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A clinical calculator to predict disease outcomes in women with triple-negative breast cancer

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Abstract

Purpose

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, characterized by substantial risks of early disease recurrence and mortality. We constructed and validated clinical calculators for predicting recurrence-free survival (RFS) and overall survival (OS) for TNBC.

Methods

Data from 605 women with centrally confirmed TNBC who underwent primary breast cancer surgery at Mayo Clinic during 1985–2012 were used to train risk models. Variables included age, menopausal status, tumor size, nodal status, Nottingham grade, surgery type, adjuvant radiation therapy, adjuvant chemotherapy, Ki67, stromal tumor-infiltrating lymphocytes (sTIL) score, and neutrophil-to-lymphocyte ratio (NLR). Final models were internally validated for calibration and discrimination using ten-fold cross-validation and compared with their base-model counterparts which include only tumor size and nodal status. Independent external validation was performed using data from 478 patients diagnosed with stage II/III invasive TNBC during 1986–1992 in the British Columbia Breast Cancer Outcomes Unit database.

Results

Final RFS and OS models were well calibrated and associated with C-indices of 0.72 and 0.73, as compared with 0.64 and 0.62 of the base models (p < 0.001). In external validation, the discriminant ability of the final models was comparable to the base models (C-index: 0.59–0.61). The RFS model demonstrated greater accuracy than the base model both overall and within patient subgroups, but the advantages of the OS model were less profound.

Conclusions

This TNBC clinical calculator can be used to predict patient outcomes and may aid physician’s communication with TNBC patients regarding their long-term disease outlook and planning treatment strategies.

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Funding

Research reported in this article was supported by the National Cancer Institute of the National Institutes of Health under Award Number P50 CA116201 (Mayo Clinic Breast Cancer Specialized Program of Research Excellence) and the Breast Cancer Research Foundation (BCRF).

Author information

Authors and Affiliations

  1. Department of Public Health Sciences, University of Chicago, Chicago, IL, USA

    Mei-Yin C. Polley

  2. Department of Oncology, Mayo Clinic, Rochester, MN, USA

    Roberto A. Leon-Ferre, Minetta C. Liu, James N. Ingle & Matthew P. Goetz

  3. Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada

    Samuel Leung, Angela Cheng, Dongxia Gao & Torsten O. Nielsen

  4. Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA

    Jason Sinnwell, Heshan Liu, David W. Hillman, Abraham Eyman-Casey & Krishna Kalari

  5. Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

    Judith A. Gilbert, Vivian Negron, Fergus Couch, Jodi M. Carter & Daniel W. Visscher

  6. Department of Surgery, Mayo Clinic, Rochester, MN, USA

    Judy C. Boughey

Authors
  1. Mei-Yin C. Polley
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  2. Roberto A. Leon-Ferre
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  3. Samuel Leung
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Corresponding author

Correspondence to Mei-Yin C. Polley.

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Conflict of interest

Dr. Torsten O. Nielsen holds a proprietary interest in the Prosigna breast cancer assay through Bioclassifier LLC, licensed to Veracyte, Inc. Dr. Goetz reports personal fees from Genomic Health, consulting fees from Lilly, Biovica, Novartis, Sermonix, Context Pharm, Pfizer, Biotheranostics, and grants from Pfizer, Lilly, and Sermonix. Other authors declare that they have no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

The need for obtaining informed consent was waived by the institutional review board, given that this study was retrospective and non-interventional.

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Polley, MY.C., Leon-Ferre, R.A., Leung, S. et al. A clinical calculator to predict disease outcomes in women with triple-negative breast cancer. Breast Cancer Res Treat 185, 557–566 (2021). https://doi.org/10.1007/s10549-020-06030-5

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