Literature DB >> 23643806

Prediction of Oncotype DX and TAILORx risk categories using histopathological and immunohistochemical markers by classification and regression tree (CART) analysis.

Helen Ingoldsby1, Mark Webber, Deirdre Wall, Carl Scarrott, John Newell, Grace Callagy.   

Abstract

Oncotype DX is an RT-PCR assay used to predict which patients with ER-positive node-negative (NN) disease will benefit from chemotherapy. Each patient is stratified into a risk category based on a recurrence score (RS) and the TAILORx trial is determining the benefit of chemotherapy for patients with mid-range RSs. We tested if Oncotype DX and TAILORx risk categories could be predicted by standard pathological features and protein markers corresponding to 10 genes in the assay (ER, PR, Ki67, HER2, BCL2, CD68, Aurora A kinase, survivin, cyclin B1 and BAG1) on 52 patients who enrolled on TAILORx. Immunohistochemistry for the protein markers was performed on whole tissue sections. Classification and regression tree (CART) analysis correctly classified 69% of cases into Oncotype DX risk categories based on the expression of PR, survivin and nuclear pleomorphism. All tumours with PR staining (Allred score ≥ 2) and marked nuclear pleomorphism were in the high-risk category. No case with PR <2, low survivin (≤ 15.5%) and nuclear pleomorphism <3 was high-risk. Similarly, 77% of cases were correctly classified into TAILORx categories based on nuclear pleomorphism, survivin, BAG1 and cyclin B1. Ki67 was the only variable that predicted the absolute RS with a cut-off for positivity of 15% (p = 0.003). In conclusion, CART revealed key predictors including proliferation markers, PR and nuclear pleomorphism that correctly classified over two thirds of ER-positive NN cancers into Oncotype DX and TAILORx risk categories. These variables could be used as an alternative to the RT-PCR assay to reduce the number of patients requiring Oncotype DX testing.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Breast cancer; Immunohistochemistry; Ki67; Oncotype DX; PR; TAILORx

Mesh:

Substances:

Year:  2013        PMID: 23643806     DOI: 10.1016/j.breast.2013.04.008

Source DB:  PubMed          Journal:  Breast        ISSN: 0960-9776            Impact factor:   4.380


  15 in total

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Authors:  Michele M Gage; Martin Rosman; W Charles Mylander; Erica Giblin; Hyun-Seok Kim; Leslie Cope; Christopher Umbricht; Antonio C Wolff; Lorraine Tafra
Journal:  Clin Breast Cancer       Date:  2015-04-23       Impact factor: 3.225

2.  A novel quantitative immunohistochemistry method for precise protein measurements directly in formalin-fixed, paraffin-embedded specimens: analytical performance measuring HER2.

Authors:  Kristian Jensen; Rikke Krusenstjerna-Hafstrøm; Jesper Lohse; Kenneth H Petersen; Helene Derand
Journal:  Mod Pathol       Date:  2016-10-21       Impact factor: 7.842

3.  Optimizing the Use of Gene Expression Profiling in Early-Stage Breast Cancer.

Authors:  Hyun-Seok Kim; Christopher B Umbricht; Peter B Illei; Ashley Cimino-Mathews; Soonweng Cho; Nivedita Chowdhury; Maria Cristina Figueroa-Magalhaes; Catherine Pesce; Stacie C Jeter; Charles Mylander; Martin Rosman; Lorraine Tafra; Bradley M Turner; David G Hicks; Tyler A Jensen; Dylan V Miller; Deborah K Armstrong; Roisin M Connolly; John H Fetting; Robert S Miller; Ben Ho Park; Vered Stearns; Kala Visvanathan; Antonio C Wolff; Leslie Cope
Journal:  J Clin Oncol       Date:  2016-10-31       Impact factor: 44.544

4.  Magee Equation 3 predicts pathologic response to neoadjuvant systemic chemotherapy in estrogen receptor positive, HER2 negative/equivocal breast tumors.

Authors:  Daniel J Farrugia; Alessandra Landmann; Li Zhu; Emilia J Diego; Ronald R Johnson; Marguerite Bonaventura; Atilla Soran; David J Dabbs; Beth Z Clark; Shannon L Puhalla; Rachel C Jankowitz; Adam M Brufsky; Barry C Lembersky; Gretchen M Ahrendt; Priscilla F McAuliffe; Rohit Bhargava
Journal:  Mod Pathol       Date:  2017-05-26       Impact factor: 7.842

5.  The 21-gene recurrence score in special histologic subtypes of breast cancer with favorable prognosis.

Authors:  Gulisa Turashvili; Edi Brogi; Monica Morrow; Clifford Hudis; Maura Dickler; Larry Norton; Hannah Y Wen
Journal:  Breast Cancer Res Treat       Date:  2017-06-03       Impact factor: 4.872

6.  Deep Learning-Based Pathology Image Analysis Enhances Magee Feature Correlation With Oncotype DX Breast Recurrence Score.

Authors:  Hongxiao Li; Jigang Wang; Zaibo Li; Melad Dababneh; Fusheng Wang; Peng Zhao; Geoffrey H Smith; George Teodoro; Meijie Li; Jun Kong; Xiaoxian Li
Journal:  Front Med (Lausanne)       Date:  2022-06-14

7.  Supervised machine learning model to predict oncotype DX risk category in patients over age 50.

Authors:  Kate R Pawloski; Mithat Gonen; Hannah Y Wen; Audree B Tadros; Donna Thompson; Kelly Abbate; Monica Morrow; Mahmoud El-Tamer
Journal:  Breast Cancer Res Treat       Date:  2021-11-09       Impact factor: 4.624

8.  Standardization for Ki-67 assessment in moderately differentiated breast cancer. A retrospective analysis of the SAKK 28/12 study.

Authors:  Zsuzsanna Varga; Estelle Cassoly; Qiyu Li; Christian Oehlschlegel; Coya Tapia; Hans Anton Lehr; Dirk Klingbiel; Beat Thürlimann; Thomas Ruhstaller
Journal:  PLoS One       Date:  2015-04-17       Impact factor: 3.240

9.  Confident gene activity prediction based on single histone modification H2BK5ac in human cell lines.

Authors:  Fereshteh Chitsazian; Mehdi Sadeghi; Elahe Elahi
Journal:  BMC Bioinformatics       Date:  2017-01-25       Impact factor: 3.169

10.  The Correlation of Magee EquationsTM and Oncotype DX® Recurrence Score From Core Needle Biopsy Tissues in Predicting Response to Neoadjuvant Chemotherapy in ER+ and HER2- Breast Cancer.

Authors:  Atilla Soran; Kaori Tane; Efe Sezgin; Rohit Bhargava
Journal:  Eur J Breast Health       Date:  2020-04-01
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