Estimating the OncotypeDX score: validation of an inexpensive estimation tool

Anne A. Eaton, Catherine E. Pesce, James O. Murphy, Michelle M. Stempel, Sujata M. Patil, Edi Brogi, Clifford A. Hudis, Mahmoud El-Tamer

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Background: OncotypeDX, a multi-gene expression assay, has been incorporated into clinical practice as a prognostic and predictive tool. However, its use in resource-constrained international healthcare systems is limited. Here we develop and validate a simplified model using clinicopathologic criteria to predict OncotypeDX score. Methods: Patients with estrogen receptor (ER) and/or progesterone receptor (PR)-positive and HER2-negative invasive ductal carcinoma for whom the OncotypeDX test was successfully performed between 09/2008 and 12/2011 were retrospectively identified. Tumor size, nuclear and histologic grade, lymphovascular invasion, and ER and PR status were extracted from pathology reports. Data were split into a training dataset comprising women tested 09/2008–04/2011, and a validation dataset comprising women tested 04/2011–12/2011. Using the training dataset, linear regression analysis was used to identify factors associated with OncotypeDX score, and to create a simplified risk score and identify risk cutoffs. Results: Estrogen and progesterone receptors, tumor size, nuclear and histologic grades, and lymphovascular involvement were independently associated with OncotypeDX. The full model explained 39% of the variation in the test data, and the simplified risk score and cutoffs assigned 57% of patients in the test data to the correct risk category (OncotypeDX score <18, 18–30, >30). 41% of patients were predicted to have OncotypeDX score <18, of these 83, 16, and 2% had true scores of <18, 18–30, and >30, respectively. Conclusions: Awaiting an inexpensive test that is prognostic and predictive, our simplified tool allows clinicians to identify a fairly large group of patients (41%) with very low chance of having high-risk disease (2%).

Original languageEnglish (US)
Pages (from-to)435-441
Number of pages7
JournalBreast Cancer Research and Treatment
Volume161
Issue number3
DOIs
StatePublished - Feb 1 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016, Springer Science+Business Media New York.

Keywords

  • Breast cancer
  • OncotypeDX
  • Risk prediction

Fingerprint

Dive into the research topics of 'Estimating the OncotypeDX score: validation of an inexpensive estimation tool'. Together they form a unique fingerprint.

Cite this