Abstract P3-04-10: Comparison between RNA-Seq and Affymetrix gene expression data
Authors: Fumagalli D , Haibe-Kains B , Michiels S , Brown DN , Gacquer D , Majjaj S , Salgado R , Larsimont D , Detour V , Piccart M , Sotiriou C , Desmedt C 
Published: August 2014

Abstract
Background: Microarrays have revolutionized breast cancer research by enabling gene expression comparisons on a transcriptome-wide scale. They have provided clinically useful information such as the development of the intrinsic classification as well as several gene prognostic signatures. Recently, with the advent of next generation sequencing technology, RNA-Seq has emerged as a promising technology for digital read-out of the transcriptome. Here, we will present a comparison of microarray and RNA-Seq data on a series of 58 breast cancer samples.
Methods: 58 tumors representing the different molecular subtypes [17 triple negative (ER−, PgR−, HER2−); 14 HER2 positive (HER2+); 17 luminal A (ER+, HER2−, histological grade 1), 10 luminal B (ER+, HER2−, histological grade 3)] were obtained from patients recruited between 2007 and 2011 at Jules Bordet Institute. Their RNA was profiled using the Affymetrix HG-U133 Plus 2.0 chips and sequenced with the ILLUMINA platform, producing ∼30 million 2*50 bp paired-end reads per sample. The reads alignment and expression quantification were performed using Tophat 2 and Cufflinks 2, respectively, on the basis of the Ensembl genome annotations hg19. Affymetrix microarray data were normalized using fRMA and probesets were selected using JetSet. A selection of 7 clinically relevant gene signatures was evaluated.
Results: About 16000 genes were retained for analysis. Comparison of Affymetrix and RNA-Seq data revealed that 50%, 25% and 5% of these genes had a Spearman correlation superior to 0.7, 0.82 and 0.92, respectively. The genes with the highest correlation coefficients between Affymetrix and RNA-Seq tended to have medium/high expression levels (median Affymetrix log2 expression of 7.5) consistent with the fact that microarrays have a limited dynamic range compared to RNA-Seq. We observed an excellent correlation between Affymetrix and RNA-Seq for the clinically relevant genes ER (0.97), PgR (0.95) and HER2 (0.90) respectively. We observed, however, a few discordances between ER and PgR quantification by immunohistochemistry and RNA-Seq/Affymetrix. We next measured the consistency of two molecular classifiers, SCMGENE (Haibe-Kains JNCI2012) and PAM50 (Parker JCO2009), between Affymetrix arrays and RNA-seq. A kappa coefficient of 0.93 (CI 0.85–1) and 0.79 (CI 0.66–0.91) was observed for SCMGENE and PAM50, respectively, with the majority of the discrepancies for PAM50 concerning the HER2 subtype. The first generation proliferation-based gene expression signatures showed an excellent correlation between Affymetrix and RNA-Seq: Genomic Grade Index (0.98), Mammaprint (0.98) and Oncotype (0.97). The immune and stroma gene signatures (Desmedt et al. 2008) showed a correlation of 0.97 and 0.79 respectively. The lower correlation for the stroma signature could partly be explained by the fact that RNA-Seq and Affymetrix are measuring different splice variants.
Conclusions: This is to our knowledge the first study to report a systematic comparison between RNA-Seq and Affymetrix HG-U133 Plus2.0 data in breast cancer for individual genes and several prognostic gene signatures. According to these results, RNA-Seq can reliably be used for the quantification of breast cancer transcriptome.
Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P3-04-10.