Molecular Classification of Breast Cancer: Limitations and Potential

Abstract Learning Objectives After completing this course, the reader will be able to: Discuss the technical variables that affect accuracy of estrogen receptor immunohistochemistry.Describe emerging methods to quantify estrogen receptor expression and predict the prognosis of estrogen receptor-positive patients.Discuss limitations of current gene expression-based molecular classification of breast cancer.Explain the conceptual differences between unsupervised molecular class discovery methods and supervised clinical outcome prediction models such as multigene prognostic signatures.Interpret results of DNA microarray literature as they relate to diagnosis and prognosis of breast cancer. Access and take the CME test online and receive 1 AMA PRA Category 1 Credit™ at CME.TheOncologist.com Reverse transcription polymerase chain reaction and DNA microarrays are increasingly used in the clinic and in clinical research as prognostic or predictive tests. Results from these tests led to novel risk stratification methods and to new molecular classification of breast cancer. Some of these tools already complement existing diagnostic tests and can aid medical decision making in some situations. Better understanding of the molecular classes of breast cancer, independent of their prognostic and predictive values, may also lead to new biological insights and eventually to better therapies that are directed toward particular molecular subsets. However, there is substantially less experience with these emerging technologies than with the more established methods, the accuracy of which is often overestimated. This review discusses some of the limitations and strengths of current gene expression-based molecular classification of breast cancer. To provide context for this discussion, we also briefly examine the performance of estrogen receptor immunohistochemistry, which represents an essential part of the routine diagnostic workup for all breast cancer patients.