Selecting Patients for Oncotype DX Testing Using Standard Clinicopathologic Information. - PubMed - NCBI

Selecting Patients for Oncotype DX Testing Using Standard Clinicopathologic Information. - PubMed - NCBI

Clin Breast Cancer. 2019 Aug 22. pii: S1526-8209(19)30650-0. doi: 10.1016/j.clbc.2019.07.006. [Epub ahead of print]

Selecting Patients for Oncotype DX Testing Using Standard Clinicopathologic Information.

Robertson SJ1, Pond GR2, Hilton J3, Petkiewicz SL1, Ayroud Y1, Kos Z1, Gravel DH1, Stober C4, Vandermeer L4, Arnaout A5, Clemons M6.

Author information

1

Eastern Ontario Regional Laboratory, Department of Pathology and Laboratory Medicine, The Ottawa Hospital and the University of Ottawa, Ottawa, Ontario, Canada.

2

Department of Oncology, McMaster University, Hamilton, Ontario, Canada.

3

Cancer Research Group, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Division of Medical Oncology, Department of Medicine, The Ottawa Hospital Cancer Centre and the University of Ottawa, Ottawa, Ontario, Canada.

4

Cancer Research Group, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.

5

Cancer Research Group, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Division of Surgical Oncology, Department of Surgery, The Ottawa Hospital and the University of Ottawa, Ottawa, Ontario, Canada.

6

Cancer Research Group, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Division of Medical Oncology, Department of Medicine, The Ottawa Hospital Cancer Centre and the University of Ottawa, Ottawa, Ontario, Canada; Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada. Electronic address: mclemons@toh.ca.

Abstract

INTRODUCTION:

Indiscriminate ordering of Oncotype DX (ODX) is expensive and of poor value to patients, physicians, and health care providers. The 3 Magee equations, Gage Algorithm, and University of Tennessee predictive algorithm all use standard clinicopathologic data to provide surrogate ODX scores. In this hypothesis-generating study, we evaluated whether these prognostic scores could be used to identify patients unlikely to benefit from additional ODX testing.

PATIENTS AND METHODS:

Retrospective data was collected from 302 patients with invasive ductal breast cancer and available ODX scores. Additional data was available for: Magee equations 1 (212 patients), 2 (299 patients), 3 (212 patients), Gage Algorithm (299 patients), and University of Tennessee predictive algorithm (286 patients). ODX scores were banded according to the TAILORx results.

RESULTS:

Correlation with ODX scores was between 0.7 and 0.8 (Gage), 0.8 and 0.9 (Magee 2, University of Tennessee predictive algorithm), and > 0.9 (Magee 1 and 3). Magee 3 was the most robust and is proposed as a screening tool: for patients aged ≤ 50 years, ODX testing would be not required if the Magee 3 score was < 14 or ≥ 20; for those aged > 50 years, ODX would not be required if the Magee 3 score was < 18 or ≥ 26. Using these cut-offs, 110 (51.9%) of 212 patients would be deemed as not requiring ODX testing, and 109 (99.1%) of110 patients would be appropriately managed.

CONCLUSIONS:

Use of all formulae, and the Magee 3 equation in particular, are proposed as possible screening tools for ODX testing, resulting in significantly reduced frequency of ODX testing. This requires validation in other populations.

Copyright © 2019 Elsevier Inc. All rights reserved.

KEYWORDS:

Adjuvant; Algorithms; Breast cancer; Pathology; Recurrence score

PMID:

 

31551182

 

DOI:

 

10.1016/j.clbc.2019.07.006