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Transfer of clinically relevant gene expression signatures in breast cancer: from Affymetrix microarray to Illumina RNA-Sequencing technology.

Fumagalli D, Blanchet-Cohen A, Brown D, Desmedt C, Gacquer D, Michiels S, Rothé F, Majjaj S, Salgado R, Larsimont D, Ignatiadis M, Maetens M, Piccart M, Detours V, Sotiriou C, Haibe-Kains B - BMC Genomics (2014)

Bottom Line: Gene-wise comparison of microarray and RNA-Seq data revealed that 52% had a Spearman's correlation coefficient greater than 0.7 with highly correlated genes displaying significantly higher expression levels.We found excellent correlation between microarray and RNA-Seq for the estrogen receptor (ER; rs = 0.973; 95% CI: 0.971-0.975), progesterone receptor (PgR; rs = 0.95; 0.947-0.954), and human epidermal growth factor receptor 2 (HER2; rs = 0.918; 0.912-0.923), while a few discordances between ER and PgR quantified by immunohistochemistry and RNA-Seq/microarray were observed.According to our results, the vast majority of single gene biomarkers and well-established GES can be reliably evaluated using the RNA-Seq technology.

View Article: PubMed Central - PubMed

Affiliation: Breast Cancer Translational Research Laboratory (BCTL), Institut Jules Bordet, Brussels, Belgium. christos.sotiriou@bordet.be.

ABSTRACT

Background: Microarrays have revolutionized breast cancer (BC) research by enabling studies of gene expression on a transcriptome-wide scale. Recently, RNA-Sequencing (RNA-Seq) has emerged as an alternative for precise readouts of the transcriptome. To date, no study has compared the ability of the two technologies to quantify clinically relevant individual genes and microarray-derived gene expression signatures (GES) in a set of BC samples encompassing the known molecular BC's subtypes. To accomplish this, the RNA from 57 BCs representing the four main molecular subtypes (triple negative, HER2 positive, luminal A, luminal B), was profiled with Affymetrix HG-U133 Plus 2.0 chips and sequenced using the Illumina HiSeq 2000 platform. The correlations of three clinically relevant BC genes, six molecular subtype classifiers, and a selection of 21 GES were evaluated.

Results: 16,097 genes common to the two platforms were retained for downstream analysis. Gene-wise comparison of microarray and RNA-Seq data revealed that 52% had a Spearman's correlation coefficient greater than 0.7 with highly correlated genes displaying significantly higher expression levels. We found excellent correlation between microarray and RNA-Seq for the estrogen receptor (ER; rs = 0.973; 95% CI: 0.971-0.975), progesterone receptor (PgR; rs = 0.95; 0.947-0.954), and human epidermal growth factor receptor 2 (HER2; rs = 0.918; 0.912-0.923), while a few discordances between ER and PgR quantified by immunohistochemistry and RNA-Seq/microarray were observed. All the subtype classifiers evaluated agreed well (Cohen's kappa coefficients >0.8) and all the proliferation-based GES showed excellent Spearman correlations between microarray and RNA-Seq (all rs >0.965). Immune-, stroma- and pathway-based GES showed a lower correlation relative to prognostic signatures (all rs >0.6).

Conclusions: To our knowledge, this is the first study to report a systematic comparison of RNA-Seq to microarray for the evaluation of single genes and GES clinically relevant to BC. According to our results, the vast majority of single gene biomarkers and well-established GES can be reliably evaluated using the RNA-Seq technology.

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Related in: MedlinePlus

Gene expression correlation between Affymetrix microarray and Illumina RNA-Seq platforms. A: Expression correlation of the 16,097 genes measured both on Affymetrix microarray and Illumina RNA-Seq platforms after selecting the best Affymetrix probeset using jetset. B: and C: Box plots showing median level of gene expression for both Affymetrix and RNA-Seq for the genes with low (<0.7) and high (≥0.7) correlation. Genes highly correlated between the two platforms showed higher levels of expression than those with low correlation.
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Fig1: Gene expression correlation between Affymetrix microarray and Illumina RNA-Seq platforms. A: Expression correlation of the 16,097 genes measured both on Affymetrix microarray and Illumina RNA-Seq platforms after selecting the best Affymetrix probeset using jetset. B: and C: Box plots showing median level of gene expression for both Affymetrix and RNA-Seq for the genes with low (<0.7) and high (≥0.7) correlation. Genes highly correlated between the two platforms showed higher levels of expression than those with low correlation.

Mentions: A subset of 16,097 genes were defined as common to the two platforms and retained for downstream analysis. Gene identifiers did not perfectly overlap due to differences in the annotation systems: jetset matched the Affymetrix probesets to the NCBI RefSeq human cDNA database, while the RNA-Seq analysis pipeline used Ensembl gene annotations (see Methods for more detail). When comparing the expression levels of the genes retained after selection of the best Affymetrix probeset, we found that although the scale of expression values differs due to different technology and normalization procedures, their rank is well conserved with 52%, 34%, and 11% of these genes having Spearman’s rank-based correlation greater than 0.7, 0.8, and 0.9, respectively (Figure 1A). The Spearman correlation coefficient for each evaluated gene is reported in Additional file 1: Table S2.Figure 1


Transfer of clinically relevant gene expression signatures in breast cancer: from Affymetrix microarray to Illumina RNA-Sequencing technology.

Fumagalli D, Blanchet-Cohen A, Brown D, Desmedt C, Gacquer D, Michiels S, Rothé F, Majjaj S, Salgado R, Larsimont D, Ignatiadis M, Maetens M, Piccart M, Detours V, Sotiriou C, Haibe-Kains B - BMC Genomics (2014)

Gene expression correlation between Affymetrix microarray and Illumina RNA-Seq platforms. A: Expression correlation of the 16,097 genes measured both on Affymetrix microarray and Illumina RNA-Seq platforms after selecting the best Affymetrix probeset using jetset. B: and C: Box plots showing median level of gene expression for both Affymetrix and RNA-Seq for the genes with low (<0.7) and high (≥0.7) correlation. Genes highly correlated between the two platforms showed higher levels of expression than those with low correlation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4289354&req=5

Fig1: Gene expression correlation between Affymetrix microarray and Illumina RNA-Seq platforms. A: Expression correlation of the 16,097 genes measured both on Affymetrix microarray and Illumina RNA-Seq platforms after selecting the best Affymetrix probeset using jetset. B: and C: Box plots showing median level of gene expression for both Affymetrix and RNA-Seq for the genes with low (<0.7) and high (≥0.7) correlation. Genes highly correlated between the two platforms showed higher levels of expression than those with low correlation.
Mentions: A subset of 16,097 genes were defined as common to the two platforms and retained for downstream analysis. Gene identifiers did not perfectly overlap due to differences in the annotation systems: jetset matched the Affymetrix probesets to the NCBI RefSeq human cDNA database, while the RNA-Seq analysis pipeline used Ensembl gene annotations (see Methods for more detail). When comparing the expression levels of the genes retained after selection of the best Affymetrix probeset, we found that although the scale of expression values differs due to different technology and normalization procedures, their rank is well conserved with 52%, 34%, and 11% of these genes having Spearman’s rank-based correlation greater than 0.7, 0.8, and 0.9, respectively (Figure 1A). The Spearman correlation coefficient for each evaluated gene is reported in Additional file 1: Table S2.Figure 1

Bottom Line: Gene-wise comparison of microarray and RNA-Seq data revealed that 52% had a Spearman's correlation coefficient greater than 0.7 with highly correlated genes displaying significantly higher expression levels.We found excellent correlation between microarray and RNA-Seq for the estrogen receptor (ER; rs = 0.973; 95% CI: 0.971-0.975), progesterone receptor (PgR; rs = 0.95; 0.947-0.954), and human epidermal growth factor receptor 2 (HER2; rs = 0.918; 0.912-0.923), while a few discordances between ER and PgR quantified by immunohistochemistry and RNA-Seq/microarray were observed.According to our results, the vast majority of single gene biomarkers and well-established GES can be reliably evaluated using the RNA-Seq technology.

View Article: PubMed Central - PubMed

Affiliation: Breast Cancer Translational Research Laboratory (BCTL), Institut Jules Bordet, Brussels, Belgium. christos.sotiriou@bordet.be.

ABSTRACT

Background: Microarrays have revolutionized breast cancer (BC) research by enabling studies of gene expression on a transcriptome-wide scale. Recently, RNA-Sequencing (RNA-Seq) has emerged as an alternative for precise readouts of the transcriptome. To date, no study has compared the ability of the two technologies to quantify clinically relevant individual genes and microarray-derived gene expression signatures (GES) in a set of BC samples encompassing the known molecular BC's subtypes. To accomplish this, the RNA from 57 BCs representing the four main molecular subtypes (triple negative, HER2 positive, luminal A, luminal B), was profiled with Affymetrix HG-U133 Plus 2.0 chips and sequenced using the Illumina HiSeq 2000 platform. The correlations of three clinically relevant BC genes, six molecular subtype classifiers, and a selection of 21 GES were evaluated.

Results: 16,097 genes common to the two platforms were retained for downstream analysis. Gene-wise comparison of microarray and RNA-Seq data revealed that 52% had a Spearman's correlation coefficient greater than 0.7 with highly correlated genes displaying significantly higher expression levels. We found excellent correlation between microarray and RNA-Seq for the estrogen receptor (ER; rs = 0.973; 95% CI: 0.971-0.975), progesterone receptor (PgR; rs = 0.95; 0.947-0.954), and human epidermal growth factor receptor 2 (HER2; rs = 0.918; 0.912-0.923), while a few discordances between ER and PgR quantified by immunohistochemistry and RNA-Seq/microarray were observed. All the subtype classifiers evaluated agreed well (Cohen's kappa coefficients >0.8) and all the proliferation-based GES showed excellent Spearman correlations between microarray and RNA-Seq (all rs >0.965). Immune-, stroma- and pathway-based GES showed a lower correlation relative to prognostic signatures (all rs >0.6).

Conclusions: To our knowledge, this is the first study to report a systematic comparison of RNA-Seq to microarray for the evaluation of single genes and GES clinically relevant to BC. According to our results, the vast majority of single gene biomarkers and well-established GES can be reliably evaluated using the RNA-Seq technology.

Show MeSH
Related in: MedlinePlus