinoma subtypes (high-grade serous, clear cell, endometrioid, mucinous, and low-grade serous) are distinct disease entities, management of ovarian carcinoma will become subtype specific in the future. In an effort to improve diagnostic accuracy, we set out to determine if an immunohistochemical panel of molecular markers could reproduce consensus subtype assignment. Immunohistochemical expression of 22 biomarkers were examined on tissue microarrays constructed from 322 archival ovarian carcinoma samples from the British Columbia Cancer Agency archives, for the period between 1984 and 2000, and an independent set of 242 cases of ovarian carcinoma from the Gynaecologic Tissue Bank at Vancouver General Hospital from 2001 to 2008. Nominal logistic regression was used to produce a subtype prediction model for each of these sets of cases. These models were then cross-validated against the other cohort, and then both models were further validated in an independent cohort of 81 ovarian carcinoma samples from five different centers. Starting with data for 22 markers, full model fit, backwards, nominal logistic regression identified the same nine markers (CDKN2A, DKK1, HNF1B, MDM2, PGR, TFF3, TP53, VIM, WT1) as being most predictive of ovarian carcinoma subtype in both the archival and tumor bank cohorts. These models were able to predict subtype in the respective cohort in which they were developed with a high degree of sensitivity and specificity (κ statistics of 0.88±0.02 and 0.86±0.04, respectively). When the models were cross-validated (ie using the model developed in one case series to predict subtype in the other series), the prediction equation's performances were reduced (κ statistics of 0.70±0.04 and 0.61±0.04, respectively) due to differences in frequency of expression of some biomarkers in the two case series. Both models were then validated on the independent series of 81 cases, with very good to excellent ability to predict subtype (κ=0.85±0.06 and 0.78±0.07, respectively). A nine-marker immunohistochemical maker panel can be used to objectively support classification into one of the five major subtypes of ovarian carcinoma.

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Year:  2010        PMID: 21131918     DOI: 10.1038/modpathol.2010.215

Source DB:  PubMed          Journal:  Mod Pathol        ISSN: 0893-3952            Impact factor:   7.842


  31 in total

1.  Molecular-based classification algorithm for endometrial carcinoma categorizes ovarian endometrioid carcinoma into prognostically significant groups.

Authors:  Carlos Parra-Herran; Jordan Lerner-Ellis; Bin Xu; Sam Khalouei; Dina Bassiouny; Matthew Cesari; Nadia Ismiil; Sharon Nofech-Mozes
Journal:  Mod Pathol       Date:  2017-08-04       Impact factor: 7.842

2.  Biomarker-based ovarian carcinoma typing: a histologic investigation in the ovarian tumor tissue analysis consortium.

Authors:  Martin Köbel; 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
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2013-07-23       Impact factor: 4.254

3.  p53 is positively regulated by miR-542-3p.

Authors:  Yemin Wang; Jen-Wei Huang; Maria Castella; David George Huntsman; Toshiyasu Taniguchi
Journal:  Cancer Res       Date:  2014-04-24       Impact factor: 12.701

4.  TFF3 Expression as Stratification Marker in Borderline Epithelial Tumors of the Ovary.

Authors:  Ahmed El-Balat; Iryna Schmeil; Thomas Karn; Sven Becker; Nicole Sänger; Uwe Holtrich; Ruza Arsenic
Journal:  Pathol Oncol Res       Date:  2017-05-03       Impact factor: 3.201

5.  Expression, Epigenetic and Genetic Changes of HNF1B in Endometrial Lesions.

Authors:  Kristýna Němejcová; Ivana Tichá; Petra Kleiblová; Michaela Bártů; David Cibula; Kateřina Jirsová; Pavel Dundr
Journal:  Pathol Oncol Res       Date:  2015-12-19       Impact factor: 3.201

6.  Histotype classification of ovarian carcinoma: A comparison of approaches.

Authors:  Lauren C Peres; Kara L Cushing-Haugen; Michael Anglesio; Kristine Wicklund; Rex Bentley; Andrew Berchuck; Linda E Kelemen; Tayyebeh M Nazeran; C Blake Gilks; Holly R Harris; David G Huntsman; Joellen M Schildkraut; Mary Anne Rossing; Martin Köbel; Jennifer A Doherty
Journal:  Gynecol Oncol       Date:  2018-08-16       Impact factor: 5.482

7.  Trefoil factor 3 expression in epithelial ovarian cancer exerts a minor effect on clinicopathological parameters.

Authors:  Friederike Hoellen; Athina Kostara; Thomas Karn; Uwe Holtrich; Ahmed El-Balat; Mike Otto; Achim Rody; Lars C Hanker
Journal:  Mol Clin Oncol       Date:  2016-08-17

Review 8.  The rise of genomic profiling in ovarian cancer.

Authors:  Rebecca A Previs; Anil K Sood; Gordon B Mills; Shannon N Westin
Journal:  Expert Rev Mol Diagn       Date:  2016-12       Impact factor: 5.225

Review 9.  Clinical implications of using molecular diagnostics for ovarian cancers.

Authors:  E C Kohn; S Romano; J-M Lee
Journal:  Ann Oncol       Date:  2013-12       Impact factor: 32.976

Review 10.  [Serous ovarian tumors].

Authors:  J Diebold
Journal:  Pathologe       Date:  2014-07       Impact factor: 1.011

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