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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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Correlation values for the evaluated subtype classifiers and gene expression signatures. A: Cohen’s Kappa coefficients for subtype classifiers (orange: SCMs; purple: SSPs). B: Spearman correlation values for prognostic (orange), immune (green), stroma (blue) and pathway (purple) signature scores as computed using Affymetrix microarray and Illumina RNA-Seq platforms.
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Fig3: Correlation values for the evaluated subtype classifiers and gene expression signatures. A: Cohen’s Kappa coefficients for subtype classifiers (orange: SCMs; purple: SSPs). B: Spearman correlation values for prognostic (orange), immune (green), stroma (blue) and pathway (purple) signature scores as computed using Affymetrix microarray and Illumina RNA-Seq platforms.

Mentions: Two different gene expression approaches have been developed to prospectively classify breast cancers into molecular subtypes: Subtype Classification Models (SCMs) [7–9] and Single Sample Predictors (SSPs) [10–12], which include PAM50. In the current dataset, our subtype classifier SCMOD2 [8] showed the highest correlation between microarray and RNA-Seq technologies (κ = 0.975; Figure 3A, Additional file 1: Table S3), which was significantly higher than the other classifiers (100 bootstrap replicates, corrected p-value <0.001; Additional file 1: Table S4). Of note, although the kappa coefficients for SCMGENE [9] and PAM50 [12] were very similar (κ = 0.903 vs. 0.902 for SCMGENE and PAM50, respectively), SCMGENE was more concordant than PAM50 in our study (corrected p-value = 0.001, Additional file 1: Table S4).Figure 3


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)

Correlation values for the evaluated subtype classifiers and gene expression signatures. A: Cohen’s Kappa coefficients for subtype classifiers (orange: SCMs; purple: SSPs). B: Spearman correlation values for prognostic (orange), immune (green), stroma (blue) and pathway (purple) signature scores as computed using Affymetrix microarray and Illumina RNA-Seq platforms.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: Correlation values for the evaluated subtype classifiers and gene expression signatures. A: Cohen’s Kappa coefficients for subtype classifiers (orange: SCMs; purple: SSPs). B: Spearman correlation values for prognostic (orange), immune (green), stroma (blue) and pathway (purple) signature scores as computed using Affymetrix microarray and Illumina RNA-Seq platforms.
Mentions: Two different gene expression approaches have been developed to prospectively classify breast cancers into molecular subtypes: Subtype Classification Models (SCMs) [7–9] and Single Sample Predictors (SSPs) [10–12], which include PAM50. In the current dataset, our subtype classifier SCMOD2 [8] showed the highest correlation between microarray and RNA-Seq technologies (κ = 0.975; Figure 3A, Additional file 1: Table S3), which was significantly higher than the other classifiers (100 bootstrap replicates, corrected p-value <0.001; Additional file 1: Table S4). Of note, although the kappa coefficients for SCMGENE [9] and PAM50 [12] were very similar (κ = 0.903 vs. 0.902 for SCMGENE and PAM50, respectively), SCMGENE was more concordant than PAM50 in our study (corrected p-value = 0.001, Additional file 1: Table S4).Figure 3

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