Intra-tumor heterogeneity in GEP test risk stratification

Purpose: To examine the effect of intra-tumor heterogeneity (ITH) on detection of genes within gene expression panels (GEPs), and the subsequent ability to predict prognostic risk. Experimental Design: Multiplexed barcoded RNA analysis was used to measure the expression of 141 genes from five GEPs (Oncotype Dx, MammaPrint, PAM50, EndoPredict, and Breast Cancer Index) in breast cancer tissue sections and tumor-rich cores from 71 estrogen receptor (ER) positive node-negative tumors, on which clinical Oncotype Dx testing was previously performed. If the tumor had foci of high Ki67 (n=26), low/negative PR (n=13), or both (n=5), additional cores were obtained. In total, 181 samples were processed. Oncotype Dx recurrence scores were calculated from NanoString nCounter gene expression data. Results: Hierarchical clustering using all GEP genes showed that majority (61/71) of tumor samples clustered by patient, indicating greater inter-patient heterogeneity (IPH) than ITH. We found a strikingly high correlation between Oncotype Dx recurrence scores obtained from whole sections versus tumor-rich cores (r=0.94). However, high Ki67 and low PR cores had slightly higher but not statistically significant recurrence scores. For 18/71 (25%) patients, scores were divergent between sections and cores and crossed the boundaries for low, intermediate and high risk. Conclusions: Our study indicates that in patients with highly heterogeneous tumors, GEP recurrence scores from a single core could under- or over-estimate prognostic risk. Hence, it may be a useful strategy to assess multiple samples (both representative and atypical cores) to fully account for the ITH-driven variation in risk prediction.

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