PAM50 breast cancer intrinsic classifier: Clinical validation of a multianalyte laboratory developed test.

10597 Background: Invasive breast cancers can be classified by gene expression profiling into 4 major biologic subtypes referred to as Luminal A, Luminal B, HER2-enriched, and Basal-like. Subtypes have been identified using several technologies including microarray, immunohistochemistry, and RT-qPCR. In 2009, a 50-gene signature (PAM50) was proposed to standardize breast cancer subtyping. We present the clinical validation of this signature by RT-qPCR. Methods: RNA was extracted from 171 breast samples (155 invasive cancers and 16 “normal” breast tissues) procured in formalin-fixed paraffin-embedded tissue blocks. The RNA was reverse transcribed and cDNA was amplified by PCR using the LC480 instrument (Roche®). A statistical analysis (SigClust) was applied to hierarchical clustering of RT-qPCR data to identify prototypes for each subtype. Robustness of the PAM50 subtype assignment was assessed using a 10-fold cross validation. Subtype centroids were used to develop a single-sample subtype predictor by Spearman rank correlation. There was additional evaluation of PCR efficiency, limits of detection, limits of quantification, reproducibility, and interference. Accuracy was assessed by comparing the PAM50 clinical subtype predictions to those previously published from microarray data. Results: Within platform cross validation of the clinical subtype predictor showed 91.6% concordance. There was 100% reproducibility in subtype predictions across 46 runs testing different subtypes. Subtype predictions across platforms showed 88.1% concordance. Dilution experiments, introducing “normal” breast tissue RNA into breast cancer RNA, showed a systematic switch towards the “normal” signature with Luminal A and Luminal B being most susceptible. Conclusions: The PAM50 Breast Cancer Intrinsic Classifier is highly reproducible within and across platforms. The clinical test has utility in the management of ER+ and ER- invasive breast cancers of all stages. It provides a necessary tool for identifying differences in tumor biology that are important for guiding patient care.