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Biomarker discovery for colon cancer using a 761 gene RT-PCR assay.

Clark-Langone KM, Wu JY, Sangli C, Chen A, Snable JL, Nguyen A, Hackett JR, Baker J, Yothers G, Kim C, Cronin MT - BMC Genomics (2007)

Bottom Line: Studies using RT-PCR technology as a discovery tool have historically been limited to relatively small gene sets compared to other gene expression platforms such as microarrays.PCR was performed as independent TaqMan reactions for each candidate gene.We have developed a high throughput, quantitatively precise multi-analyte gene expression platform for biomarker discovery that approaches low density DNA arrays in numbers of genes analyzed while maintaining the high specificity, sensitivity and reproducibility that are characteristics of RT-PCR.

View Article: PubMed Central - HTML - PubMed

Affiliation: Genomic Health, Inc, Redwood City, CA, USA. klangone@genomichealth.com

ABSTRACT

Background: Reverse transcription PCR (RT-PCR) is widely recognized to be the gold standard method for quantifying gene expression. Studies using RT-PCR technology as a discovery tool have historically been limited to relatively small gene sets compared to other gene expression platforms such as microarrays. We have recently shown that TaqMan RT-PCR can be scaled up to profile expression for 192 genes in fixed paraffin-embedded (FPE) clinical study tumor specimens. This technology has also been used to develop and commercialize a widely used clinical test for breast cancer prognosis and prediction, the Onco typeDX assay. A similar need exists in colon cancer for a test that provides information on the likelihood of disease recurrence in colon cancer (prognosis) and the likelihood of tumor response to standard chemotherapy regimens (prediction). We have now scaled our RT-PCR assay to efficiently screen 761 biomarkers across hundreds of patient samples and applied this process to biomarker discovery in colon cancer. This screening strategy remains attractive due to the inherent advantages of maintaining platform consistency from discovery through clinical application.

Results: RNA was extracted from formalin fixed paraffin embedded (FPE) tissue, as old as 28 years, from 354 patients enrolled in NSABP C-01 and C-02 colon cancer studies. Multiplexed reverse transcription reactions were performed using a gene specific primer pool containing 761 unique primers. PCR was performed as independent TaqMan reactions for each candidate gene. Hierarchal clustering demonstrates that genes expected to co-express form obvious, distinct and in certain cases very tightly correlated clusters, validating the reliability of this technical approach to biomarker discovery.

Conclusion: We have developed a high throughput, quantitatively precise multi-analyte gene expression platform for biomarker discovery that approaches low density DNA arrays in numbers of genes analyzed while maintaining the high specificity, sensitivity and reproducibility that are characteristics of RT-PCR. Biomarkers discovered using this approach can be transferred to a clinical reference laboratory setting without having to re-validate the assay on a second technology platform.

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Related in: MedlinePlus

Stromal response. Gene groups identified by clustering analysis. Clustering analysis was performed using the 1-Pearson's R distance and unweighted pair-group average amalgamation method. Clustering was performed using all 761 genes. Figures 8-13 represent selected clusters from the entire 761 gene dendogram.
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Figure 11: Stromal response. Gene groups identified by clustering analysis. Clustering analysis was performed using the 1-Pearson's R distance and unweighted pair-group average amalgamation method. Clustering was performed using all 761 genes. Figures 8-13 represent selected clusters from the entire 761 gene dendogram.

Mentions: As an indication of the robustness of this high complexity assay we sought to identify co-expressed groups of genes, that are plausible based on known biological pathways. Unsupervised cluster analysis was performed on the final sample set using all 761 genes, resulting in several distinct clusters representing known biological pathways. Figures 8, 9, 10, 11, 12, 13 display sub-clusters observed among the 761 genes. We identified the following gene groups and pathways: cell cycle and proliferation, epithelial markers (or products secreted by epithelial cells), focal adhesion, stromal response, early response and immune/interferon inducible genes. The most highly correlated gene set from among these groups was the stromal response gene group, where the 1-Pearson's distance for 26 genes was less than 0.5. Within this group are many genes that encode extracellular matrix (ECM) proteins and regulators thereof – a signature similar to that seen during wound healing [15]. There is also a very high concordance in gene expression between p16ink4 and p14arf. This is to be expected since these are alternative transcripts of the same gene and the primer probe set used for p16ink4 also amplifies the p14arf variant. Additionally, correlations among genes within families were observed; for example, CDX1 and CDX2 (Pearsons correlation = 0.67), FUT3 and FUT6 (Pearsons correlation = 0.59), AREG and EREG (Pearsons correlation = 0.73), HSP1A1 and HSP1AB (Pearsons correlation = 0.44).


Biomarker discovery for colon cancer using a 761 gene RT-PCR assay.

Clark-Langone KM, Wu JY, Sangli C, Chen A, Snable JL, Nguyen A, Hackett JR, Baker J, Yothers G, Kim C, Cronin MT - BMC Genomics (2007)

Stromal response. Gene groups identified by clustering analysis. Clustering analysis was performed using the 1-Pearson's R distance and unweighted pair-group average amalgamation method. Clustering was performed using all 761 genes. Figures 8-13 represent selected clusters from the entire 761 gene dendogram.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1994687&req=5

Figure 11: Stromal response. Gene groups identified by clustering analysis. Clustering analysis was performed using the 1-Pearson's R distance and unweighted pair-group average amalgamation method. Clustering was performed using all 761 genes. Figures 8-13 represent selected clusters from the entire 761 gene dendogram.
Mentions: As an indication of the robustness of this high complexity assay we sought to identify co-expressed groups of genes, that are plausible based on known biological pathways. Unsupervised cluster analysis was performed on the final sample set using all 761 genes, resulting in several distinct clusters representing known biological pathways. Figures 8, 9, 10, 11, 12, 13 display sub-clusters observed among the 761 genes. We identified the following gene groups and pathways: cell cycle and proliferation, epithelial markers (or products secreted by epithelial cells), focal adhesion, stromal response, early response and immune/interferon inducible genes. The most highly correlated gene set from among these groups was the stromal response gene group, where the 1-Pearson's distance for 26 genes was less than 0.5. Within this group are many genes that encode extracellular matrix (ECM) proteins and regulators thereof – a signature similar to that seen during wound healing [15]. There is also a very high concordance in gene expression between p16ink4 and p14arf. This is to be expected since these are alternative transcripts of the same gene and the primer probe set used for p16ink4 also amplifies the p14arf variant. Additionally, correlations among genes within families were observed; for example, CDX1 and CDX2 (Pearsons correlation = 0.67), FUT3 and FUT6 (Pearsons correlation = 0.59), AREG and EREG (Pearsons correlation = 0.73), HSP1A1 and HSP1AB (Pearsons correlation = 0.44).

Bottom Line: Studies using RT-PCR technology as a discovery tool have historically been limited to relatively small gene sets compared to other gene expression platforms such as microarrays.PCR was performed as independent TaqMan reactions for each candidate gene.We have developed a high throughput, quantitatively precise multi-analyte gene expression platform for biomarker discovery that approaches low density DNA arrays in numbers of genes analyzed while maintaining the high specificity, sensitivity and reproducibility that are characteristics of RT-PCR.

View Article: PubMed Central - HTML - PubMed

Affiliation: Genomic Health, Inc, Redwood City, CA, USA. klangone@genomichealth.com

ABSTRACT

Background: Reverse transcription PCR (RT-PCR) is widely recognized to be the gold standard method for quantifying gene expression. Studies using RT-PCR technology as a discovery tool have historically been limited to relatively small gene sets compared to other gene expression platforms such as microarrays. We have recently shown that TaqMan RT-PCR can be scaled up to profile expression for 192 genes in fixed paraffin-embedded (FPE) clinical study tumor specimens. This technology has also been used to develop and commercialize a widely used clinical test for breast cancer prognosis and prediction, the Onco typeDX assay. A similar need exists in colon cancer for a test that provides information on the likelihood of disease recurrence in colon cancer (prognosis) and the likelihood of tumor response to standard chemotherapy regimens (prediction). We have now scaled our RT-PCR assay to efficiently screen 761 biomarkers across hundreds of patient samples and applied this process to biomarker discovery in colon cancer. This screening strategy remains attractive due to the inherent advantages of maintaining platform consistency from discovery through clinical application.

Results: RNA was extracted from formalin fixed paraffin embedded (FPE) tissue, as old as 28 years, from 354 patients enrolled in NSABP C-01 and C-02 colon cancer studies. Multiplexed reverse transcription reactions were performed using a gene specific primer pool containing 761 unique primers. PCR was performed as independent TaqMan reactions for each candidate gene. Hierarchal clustering demonstrates that genes expected to co-express form obvious, distinct and in certain cases very tightly correlated clusters, validating the reliability of this technical approach to biomarker discovery.

Conclusion: We have developed a high throughput, quantitatively precise multi-analyte gene expression platform for biomarker discovery that approaches low density DNA arrays in numbers of genes analyzed while maintaining the high specificity, sensitivity and reproducibility that are characteristics of RT-PCR. Biomarkers discovered using this approach can be transferred to a clinical reference laboratory setting without having to re-validate the assay on a second technology platform.

Show MeSH
Related in: MedlinePlus