Related Experiment Video
Updated: Aug 1, 2025
11:34
Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers
Published on: December 5, 2017
12.7K
Prediction of Oncotype DX Recurrence Score Using Clinicopathological Variables in Estrogen Receptor-Positive/Human
Min Chong Kim1, Sun Young Kwon2, Jung Eun Choi3
1Department of Pathology, Yeungnam University College of Medicine, Daegu, Korea.
Journal of Breast Cancer
|April 25, 2023
Summary
A new clinicopathological prediction (CPP) model using progesterone receptor, Ki-67, and nuclear grade accurately identifies high-risk breast cancer patients for Oncotype DX testing.
Area of Science:
- Oncology
- Genomics
- Biomarker Discovery
Background:
- The Oncotype DX (ODX) assay is crucial for guiding breast cancer treatment decisions in Korean clinical practice.
- Accurate risk stratification is essential for optimizing patient management and avoiding unnecessary treatments.
Purpose of the Study:
- To develop and validate a clinicopathological prediction (CPP) model for Oncotype DX recurrence scores (RSs).
- To identify key clinicopathological variables that predict high-risk ODX RS in early-stage breast cancer.
Main Methods:
- A cohort of 297 patients with ER+, HER2- breast cancer were analyzed.
- Logistic regression models were used to identify predictors of high-risk ODX RS (RS > 25).
- A CPP model was constructed using significant clinicopathological variables and validated externally.
Main Results:
- Progesterone receptor (PR) negativity, high Ki-67 index, and nuclear grade (NG) 3 were independent predictors of high-risk ODX RS.
- The developed CPP model demonstrated high discriminatory ability with a C-index of 0.915 in the primary cohort and 0.926 in the validation cohort.
Conclusions:
- A novel CPP model incorporating PR, Ki-67, and NG effectively predicts high-risk ODX RS.
- This model can assist clinicians in selecting appropriate breast cancer patients who would benefit from Oncotype DX testing.
