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A Method to Correlate mRNA Expression Datasets Obtained from Fresh Frozen and Formalin-Fixed, Paraffin-Embedded Tissue Samples: A Matter of Thresholds.

Mustafa DA, Sieuwerts AM, Smid M, de Weerd V, van der Weiden M, Meijer-van Gelder ME, Martens JW, Foekens JA, Kros JM - PLoS ONE (2015)

Bottom Line: Spearman correlation coefficients between the matched FFPE and FF samples were calculated for three probe lists with varying levels of significance and compared to the correlation based on all measured probes.Unsupervised hierarchical clustering of the 27 pairs using the resulting probes yielded 25, 21, and 19 correctly clustered pairs, respectively, compared to 1 pair when all probes were used.The proposed method enables comparison of gene expression profiles of FFPE and/or FF origin measured on the same platform.

View Article: PubMed Central - PubMed

Affiliation: Dept. of Pathology, Erasmus University Medical Center, Rotterdam, The Netherlands.

ABSTRACT

Background: Gene expression profiling of tumors is a successful tool for the discovery of new cancer biomarkers and potential targets for the development of new therapeutic strategies. Reliable profiling is preferably performed on fresh frozen (FF) tissues in which the quality of nucleic acids is better preserved than in formalin-fixed paraffin-embedded (FFPE) material. However, since snap-freezing of biopsy materials is often not part of daily routine in pathology laboratories, one may have to rely on archival FFPE material. Procedures to retrieve the RNAs from FFPE materials have been developed and therefore, datasets obtained from FFPE and FF materials need to be made compatible to ensure reliable comparisons are possible.

Aim: To develop an efficient method to compare gene expression profiles obtained from FFPE and FF samples using the same platform.

Methods: Twenty-six FFPE-FF sample pairs of the same tumors representing various cancer types, and two FFPE-FF sample pairs of breast cancer cell lines, were included. Total RNA was extracted and gene expression profiling was carried out using Illumina's Whole-Genome cDNA-mediated Annealing, Selection, extension and Ligation (WG-DASL) V3 arrays, enabling the simultaneous detection of 24,526 mRNA transcripts. A sample exclusion criterion was created based on the expression of 11 stably expressed reference genes. Pearson correlation at the probe level was calculated for paired FFPE-FF, and three cut-off values were chosen. Spearman correlation coefficients between the matched FFPE and FF samples were calculated for three probe lists with varying levels of significance and compared to the correlation based on all measured probes. Unsupervised hierarchical cluster analysis was performed to verify performance of the included probe lists to compare matched FPPE-FF samples.

Results: Twenty-seven FFPE-FF pairs passed the sample exclusion criterion. From the profiles of 27 FFPE and FF matched samples, the best correlating probes were identified for various levels of significance (Pearson P<0.01, n = 1,432; P<0.05, n = 2,530; and P<0.10, n = 3,351 probes). Unsupervised hierarchical clustering of the 27 pairs using the resulting probes yielded 25, 21, and 19 correctly clustered pairs, respectively, compared to 1 pair when all probes were used.

Conclusion: The proposed method enables comparison of gene expression profiles of FFPE and/or FF origin measured on the same platform.

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Related in: MedlinePlus

Ruler of the three cut-off values used to compare the gene expression profiles of FFPE and FF samples.Each Pearson P-value results in different numbers of probes to be included for further analysis.
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pone.0144097.g003: Ruler of the three cut-off values used to compare the gene expression profiles of FFPE and FF samples.Each Pearson P-value results in different numbers of probes to be included for further analysis.

Mentions: In summary, we describe a novel and simple method to reliably compare gene expression data derived from FFPE and FF samples, using probes with various levels of correlation strength (P-value <0.01, <0.05 and <0.10). The reliable probes identified by this method will vary between different datasets. The method is applicable to various platforms, and is successful in selecting probes yielding consistent signals across tumor types, and proved independent of the normalization pre-processing. The number of probes to include and the comparison between FFPE and FF samples is dependent on individual criteria for significance. Applying this method will enable researchers to profile the archived FFPE samples and compare the data to already available profiled data obtained from FF samples. Our data however also show that, in order to analyze expression data from FFPE and FF samples, a trade-off (Fig 3) between the number of included probes and the ability to accurately compare FFPE and FF samples will apply.


A Method to Correlate mRNA Expression Datasets Obtained from Fresh Frozen and Formalin-Fixed, Paraffin-Embedded Tissue Samples: A Matter of Thresholds.

Mustafa DA, Sieuwerts AM, Smid M, de Weerd V, van der Weiden M, Meijer-van Gelder ME, Martens JW, Foekens JA, Kros JM - PLoS ONE (2015)

Ruler of the three cut-off values used to compare the gene expression profiles of FFPE and FF samples.Each Pearson P-value results in different numbers of probes to be included for further analysis.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4696787&req=5

pone.0144097.g003: Ruler of the three cut-off values used to compare the gene expression profiles of FFPE and FF samples.Each Pearson P-value results in different numbers of probes to be included for further analysis.
Mentions: In summary, we describe a novel and simple method to reliably compare gene expression data derived from FFPE and FF samples, using probes with various levels of correlation strength (P-value <0.01, <0.05 and <0.10). The reliable probes identified by this method will vary between different datasets. The method is applicable to various platforms, and is successful in selecting probes yielding consistent signals across tumor types, and proved independent of the normalization pre-processing. The number of probes to include and the comparison between FFPE and FF samples is dependent on individual criteria for significance. Applying this method will enable researchers to profile the archived FFPE samples and compare the data to already available profiled data obtained from FF samples. Our data however also show that, in order to analyze expression data from FFPE and FF samples, a trade-off (Fig 3) between the number of included probes and the ability to accurately compare FFPE and FF samples will apply.

Bottom Line: Spearman correlation coefficients between the matched FFPE and FF samples were calculated for three probe lists with varying levels of significance and compared to the correlation based on all measured probes.Unsupervised hierarchical clustering of the 27 pairs using the resulting probes yielded 25, 21, and 19 correctly clustered pairs, respectively, compared to 1 pair when all probes were used.The proposed method enables comparison of gene expression profiles of FFPE and/or FF origin measured on the same platform.

View Article: PubMed Central - PubMed

Affiliation: Dept. of Pathology, Erasmus University Medical Center, Rotterdam, The Netherlands.

ABSTRACT

Background: Gene expression profiling of tumors is a successful tool for the discovery of new cancer biomarkers and potential targets for the development of new therapeutic strategies. Reliable profiling is preferably performed on fresh frozen (FF) tissues in which the quality of nucleic acids is better preserved than in formalin-fixed paraffin-embedded (FFPE) material. However, since snap-freezing of biopsy materials is often not part of daily routine in pathology laboratories, one may have to rely on archival FFPE material. Procedures to retrieve the RNAs from FFPE materials have been developed and therefore, datasets obtained from FFPE and FF materials need to be made compatible to ensure reliable comparisons are possible.

Aim: To develop an efficient method to compare gene expression profiles obtained from FFPE and FF samples using the same platform.

Methods: Twenty-six FFPE-FF sample pairs of the same tumors representing various cancer types, and two FFPE-FF sample pairs of breast cancer cell lines, were included. Total RNA was extracted and gene expression profiling was carried out using Illumina's Whole-Genome cDNA-mediated Annealing, Selection, extension and Ligation (WG-DASL) V3 arrays, enabling the simultaneous detection of 24,526 mRNA transcripts. A sample exclusion criterion was created based on the expression of 11 stably expressed reference genes. Pearson correlation at the probe level was calculated for paired FFPE-FF, and three cut-off values were chosen. Spearman correlation coefficients between the matched FFPE and FF samples were calculated for three probe lists with varying levels of significance and compared to the correlation based on all measured probes. Unsupervised hierarchical cluster analysis was performed to verify performance of the included probe lists to compare matched FPPE-FF samples.

Results: Twenty-seven FFPE-FF pairs passed the sample exclusion criterion. From the profiles of 27 FFPE and FF matched samples, the best correlating probes were identified for various levels of significance (Pearson P<0.01, n = 1,432; P<0.05, n = 2,530; and P<0.10, n = 3,351 probes). Unsupervised hierarchical clustering of the 27 pairs using the resulting probes yielded 25, 21, and 19 correctly clustered pairs, respectively, compared to 1 pair when all probes were used.

Conclusion: The proposed method enables comparison of gene expression profiles of FFPE and/or FF origin measured on the same platform.

Show MeSH
Related in: MedlinePlus