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Exon-array profiling unlocks clinically and biologically relevant gene signatures from formalin-fixed paraffin-embedded tumour samples.

Hall JS, Leong HS, Armenoult LS, Newton GE, Valentine HR, Irlam JJ, Möller-Levet C, Sikand KA, Pepper SD, Miller CJ, West CM - Br. J. Cancer (2011)

Bottom Line: Differential gene expression was confirmed using Quantigene, a multiplex bead-based alternative to qRT-PCR.Quantigene analysis of the top 26 differentially expressed genes correctly partitioned cervix samples as SCC or AC.FFPE samples can be profiled using Exon arrays to derive gene expression signatures that are sufficiently robust to be applied to independent data sets, identify novel biology and design assays for independent platform validation.

View Article: PubMed Central - PubMed

Affiliation: Translational Radiobiology Group, School of Cancer and Enabling Sciences, Manchester Academic Health Science Centre, The University of Manchester, Wilmslow Road, Manchester M20 4BX, UK.

ABSTRACT

Background: Degradation and chemical modification of RNA in formalin-fixed paraffin-embedded (FFPE) samples hamper their use in expression profiling studies. This study aimed to show that useful information can be obtained by Exon-array profiling archival FFPE tumour samples.

Methods: Nineteen cervical squamous cell carcinoma (SCC) and 9 adenocarcinoma (AC) FFPE samples (10-16-year-old) were profiled using Affymetrix Exon arrays. The gene signature derived was tested on a fresh-frozen non-small cell lung cancer (NSCLC) series. Exploration of biological networks involved gene set enrichment analysis (GSEA). Differential gene expression was confirmed using Quantigene, a multiplex bead-based alternative to qRT-PCR.

Results: In all, 1062 genes were higher in SCC vs AC, and 155 genes higher in AC. The 1217-gene signature correctly separated 58 NSCLC into SCC and AC. A gene network centered on hepatic nuclear factor and GATA6 was identified in AC, suggesting a role in glandular cell differentiation of the cervix. Quantigene analysis of the top 26 differentially expressed genes correctly partitioned cervix samples as SCC or AC.

Conclusion: FFPE samples can be profiled using Exon arrays to derive gene expression signatures that are sufficiently robust to be applied to independent data sets, identify novel biology and design assays for independent platform validation.

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Related in: MedlinePlus

Cross-validation of the FFPE-derived gene signature on an independent fresh-frozen NSCLC data set. (A) Hierarchical clustering of genes and samples was based on Pearson's correlation. The scaled expression of each probeset, denoted as the row Z-score, is plotted in red–blue colour scale with red indicating high expression and blue indicating low expression. The coloured bar above the heatmap indicates the histological classification: orange=SCC; green=AC; blue=misclassified samples. (B) Principal component analysis of the SCC/AC gene signature when applied to the NSCLC data set. The numbers represent the patient IDs. Different colours are used to represent the different histological subtypes: orange=SCC; green=AC; blue=misclassified samples. (C) Percentage variance explained by the first 10 principal components.
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fig5: Cross-validation of the FFPE-derived gene signature on an independent fresh-frozen NSCLC data set. (A) Hierarchical clustering of genes and samples was based on Pearson's correlation. The scaled expression of each probeset, denoted as the row Z-score, is plotted in red–blue colour scale with red indicating high expression and blue indicating low expression. The coloured bar above the heatmap indicates the histological classification: orange=SCC; green=AC; blue=misclassified samples. (B) Principal component analysis of the SCC/AC gene signature when applied to the NSCLC data set. The numbers represent the patient IDs. Different colours are used to represent the different histological subtypes: orange=SCC; green=AC; blue=misclassified samples. (C) Percentage variance explained by the first 10 principal components.

Mentions: An independent carcinoma of the cervix data set with histology data was not available publically. A search for any clinical cancer cohort that contained the histological groups SCC and AC identified one data set. This independent cohort comprised 58 fresh-frozen human NSCLC tissue samples hybridised to Plus 2.0 arrays (Kuner et al, 2009) (GEO accession: GSE10245). The use of a different tumour type represents a more stringent test of the signature derived in FFPE than if a cervix carcinoma validation cohort was available. Of our 2673 probesets, only 27% had corresponding probesets on the Plus 2.0 array through mapping to common exonic locus, such that the Plus 2.0 array probesets identified are contained within or overlapping the genomic regions spanned by the Exon array probes (see Supplementary Methods for more details). We used xmapcore (Yates et al, 2008) to perform this cross-platform probeset conversion and identified 730 Plus 2.0 probesets that targeted 333 genes in the SCC/AC gene signature. When tested on the NSCLC data set, the signature stratified the NSCLC sample as SCC or AC in good agreement with histopathological data (Figure 5). Disagreements were observed for four samples: Pat 342 was classified as AC but clustered to the SCC group, while Pat 30, 55, 188 which were classified as SCC by histology, clustered with the other AC samples. The same discrepancies were apparent in the original study, attributed, by the authors, to sample misclassification (Kuner et al, 2009). Thus, the FFPE signature is not only robust, but generalises across cancer types.


Exon-array profiling unlocks clinically and biologically relevant gene signatures from formalin-fixed paraffin-embedded tumour samples.

Hall JS, Leong HS, Armenoult LS, Newton GE, Valentine HR, Irlam JJ, Möller-Levet C, Sikand KA, Pepper SD, Miller CJ, West CM - Br. J. Cancer (2011)

Cross-validation of the FFPE-derived gene signature on an independent fresh-frozen NSCLC data set. (A) Hierarchical clustering of genes and samples was based on Pearson's correlation. The scaled expression of each probeset, denoted as the row Z-score, is plotted in red–blue colour scale with red indicating high expression and blue indicating low expression. The coloured bar above the heatmap indicates the histological classification: orange=SCC; green=AC; blue=misclassified samples. (B) Principal component analysis of the SCC/AC gene signature when applied to the NSCLC data set. The numbers represent the patient IDs. Different colours are used to represent the different histological subtypes: orange=SCC; green=AC; blue=misclassified samples. (C) Percentage variance explained by the first 10 principal components.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3065290&req=5

fig5: Cross-validation of the FFPE-derived gene signature on an independent fresh-frozen NSCLC data set. (A) Hierarchical clustering of genes and samples was based on Pearson's correlation. The scaled expression of each probeset, denoted as the row Z-score, is plotted in red–blue colour scale with red indicating high expression and blue indicating low expression. The coloured bar above the heatmap indicates the histological classification: orange=SCC; green=AC; blue=misclassified samples. (B) Principal component analysis of the SCC/AC gene signature when applied to the NSCLC data set. The numbers represent the patient IDs. Different colours are used to represent the different histological subtypes: orange=SCC; green=AC; blue=misclassified samples. (C) Percentage variance explained by the first 10 principal components.
Mentions: An independent carcinoma of the cervix data set with histology data was not available publically. A search for any clinical cancer cohort that contained the histological groups SCC and AC identified one data set. This independent cohort comprised 58 fresh-frozen human NSCLC tissue samples hybridised to Plus 2.0 arrays (Kuner et al, 2009) (GEO accession: GSE10245). The use of a different tumour type represents a more stringent test of the signature derived in FFPE than if a cervix carcinoma validation cohort was available. Of our 2673 probesets, only 27% had corresponding probesets on the Plus 2.0 array through mapping to common exonic locus, such that the Plus 2.0 array probesets identified are contained within or overlapping the genomic regions spanned by the Exon array probes (see Supplementary Methods for more details). We used xmapcore (Yates et al, 2008) to perform this cross-platform probeset conversion and identified 730 Plus 2.0 probesets that targeted 333 genes in the SCC/AC gene signature. When tested on the NSCLC data set, the signature stratified the NSCLC sample as SCC or AC in good agreement with histopathological data (Figure 5). Disagreements were observed for four samples: Pat 342 was classified as AC but clustered to the SCC group, while Pat 30, 55, 188 which were classified as SCC by histology, clustered with the other AC samples. The same discrepancies were apparent in the original study, attributed, by the authors, to sample misclassification (Kuner et al, 2009). Thus, the FFPE signature is not only robust, but generalises across cancer types.

Bottom Line: Differential gene expression was confirmed using Quantigene, a multiplex bead-based alternative to qRT-PCR.Quantigene analysis of the top 26 differentially expressed genes correctly partitioned cervix samples as SCC or AC.FFPE samples can be profiled using Exon arrays to derive gene expression signatures that are sufficiently robust to be applied to independent data sets, identify novel biology and design assays for independent platform validation.

View Article: PubMed Central - PubMed

Affiliation: Translational Radiobiology Group, School of Cancer and Enabling Sciences, Manchester Academic Health Science Centre, The University of Manchester, Wilmslow Road, Manchester M20 4BX, UK.

ABSTRACT

Background: Degradation and chemical modification of RNA in formalin-fixed paraffin-embedded (FFPE) samples hamper their use in expression profiling studies. This study aimed to show that useful information can be obtained by Exon-array profiling archival FFPE tumour samples.

Methods: Nineteen cervical squamous cell carcinoma (SCC) and 9 adenocarcinoma (AC) FFPE samples (10-16-year-old) were profiled using Affymetrix Exon arrays. The gene signature derived was tested on a fresh-frozen non-small cell lung cancer (NSCLC) series. Exploration of biological networks involved gene set enrichment analysis (GSEA). Differential gene expression was confirmed using Quantigene, a multiplex bead-based alternative to qRT-PCR.

Results: In all, 1062 genes were higher in SCC vs AC, and 155 genes higher in AC. The 1217-gene signature correctly separated 58 NSCLC into SCC and AC. A gene network centered on hepatic nuclear factor and GATA6 was identified in AC, suggesting a role in glandular cell differentiation of the cervix. Quantigene analysis of the top 26 differentially expressed genes correctly partitioned cervix samples as SCC or AC.

Conclusion: FFPE samples can be profiled using Exon arrays to derive gene expression signatures that are sufficiently robust to be applied to independent data sets, identify novel biology and design assays for independent platform validation.

Show MeSH
Related in: MedlinePlus