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Revealing determinant factors for early breast cancer recurrence by decision tree

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Abstract

Early breast cancer recurrence is indicative of poor response to adjuvant therapy and poses threats to patients’ lives. Most existing prediction models for breast cancer recurrence are regression-based models and difficult to interpret. We apply a Decision Tree algorithm to the clinical information of a cohort of non-metastatic invasive breast cancer patients, to establish a classifier that categorizes patients based on whether they develop early recurrence and on similarities of their clinical and pathological diagnoses. The classifier predicts for whether a patient developed early disease recurrence; and is estimated to be about 70% accurate. For an independent validation cohort of 65 patients, the classifier predicts correctly for 55 patients. The classifier also groups patients based on intrinsic properties of their diseases; and for each subgroup lists the disease characteristics in a hierarchal order, according to their relevance to early relapse. Overall, it identifies pathological nodal stage, percentage of intra-tumor stroma and components of TGFβ-Smad signaling pathway as highly relevant factors for early breast cancer recurrence. Since most of the disease characteristics used by this classifier are results of standardized tests, routinely collected during breast cancer diagnosis, the classifier can easily be adopted in various research and clinical settings.

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Acknowledgements

We would like to thank Drs. C. C. Engels, J. W. T. Dekker and E. M. de Kruijf for conducting immunohistochemistry staining, evaluating stroma percentage and recording original data; and Drs. A. Dibrov and Catalin Mihalcioiu for valuable discussions. J. Guo is supported by a Traineeship from the Breast Cancer Research Program of Congressionally Directed Medical Research Program (CDMRP). B. C. M. Fung is a Canada Research Chair in Data Mining for Cybersecurity. J.-J. Lebrun is a Sir William Dawson Research Chair of McGill University. This work was supported in part by grants from the Canadian Institutes for Health Research (CIHR) (fund codes 230670 and 233716 to J.-J. Lebrun), the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants (fund code 356065-2013 to B. C. M. Fung), Canada Research Chairs Program (fund code 950-230623 to B. C. M. Fung), and Zayed University Research Incentive Fund and Research Cluster Award (fund codes R15048 and R16083 to F. Iqbal and B. C. M. Fung).

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Author notes

  1. Jimin Guo

    Present address: Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

Authors and Affiliations

  1. Division of Medical Oncology, McGill University Health Center, Montreal, QC, Canada

    Jimin Guo & Jean-Jacques Lebrun

  2. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Jimin Guo

  3. School of Information Studies, McGill University, Montreal, QC, Canada

    Benjamin C. M. Fung

  4. Zayed University, Abu Dhabi, United Arab Emirates

    Farkhund Iqbal

  5. Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands

    Peter J. K. Kuppen, Rob A. E. M. Tollenaar & Wilma E. Mesker

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  1. Jimin Guo
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  2. Benjamin C. M. Fung
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  3. Farkhund Iqbal
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  4. Peter J. K. Kuppen
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  7. Jean-Jacques Lebrun
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Contributions

J. Guo, B. C. M. Fung and J.-J. Lebrun designed the study, analyzed and interpreted the results. F. Iqbal participated in interpreting the results. P. J. K. Kuppen, R. A. E. M. Tollenaar and W. E. Mesker collected patient samples and designed the tumor tissue microarrays.

Corresponding authors

Correspondence to Benjamin C. M. Fung or Jean-Jacques Lebrun.

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Guo, J., Fung, B.C.M., Iqbal, F. et al. Revealing determinant factors for early breast cancer recurrence by decision tree. Inf Syst Front 19, 1233–1241 (2017). https://doi.org/10.1007/s10796-017-9764-0

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  • DOI: https://doi.org/10.1007/s10796-017-9764-0

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