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Predicting oncotype DX recurrence scores using locally available immunohistochemical markers: experience in a district general hospital
  1. Katherine Humphris1,
  2. John Stephenson2,
  3. Vidya Kumaraswamy3
  1. 1 Department of Pathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
  2. 2 Allied Health Sciences, Sport and Exercise, University of Huddersfield, Huddersfield, Kirklees, UK
  3. 3 Department of Pathology, Calderdale and Huddersfield NHS Foundation Trust, Huddersfield, West Yorkshire, UK
  1. Correspondence to Dr John Stephenson, Allied Health Sciences, Sport & Exercise, University of Huddersfield, Huddersfield, Kirklees, UK; J.Stephenson{at}


Aims Oncotype DX testing is a reliable widely used gene assay to determine whether chemotherapy is of additional value in oestrogen receptor (ER) positive Human Epidermal Growth Factor receptor 2 (HER2) negative, node negative breast cancer, but the high cost of the test can be a barrier for optimal therapy guidance for a substantial proportion of eligible patients around the world. We aimed to determine whether the commonly available immunohistochemical markers Ki67 and progesterone receptor (PR) can predict Oncotype DX Recurrence Score (RS) scores in a district general hospital setting.

Methods The Oncotype DX RS scores from 58 tumours were regressed against corrected Ki67 values in a simple regression model, and against ER-derived and PR-derived indices and corrected Ki67 values in a multiple model. Model portability was assessed using leave-one-out cross-validation (LOOCV).

Results All terms in both regression models were significantly associated with RS scores at the 5% significance level (p<0.001 for all parameters). The multiple model was a better fit to the data (adjusted R2=0.784), and performed better under LOOCV (root mean square error=7.26), suggesting good predictive capability and model portability.

Conclusions Locally available, cheaper alternatives to multigene assays to determine therapy in ER positive HER2 negative patients is of benefit both from patient management and financial perspectives. A model has been derived with high capability to predict RS scores accurately from linear combinations of predictive biomarkers in a district general hospital setting, which should show good properties when applied to other samples.

  • Pathology (Surgical)
  • Chemotherapy/Cancer/Regional Perfusion
  • Statistics

Data availability statement

Data are available upon reasonable request.

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Data availability statement

Data are available upon reasonable request.

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  • Handling editor Runjan Chetty.

  • Contributors VK and JS agreed the methodology, drafted the manuscript, conducted the analysis, interpreted the results, fed back comments and read and approved the final manuscript. KH conducted the laboratory analysis. VK is the guarantor.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Ethical approval The study did not involve human participants. Ethical approval was not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.