Intratumor heterogeneity affects gene expression profile test prognostic risk stratification in early breast cancer

Rekha Gyanchandani, Yan Lin, Hui Min Lin, Kristine Cooper, Daniel P. Normolle, Adam Brufsky, Michael Fastuca, Whitney Crosson, Steffi Oesterreich, Nancy E. Davidson, Rohit Bhargava, David J. Dabbs, Adrian V. Lee

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: To examine the effect of intratumor 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 progesterone receptor (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 of 71) of tumor samples clustered by patient, indicating greater interpatient 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 of 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 overestimate 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.

Original languageEnglish (US)
Pages (from-to)5362-5369
Number of pages8
JournalClinical Cancer Research
Volume22
Issue number21
DOIs
StatePublished - Nov 1 2016

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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