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Use of a supervised machine learning model to predict Oncotype DX risk category in node-positive patients older than 50 years of age

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Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Purpose

The use of the Oncotype DX recurrence score (RS) to predict chemotherapy benefit in patients with hormone receptor-positive/HER2 negative (HR+/HER2-) breast cancer has recently expanded to include postmenopausal patients with N1 disease. RS availability is limited in resource-poor settings, however, prompting the development of statistical models that predict RS using clinicopathologic features. We sought to assess the performance of our supervised machine learning model in a cohort of patients > 50 years of age with N1 disease.

Methods

We identified patients > 50 years of age with pT1-2N1 HR+/HER2- breast cancer and applied the statistical model previously developed in a node-negative cohort, which uses age, pathologic tumor size, histology, progesterone receptor expression, lymphovascular invasion, and tumor grade to predict RS. We measured the model’s ability to predict RS risk category (low: RS ≤ 25; high: RS > 25).

Results

Our cohort included 401 patients, 60.6% of whom had macrometastases, with a median of 1 positive node. The majority of patients had a low-risk observed RS (85.8%). For predicting RS category, the model had specificity of 97.3%, sensitivity of 31.8%, a negative predictive value of 87.9%, and a positive predictive value of 70.0%.

Conclusion

Our model, developed in a cohort of node-negative patients, was highly specific for identifying cN1 patients > 50 years of age with a low RS who could safely avoid chemotherapy. The use of this model for identifying patients in whom genomic testing is unnecessary would help decrease the cost burden in resource-poor settings as reliance on RS for adjuvant treatment recommendations increases.

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Data availability

The datasets generated and/or analyzed during the study are available from the corresponding author on reasonable request.

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Funding

This work was supported in part by NIH/NCI Cancer Center Support Grant No. P30CA008748 to Memorial Sloan Kettering Cancer Center. Editorial support in preparing this manuscript was provided by Hannah Rice, BA, ELS.

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Authors and Affiliations

  1. Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA

    Austin D. Williams, Kate R. Pawloski, Monica Morrow & Mahmoud El-Tamer

  2. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA

    Hannah Y. Wen & Donna Thompson

  3. Biostatistics Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA

    Varadan Sevilimedu

Authors
  1. Austin D. Williams
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  2. Kate R. Pawloski
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Corresponding author

Correspondence to Mahmoud El-Tamer.

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Conflict of interest

Dr. Monica Morrow has received honoraria from Exact Sciences and Roche. All other authors have no conflict of interest disclosures to report.

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This study was approved by the Memorial Sloan Kettering Cancer Center Institutional Review Board.

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Informed consent for this study was waived by the Memorial Sloan Kettering Cancer Center Institutional Review Board.

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Williams, A.D., Pawloski, K.R., Wen, H.Y. et al. Use of a supervised machine learning model to predict Oncotype DX risk category in node-positive patients older than 50 years of age. Breast Cancer Res Treat 196, 565–570 (2022). https://doi.org/10.1007/s10549-022-06763-5

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