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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

Risk prediction scores of the commercially available prognostic signatures. Scatterplots reporting the continuous risk prediction scores of the commercially available prognostic signatures. Each dot is colored according to the corresponding risk classification: blue for concordant low-risk, orange for concordant intermediate risk, green for concordant high-risk and red for discordance. The cutoff used to discretize the continuous risk predictions into risk classifications are represented in dashed red lines. Spearman correlation coefficient and p-value are provided below the plots.
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Fig4: Risk prediction scores of the commercially available prognostic signatures. Scatterplots reporting the continuous risk prediction scores of the commercially available prognostic signatures. Each dot is colored according to the corresponding risk classification: blue for concordant low-risk, orange for concordant intermediate risk, green for concordant high-risk and red for discordance. The cutoff used to discretize the continuous risk predictions into risk classifications are represented in dashed red lines. Spearman correlation coefficient and p-value are provided below the plots.

Mentions: Using microarray technology, several prognostic gene expression signatures have been developed in the attempt to help clinicians to identify which breast cancers are at high or low risk of recurrence [43]. Among these, MammaPrint® (here referred to as GENE70) [14], OncotypeDx® (here referred to as GENE21) [15], GGI [16], ENDOPREDICT [17] and ROR-S [12] have been widely investigated and applied in the clinical setting. When comparing the values of these signatures on a continuum as defined by either microarray or RNA-Seq, an excellent Spearman correlation was found: 0.97 [95% CI 0.968-0.972] for GENE70; 0.965 [95% CI 0.962-0.967] for GENE21; 0.985 [95% CI 0.984-0.986] for GGI; 0.979 [95%CI 0.977,0.981] for ENDOPREDICT; and 0.965 (95% CI 0.962,0.967) for ROR-S (Figure 4).Figure 4


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)

Risk prediction scores of the commercially available prognostic signatures. Scatterplots reporting the continuous risk prediction scores of the commercially available prognostic signatures. Each dot is colored according to the corresponding risk classification: blue for concordant low-risk, orange for concordant intermediate risk, green for concordant high-risk and red for discordance. The cutoff used to discretize the continuous risk predictions into risk classifications are represented in dashed red lines. 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

Fig4: Risk prediction scores of the commercially available prognostic signatures. Scatterplots reporting the continuous risk prediction scores of the commercially available prognostic signatures. Each dot is colored according to the corresponding risk classification: blue for concordant low-risk, orange for concordant intermediate risk, green for concordant high-risk and red for discordance. The cutoff used to discretize the continuous risk predictions into risk classifications are represented in dashed red lines. Spearman correlation coefficient and p-value are provided below the plots.
Mentions: Using microarray technology, several prognostic gene expression signatures have been developed in the attempt to help clinicians to identify which breast cancers are at high or low risk of recurrence [43]. Among these, MammaPrint® (here referred to as GENE70) [14], OncotypeDx® (here referred to as GENE21) [15], GGI [16], ENDOPREDICT [17] and ROR-S [12] have been widely investigated and applied in the clinical setting. When comparing the values of these signatures on a continuum as defined by either microarray or RNA-Seq, an excellent Spearman correlation was found: 0.97 [95% CI 0.968-0.972] for GENE70; 0.965 [95% CI 0.962-0.967] for GENE21; 0.985 [95% CI 0.984-0.986] for GGI; 0.979 [95%CI 0.977,0.981] for ENDOPREDICT; and 0.965 (95% CI 0.962,0.967) for ROR-S (Figure 4).Figure 4

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