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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

Expression correlation for ER, PgR, and HER2 genes. Scatterplots reporting the expression correlation of ER, PgR, and HER2 defined by Affymetrix microarray or Illumina RNA-Seq. Each dot is colored according to the corresponding status determined by IHC: green for positive, blue for negative, red for borderline. Spearman correlation coefficient and p-value are provided below the plots.
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Fig2: Expression correlation for ER, PgR, and HER2 genes. Scatterplots reporting the expression correlation of ER, PgR, and HER2 defined by Affymetrix microarray or Illumina RNA-Seq. Each dot is colored according to the corresponding status determined by IHC: green for positive, blue for negative, red for borderline. Spearman correlation coefficient and p-value are provided below the plots.

Mentions: Among the genes retained for analysis, we focused our attention on three that are clinically relevant for breast cancer: ER, PgR, and HER2. Measuring them precisely is of utmost importance to clinical practice as these are presently the only validated breast cancer predictive biomarkers available, and they are routinely used to make decisions about patient treatment [6, 59].When comparing the expression levels of these three genes as defined by microarray and RNA-Seq, we found excellent Spearman correlation coefficients: 0.973 for ER [95% CI: 0.971-0.975]; 0.95 for PgR [95% CI: 0.947-0.954]; and 0.918 for HER2 [95% CI: 0.912-0.923] (Figure 2).Figure 2


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)

Expression correlation for ER, PgR, and HER2 genes. Scatterplots reporting the expression correlation of ER, PgR, and HER2 defined by Affymetrix microarray or Illumina RNA-Seq. Each dot is colored according to the corresponding status determined by IHC: green for positive, blue for negative, red for borderline. Spearman correlation coefficient and p-value are provided below the plots.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig2: Expression correlation for ER, PgR, and HER2 genes. Scatterplots reporting the expression correlation of ER, PgR, and HER2 defined by Affymetrix microarray or Illumina RNA-Seq. Each dot is colored according to the corresponding status determined by IHC: green for positive, blue for negative, red for borderline. Spearman correlation coefficient and p-value are provided below the plots.
Mentions: Among the genes retained for analysis, we focused our attention on three that are clinically relevant for breast cancer: ER, PgR, and HER2. Measuring them precisely is of utmost importance to clinical practice as these are presently the only validated breast cancer predictive biomarkers available, and they are routinely used to make decisions about patient treatment [6, 59].When comparing the expression levels of these three genes as defined by microarray and RNA-Seq, we found excellent Spearman correlation coefficients: 0.973 for ER [95% CI: 0.971-0.975]; 0.95 for PgR [95% CI: 0.947-0.954]; and 0.918 for HER2 [95% CI: 0.912-0.923] (Figure 2).Figure 2

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