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2018 ASCO Annual Meeting!


Session: Breast Cancer—Local/Regional/Adjuvant

Type: Poster Session

Time: Saturday June 2, 8:00 AM to 11:30 AM

Location: Hall A

Predicting Oncotype DX scores using clinicopathologic features: A report from the National Cancer Database.

Sub-category:
Adjuvant Therapy

Category:
Breast Cancer—Local/Regional/Adjuvant

Meeting:
2018 ASCO Annual Meeting

Abstract No:
551

Poster Board Number:
Poster Session (Board #43)

Citation:
J Clin Oncol 36, 2018 (suppl; abstr 551)

Author(s): Catherine Pesce, Kristine Kuchta, Beyza Erdogan, Chihsiung Wang, Katharine Yao, Mahmoud El-Tamer; Department of Surgery, NorthShore University Health System, Evanston, IL; NorthShore University Health System, Evanston, IL; NorthShore University HealthSystem, Evanston, IL; Department of Surgery, NorthShore University HealthSystem, Evanston, IL; Memorial Sloan Kettering Cancer Center, New York, NY

Abstract Disclosures

Abstract:

Background: The Oncotype DX recurrence score is used to predict the benefits of chemotherapy added to adjuvant hormone therapy in ER positive early-stage breast cancer. While its use has been validated and cost effectiveness has been established, its expense remains a concern in some health care systems and communities. Using clinical and pathologic features and the National Cancer Data Base (NCDB) we aim to predict patients with low or high Oncotype DX scores. Methods: From 2010-2014, 78,663 breast cancer patients with Oncotype DX scores were selected from the NCDB. Seven clinical and pathologic variables including age, ER, PR, histologic subtype, lymphovascular invasion (LVI), grade, and tumor size were used to predict high-risk ( > 30) or low-risk ( < 18) Oncotype DX scores using logistic regression. Data were split into training (70%) and testing (30%) sets for external validation. The predictive accuracy of the regression model was assessed using a Receiver Operator Characteristic (ROC) analysis. Model fit was analyzed by plotting the predicted probabilities against the actual probabilities. Nomograms were created for visualization of the high-risk and low-risk models using bootstrap estimation method of the model coefficients. Results: Estrogen receptor status, progesterone receptor status, and grade were the strongest predictors of both low-risk and high-risk Oncotype DX scores, followed by age, histology, tumor size, and LVI, yielding AUC of 0.70 for the low-risk model and 0.86 for the high-risk model. Conclusions: We have developed a model that can predict high-risk Oncotype DX scores with very good reliability. Such a tool may be useful in health care systems with limited resources. Model Coefficients of Strongest Predictors of High-Risk Oncotype DX Score

FactorsCoefficients
ERnegative2.977
PRnegative2.095
Gradewell differentiated0.000
moderately differentiated1.356
poorly differentiated3.374
Tumor size< 10mm0.000
10-20mm0.264
20-30mm0.584
30mm+0.78

 
Other Abstracts in this Sub-Category:

 

1. TAILORx: Phase III trial of chemoendocrine therapy versus endocrine therapy alone in hormone receptor-positive, HER2-negative, node-negative breast cancer and an intermediate prognosis 21-gene recurrence score.

Meeting: 2018 ASCO Annual Meeting Abstract No: LBA1 First Author: Joseph A. Sparano
Category: Breast Cancer—Local/Regional/Adjuvant - Adjuvant Therapy

 

2. Adjuvant denosumab in early breast cancer: Disease-free survival analysis of 3,425 postmenopausal patients in the ABCSG-18 trial.

Meeting: 2018 ASCO Annual Meeting Abstract No: 500 First Author: Michael Gnant
Category: Breast Cancer—Local/Regional/Adjuvant - Adjuvant Therapy

 

3. Adjuvant denosumab in early breast cancer: First results from the international multicenter randomized phase III placebo controlled D-CARE study.

Meeting: 2018 ASCO Annual Meeting Abstract No: 501 First Author: Robert E. Coleman
Category: Breast Cancer—Local/Regional/Adjuvant - Adjuvant Therapy

 

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