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Feasibility of developing reliable gene expression modules from FFPE derived RNA profiled on Affymetrix arrays.

ABSTRACT: The reliability of differential gene expression analysis on formalin-fixed, paraffin-embedded (FFPE) expression profiles generated using Affymetrix arrays is questionable, due to the high range of percent-present values reported in studies which profiled FFPE samples using this technology. Moreover, the validity of gene-modules derived from external datasets in FFPE microarray expression profiles is unknown. By generating matched gene expression profiles using RNAs derived from fresh-frozen (FF) and FFPE preserved breast tumors with Affymetrix arrays and FF/FFPE RNA specific amplification-and-labeling kits, the reliability of differential expression analysis and the validity of gene modules derived from external datasets were investigated. Specifically, the reliability of differential expression analysis was investigated by developing de-novo ER/HER2 pathway gene-modules from the matched datasets and validating them on external FF/FFPE gene expression datasets using ROC analysis. Spearman's rank correlation coefficient of module scores between matched FFPE/frozen datasets was used to measure the reliability of gene-modules derived from external datasets in FFPE expression profiles. Independent of the array/amplification-kit/sample preservation method used, de-novo ER/HER2 gene-modules derived from all matched datasets showed similar prediction performance in the independent validation (AUC range in FFPE dataset; ER: 0.93-0.95, HER2: 0.85-0.91), except for the de-novo ER/HER2 gene-module derived from the FFPE dataset using the 3'IVT kit (AUC range in FFPE dataset; ER: 0.79-0.81, HER2: 0.78). Among the external gene modules considered, roughly ~50% gene modules showed high concordance between expression profiles derived from matching FF and FFPE RNA. The remaining discordant gene modules between FF and FFPE expression profiles showed high concordance within matching FF datasets and within matching FFPE datasets independently, implying that microarrays still require improved amplification-and-sample-preparation protocols for deriving 100% concordant expression profiles from matching FF and FFPE RNA.

SUBMITTER: Jose V

PROVIDER: S-EPMC6118369 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Feasibility of developing reliable gene expression modules from FFPE derived RNA profiled on Affymetrix arrays.

Jose Vinu V Fumagalli Debora D Rothé Françoise F Majjaj Samira S Loi Sherene S Michiels Stefan S Sotiriou Christos C

PloS one 20180831 8

The reliability of differential gene expression analysis on formalin-fixed, paraffin-embedded (FFPE) expression profiles generated using Affymetrix arrays is questionable, due to the high range of percent-present values reported in studies which profiled FFPE samples using this technology. Moreover, the validity of gene-modules derived from external datasets in FFPE microarray expression profiles is unknown. By generating matched gene expression profiles using RNAs derived from fresh-frozen (FF) ...[more]

PMID: 30169535

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Project description:BackgroundAffymetrix GeneChips are an important tool in many facets of biological research. Recently, notable design changes to the chips have been made. In this study, we use publicly available data from Affymetrix to gauge the performance of three human gene expression arrays: Human Genome U133 Plus 2.0 (U133), Human Exon 1.0 ST (HuEx) and Human Gene 1.0 ST (HuGene).ResultsWe studied probe-, exon- and gene-level reproducibility of technical and biological replicates from each of the 3 platforms. The U133 array has larger feature sizes so it is no surprise that probe-level variances are smaller, however the larger number of probes per gene on the HuGene array seems to produce gene-level summaries that have similar variances. The gene-level summaries of the HuEx array are less reproducible than the other two, despite having the largest average number of probes per gene. Greater than 80% of the content on the HuEx arrays is expressed at or near background. Biological variation seems to have a smaller effect on U133 data. Comparing the overlap of differentially expressed genes, we see a high overall concordance among all 3 platforms, with HuEx and HuGene having greater overlap, as expected given their design. We performed an analysis of detection rates and area under ROC curves using an experiment made up of several mixtures of 2 human tissues. Though it appears that the HuEx array has worse performance in terms of detection rates, all arrays have similar ability to separate differentially expressed and non-differentially expressed genes.ConclusionDespite noticeable differences in the probe-level reproducibility, gene-level reproducibility and differential expression detection are quite similar across the three platforms. The HuEx array, an all-encompassing array, has the flexibility of measuring all known or predicted exonic content. However, the HuEx array induces poorer reproducibility for genes with fewer exons. The HuGene measures just the well-annotated genome content and appears to perform well. The U133 array, though not able to measure across the full length of a transcript, appears to perform as well as the newer designs on the set of genes common to all 3 platforms.

Project description:BackgroundRNA and microarray quality assessment form an integral part of gene expression analysis and, although methods such as the RNA integrity number (RIN) algorithm reliably asses RNA integrity, the relevance of RNA integrity in gene expression analysis as well as analysis methods to accommodate the possible effects of degradation requires further investigation. We investigated the relationship between RNA integrity and array quality on the commonly used Affymetrix Gene 1.0 ST array platform using reliable within-array and between-array quality assessment measures. The possibility of a transcript specific bias in the apparent effect of RNA degradation on the measured gene expression signal was evaluated after either excluding quality-flagged arrays or compensation for RNA degradation at different steps in the analysis.ResultsUsing probe-level and inter-array quality metrics to assess 34 Gene 1.0 ST array datasets derived from historical, paired tumour and normal primary colorectal cancer samples, 7 arrays (20.6%), with a mean sample RIN of 3.2 (SD = 0.42), were flagged during array quality assessment while 10 arrays from samples with RINs < 7 passed quality assessment, including one sample with a RIN < 3. We detected a transcript length bias in RNA degradation in only 5.8% of annotated transcript clusters (p-value 0.05, FC ≥ |2|), with longer and shorter than average transcripts under- and overrepresented in quality-flagged samples respectively. Applying compensatory measures for RNA degradation performed at least as well as excluding quality-flagged arrays, as judged by hierarchical clustering, gene expression analysis and Ingenuity Pathway Analysis; importantly, use of these compensatory measures had the significant benefit of enabling lower quality array data from irreplaceable clinical samples to be retained in downstream analyses.ConclusionsHere, we demonstrate an effective array-quality assessment strategy, which will allow the user to recognize lower quality arrays that can be included in the analysis once appropriate measures are applied to account for known or unknown sources of variation, such as array quality- and batch- effects, by implementing ComBat or Surrogate Variable Analysis. This approach of quality control and analysis will be especially useful for clinical samples with variable and low RNA qualities, with RIN scores ≥ 2.

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Feasibility of developing reliable gene expression modules from FFPE derived RNA profiled on Affymetrix arrays.

Publications

Feasibility of developing reliable gene expression modules from FFPE derived RNA profiled on Affymetrix arrays.

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