BCL-2 expression aids in the immunohistochemical prediction of the Oncotype DX breast cancer recurrence score

Mark D. Zarella, Rebecca C. Heintzelman, Nikolay K. Popnikolov, Fernando U. Garcia

Research output: Contribution to journalArticlepeer-review

Abstract

Background: The development of molecular techniques to estimate the risk of breast cancer recurrence has been a significant addition to the suite of tools available to pathologists and breast oncologists. It has previously been shown that immunohistochemistry can provide a surrogate measure of tumor recurrence risk, effectively providing a less expensive and more rapid estimate of risk without the need for send-out. However, concordance between gene expression-based and immunohistochemistry-based approaches has been modest, making it difficult to determine when one approach can serve as an adequate substitute for the other. We investigated whether immunohistochemistry-based methods can be augmented to provide a useful therapeutic indicator of risk. Methods: We studied whether the Oncotype DX breast cancer recurrence score can be predicted from routinely acquired immunohistochemistry of breast tumor histology. We examined the effects of two modifications to conventional scoring measures based on ER, PR, Ki-67, and Her2 expression. First, we tested a mathematical transformation that produces a more diagnostic-relevant representation of the staining attributes of these markers. Second, we considered the expression of BCL-2, a complex involved in regulating apoptosis, as an additional prognostic marker. Results: We found that the mathematical transformation improved concordance rates over the conventional scoring model. By establishing a measure of prediction certainty, we discovered that the difference in concordance between methods was even greater among the most certain cases in the sample, demonstrating the utility of an accompanying measure of prediction certainty. Including BCL-2 expression in the scoring model increased the number of breast cancer cases in the cohort that were considered high certainty, effectively expanding the applicability of this technique to a greater proportion of patients. Conclusions: Our results demonstrate an improvement in concordance between immunohistochemistry-based and gene expression-based methods to predict breast cancer recurrence risk following two simple modifications to the conventional scoring model.

Original languageEnglish (US)
Article number14
JournalBMC Clinical Pathology
Volume18
Issue number1
DOIs
StatePublished - Dec 18 2018
Externally publishedYes

Keywords

  • Computer-assisted diagnosis
  • Digital pathology
  • Prognostic markers
  • Staining

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Histology

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