Technical AdvanceQuantitative Expression Profiling in Formalin-FixedParaffin-Embedded Samples by AffymetrixMicroarrays

Diana Abdueva,* Michele Wing,† Betty Schaub,†

Timothy Triche,*† and Elai Davicioni‡

From the Departments of Pathology and Laboratory Medicine,*

Children’s Hospital Los Angeles Saban Research Institute and

Keck School of Medicine, University of Southern California, Los

Angeles, California; the USC/CHLA Genome Core Laboratory,†

Children’s Hospital Los Angeles, Los Angeles, California; and

GenomeDx Biosciences Inc.,‡ Vancouver, British Columbia, Canada

To date, few studies have systematically characterizedmicroarray gene expression signal performance withdegraded RNA from fixed (FFPE) in comparison withintact RNA from unfixed fresh-frozen (FF) specimens.RNA was extracted and isolated from paired tumorand normal samples from both FFPE and FF kidney,lung, and colon tissue specimens and microarray sig-nal dynamics on both the raw probe and probesetlevel were evaluated. A contrast metric was developedto directly compare microarray signal derived fromRNA extracted from matched FFPE and FF specimens.Gene-level summaries were then compared to deter-mine the degree of overlap in expression profiles. RNAextracted from FFPE material was more degraded andfragmented than FF, resulting in a reduced dynamicrange of expression signal. In addition, probe perfor-mance was not affected uniformly and declined sharplytoward 5� end of genes. The most significant differences inFFPE versus FF signal were consistent across three tissuetypes and enriched with ribosomal genes. Our resultsshow that archived FFPE samples can be used to profile forexpression signatures and assess differential expressionsimilar to unfixed tissue sources. This study providesguidelines for application of these methods in the dis-covery, validation, and clinical application of microar-ray expression profiling with FFPE material. (J MolDiagn 2010, 12:409–417; DOI: 10.2353/jmoldx.2010.090155)

Over the past decade, analysis of genome-wide patternsof gene expression using oligonucleotide microarrayshas proven itself as a powerful discovery tool in basic and

translational research. Recently, this technology hasbeen leveraged to develop gene expression–based testsdesigned to aid in the treatment and management ofpatients with diseases such as cancer.1–4 These testsmeasure the expression of a number of genes, referred toas an ‘expression signature’ of a clinical or pathologicalstate and mathematical algorithms to derive patientscores. Traditionally, the discovery of gene expressionsignatures relevant for the development of clinical testshave relied on microgram quantities of high-quality RNAsuch as can be extracted from fresh or fresh-frozen (FF)tissue specimens.3–5 Subsequent studies are inevitablyrequired to adopt expression signatures discovered in FFmaterial6 to clinical specimens.7

Formalin-fixation and paraffin-embedding (FFPE) of clin-ical tissue specimens remains the standard method used topreserve tissue morphology for pathological diagnosis andsample archiving. Preserved samples, meticulously col-lected and preserved through many decades of work, areextremely rich sources of study material. A significant bar-rier to the development of such cancer tests is the fact thatfew biorepositories exist with sufficient numbers of FF sam-ples that have annotated long-term follow-up data suitablefor the design of retrospective clinical trials.8 However,working with RNA derived from FFPE specimens is con-founded by several factors, such as variability in tissuehandling/processing,9,10 tissue sources, and RNA extrac-tion methods,11 all are known to affect the quality of RNA12

and therefore can potentially be sources of bias for expres-sion profiling studies. Formalin reacts with proteins andnucleic acids causing cross-linking and chemical modifica-tion of RNA13–22 leading to lower yields, shorter, more de-

Supported in part by the National Cancer Institute, Strategic Partnering toEvaluate Cancer Signatures (U01CA-114757, to T.J.T.).

Accepted for publication January 15, 2010.

Supplemental material for this article can be found on http://jmd.amjpathol.org.

Current address of D.A.: Affymetrix, Inc., Emeryville, CA.

Address reprint requests to Timothy J. Triche, M.D., Ph.D., Departmentof Pathology, Children’s Hospital Los Angeles, 4650 Sunset Blvd., LosAngeles, CA 90027. E-mail: triche@usc.edu.

Journal of Molecular Diagnostics, Vol. 12, No. 4, July 2010

Copyright © American Society for Investigative Pathology

and the Association for Molecular Pathology

DOI: 10.2353/jmoldx.2010.090155

409

graded RNA, lacking in poly-A tail, which must be taken intoaccount when selecting a suitable reverse transcription pro-tocol for expression profiling.23 Other factors such as thedegree of successful reversal of protein-nucleic acid cross-linking, chemical modifications to RNA, and the fact thatshorter, more fragmented RNA molecules have statisticallyfewer hybridization sites for primer binding all make FFPERNA less efficient template for reverse transcription oramplification.14,17–19

Although several studies have indeed reported useableexpression data from FFPE specimens20,24–27 and a fewhave characterized differences between samples with ‘in-tact’ (e.g., FF) and degraded (e.g., FFPE) RNA,7,12,24,25,28–40

systematic investigation of these differences have remainedlargely unexplored. In this study, we performed a compar-ative analysis of archived, matched FF, and FFPE Wilm’stumor and normal tissue pairs (‘quadsets’) as well as similarquadsets from lung and colon samples. RNA was amplifiedusing the WT-Ovation FFPE System,20,41 optimized for am-plification of nanogram quantities of FFPE RNA and hybrid-ized to standard Affymetrix U133 Plus 2 GeneChips. Herewe show that whereas overall FFPE samples yielded RNA ofpoor quality relative to matched FF samples, we obtaineddata of sufficient quality for differential gene expressionstudies using combined random and poly-A priming wholetranscript amplification for oligonucleotide microarrays. Inaddition, we assess the reliability of microarray analysisusing a novel contrast metric function that can be used toderive filtered gene lists for microarray experiments usingRNA from FFPE material.

Materials and Methods

Tissue Samples

Matched FFPE and frozen samples of Wilm’s tumor andnormal adjacent kidney tissue (‘quad-sets’) were col-lected from patients at Children’s Hospital Los Angelesaccording to an institutional review board–approved pro-tocol. FFPE archived blocks and frozen tissue quad-setswere obtained from three patients who underwent surgi-cal resection 0.5, 1.5, and 7.5 years before tissue pro-cessing and nucleic acid extraction. For each patientsample three 10-micron sections (approximately 1 cm2 inarea) were cut from both formalin-fixed paraffin embed-ded blocks as well as matched frozen specimens andplaced in microfuge tubes for immediate RNA extraction.Lung and colon quadsets samples were independently pro-cessed by NuGEN Technologies as previously described(Technical Report #3, 2007, available at http://www.nugeninc.com/tasks/sites/nugen/assets/File/technical_documents/techdoc_wt_ov_ffpe_rep_03.pdf, last accessedMay 21, 2010). Additional details of the individual samplesused in this analysis can be found in supplemental Table S1at http://jmd.amjpathol.org.

RNA Extraction

RNA was extracted and purified from Wilm’s FFPE tissuesections using the commercially available Formapure nu-

cleic acid extraction kit (Agencourt Biosciences, Bev-erly MA). RNA was extracted from Wilm’s frozen tissuesections using TRIzol (Invitrogen, Carlsbad, CA) andpurified using RNeasy Protect kit (Qiagen, Valencia,CA). RNA was further purified using DNase I treatment(Ambion, Austin, TX) to eliminate any contaminatingDNA. RNA concentrations were calculated using aNanodrop ND-1000 spectrophotometer (NanodropTechnologies, Rockland, DE). RNA integrity was eval-uated by running electropherograms and RNA integritynumber, RIN42 (a correlative measure that indicatesintactness of mRNA) was determined using the RNA6000 PicoAssay for the Bioanalyzer 2100 (AgilentTechnologies, Santa Clara, CA).

RNA Amplification and GeneChip Hybridization

Purified RNA was subjected to whole-transcriptome am-plification using the WT-Ovation FFPE system. For RNAextracted from frozen or FFPE sections, 10 ng or 50 ng ofinput RNA, respectively, was used to generate amplifiedRibo-SPIA product. All clean-up steps were performedwith RNAClean magnetic beads (Agencourt Biosciences).Five micrograms of WT-Ovation product were used to frag-ment and label using the FL-Ovation Biotin V2 labelingmodule, and labeled product was hybridized to AffymetrixHuman U133 Plus 2.0 (Wilm’s quadset) or U133Av2 (colonand lung quadsets) GeneChips following manufacturer’srecommendations (Affymetrix, Santa Clara, CA).

Data Analysis

Raw CEL files were used in low level analysis. Datapreprocessing and gene-level summarization was donein Affymetrix Expression Console Software (version 1.1),where FFPE and FF samples were processed separatelyto avoid biases. All array data were processed in thestatistical language ‘R’43 using several packages in Bio-conductor.44 In particular, probe level data were read byaffy package, probe sequence data were obtained usingBioconductor annotation library (hgu133plus2), and Venndiagrams were generated using limma package.45 Geneontology overrepresentation analysis was conducted us-ing the DAVID online tool.46 Data for lung and colonquadsets were kindly provided by NuGEN Technologies.Cross-tissue comparisons involving the two different Af-fymetrix GeneChips (i.e., U133A for colon and lung andPlus 2.0 for Wilm’s quadsets) were based on the signalfrom 22,515 overlapping probesets. Please refer to NCBIGene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) study GSE19249 to access Affymetrix CEL file data.

Results

RNA Analysis

We first evaluated yields and some basic nucleic acidquality metrics for quadset RNA extractions, obtainingresults consistent with previous reports in the litera-ture.13,16,18–22,47–49 Representative data from Wilm’s

410 Abdueva et alJMD July 2010, Vol. 12, No. 4

quadset show RNA yields were dependent on both thefixation method and the sample block archive age (Figure1A). Overall, greater than twofold more RNA was ex-tracted from FF than FFPE samples (t test, P � 0.02) andalthough not statistically significant, approximately two-fold more RNA was extracted from more recently archivedblocks (0.5 and 1.5 years) as compared with older blocks(7.5 years), an observation that was more pronounced inFFPE samples (more than fourfold more RNA).

Next, we looked at the distribution of RNA sizes (toassess integrity of extracted RNA molecules) on agarosegel and by analyzing electropherogram traces (data notshown). In FFPE samples, the majority of RNAs were�500 nucleotides in length and 18S and 28S ribosomalpeaks were missing but in FF samples ribosomal peakswere clearly observable in all samples within a charac-teristic ‘smear’ of RNAs of various sizes, typical of highquality total RNA extractions.50 Bioanalyzer software wasused to assign RNA Integrity Number (RIN) values forextractions ranging from 1 to 10, with 1 being the mostdegraded profile and 10 being the most intact.42 Datafrom the Wilm quadsets shows that RIN values weresignificantly lower in FFPE samples (mean, 2.2 � 0.1)compared with FF samples (8.2 � 0.6). Similar resultswere observed in the lung and colon quadsets (data notshown).

Finally, the suitability of extracted RNA as a templatefor reverse transcriptase and subsequent amplificationfor microarray hybridization was evaluated (Figure 1B).The mean concentration of amplification product reac-tions (recorded after the second amplification round be-fore biotin labeling) from FF RNA was 1.5-fold greaterthan FFPE RNA (P � 0.0004) even though the amount ofRNA input was fivefold greater in FFPE samples (as rec-ommended by manufacturer). Adjusting for RNA input,there was about a ninefold increase in FF amplificationyields than FFPE samples. Additionally, FFPE RNA yieldwas twofold greater in recently archived blocks (0.5 and1.5 years) compared with older blocks (7.5 years; P �0.009). Although extractions from FFPE yielded lowerquantities of RNA that appeared more degraded thanfrom FF, sufficient amplified product for microarray hy-bridization was generated from quadsets evaluated. To-gether, these findings indicate that although in patient-

matched starting material, FFPE tissues yield less RNAand amplified cDNA than FF tissues, this material is stillsuitable for whole-transcriptome amplification and yieldssufficient for microarray hybridization.34

Microarray QC Analysis

Standard QC analysis workflows, available in AffymetrixExpression Console Software (available at: http://www.affymetrix.com/support/downloads/manuals/expression_console_userguide.pdf, last accessed May 21, 2010),were used to derive QC metrics (Table 1). These metricsare commonly used to evaluate whether or not expressiondata are of sufficient quality for downstream analysis. Thepercentage of genes called present (%P) was �1.4-foldhigher in FF than FFPE. Scale factor is used to scale chipraw intensity to an arbitrary value and reflects the fact thatFFPE signal is systematically lower than FF. Mean absolutedeviation (MAD) shows that FFPE samples had significantlyhigher variance than FF. All quantitative measures werefound to be consistent within the assay, while differing sig-nificantly between assays (i.e., FFPE versus FF samplepreparations). Significant differences in QC metrics of FFPEand FF material indicate that probe modeling should beperformed for FFPE and FF assays separately as thesedifferences could potentially skew normalized data, con-found results, and lead to false interpretations.51 To furtherexplore microarray signal dynamics between FFPE and FFwe looked at raw signal analysis to determine whether we

Figure 1. Analysis of RNA from Wilm’s tumorand paired kidney samples from FFPE and FFquadsets. A: Yields of RNA extracted from FFand FFPE samples archived for various blockages. B: Mean yields of amplified DNA fromFFPE and FF generated using WT-Ovation FFPEkit. Yields are expressed as concentration ofproduct obtained after two rounds of amplifica-tion using a combined random- and oligo-dT–primed protocol. Note for FFPE samples 50 ngwas used as input, whereas for FF samples 10 nginput RNA was used as per manufacturer’srecommendations.

Table 1. Data Quality Metrics Generated Using Affymetrix�Expression Console™ Software to Help MonitorData Quality

Sampletype %P SF

MADresidualmean

MADresidual

SDRLE

meanRLESD

AverageFFPE

43.12 18.05 0.37 0.22 0.35 0.41

AverageFF

60.79 3.81 0.21 0.16 0.33 0.52

For the Wilm quadset study, average metrics including percentpresent (%P), scale factor (SF), mean absolute deviation (MAD), andrelative log expression (RLE) were generated separately for FFPE andFF samples.

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could observe subsets of genes or probesets that reportsignal similarly from FFPE and FF.

Raw Signal Analysis

We next examined overall probe signal behavior in bothassays at the raw probe level. Raw intensities of 604,258probes for each assay were examined to determine keysignal characteristics. Analysis of probe level signal den-sities revealed distinct difference in the shapes of densityplots, with FFPE data exhibiting reduced dynamic rangeof raw probe signal compared with FF signal densityprofile (Figure 2A). These density plots also illustrate thehigh reproducibility of raw signal distribution within thesame sample type despite tissue type, block age, etc.,with median within-assay raw signal Pearson correlationof 0.91 and 0.92 in FF and FFPE, respectively. Scatter-plots of log2 intensity of median signal across normalkidney FF samples versus log2 intensity of median signalacross normal kidney FFPE samples show Pearson cor-relation coefficients of 0.75, indicating significantly lowersimilarity for probe signal between FF and FFPE assays(Figure 2B) and in accordance with previously reportedresults with a different assay system.34

To study probe signal as a function of 5�–3� position ofprobes within a probeset, the average intensity of the

probes was plotted as a function of probe position.Namely, probes within each probeset were numbereddirectionally from the 5� end to the 3� end, and probeintensities were averaged by probe number across alltranscripts independently for each sample. Figure 3shows so-called ‘degradation plots’ of FFPE and FFprofiles. We observe characteristic linear degradationcurves in fresh frozen samples but distinct nonlinearprofiles in FFPE, suggesting a relatively sharp decay ofsignal intensity toward 5� end compared with 3� end.These data suggest that FFPE samples are more sen-sitive to the 3� biased probe-design of the Gene Chipsthan FF samples and that RNA in FFPE samples issubjected to more chemical degradation, which occursfrom 5� to 3� ends.28,52

To further study differences in individual probe perfor-mance, we defined a ‘contrast metric’ as the medianpair-wise difference in log2 signals between the FF andFFPE assays. We calculated the difference in log2 probesignal for every matching pair of FFPE and FF samples,and the median difference across all pairs was used as ameasure of assay dissimilarity for a given probe. Weobserve that probe contrast has a positive mean (i.e.,systematically higher raw signal in FF samples comparedwith FFPE) and an asymmetrical shape, depicted in thecontrast density plot on Figure 4A. The elongated rightshoulder of the density plot illustrates that probes are notaffected uniformly and suggests a nonuniform FFPE bias.To track the origin of the positive contrast, we evaluatedprobe contrast behavior within each probeset to deter-mine whether probe performance is compromised for agroup of probesets or whether the shape of the contrastmetric curve is dependent on other variables such asprobe GC content or probe interrogation position. Whilewe did not find any dependence of contrast on probe GCcontent, strand or chromosomal location (supplementalFigure S1 at http://jmd.amjpathol.org), we observed thatthe median contrast density within probesets was highlysimilar to that observed on the raw probe level (Figure4B), suggesting that probesets (i.e., genes) are also af-

Figure 2. A: Raw signal density, FFPE (blue) and FF (red). B: Scatterplot oflog2 raw intensity of median FFPE normal samples (y axis) versus median FFnormal samples (x axis).

Figure 3. The degradation plots, based on ordering probes within a probe-set according to their 3� position and then combining the signal from similarlylocated probes across the array. Each line corresponds to an array, red forFFPE and blue for FF, and the slope of its trend indicates potential RNAdegradation and/or inefficient labeling.

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fected nonuniformly, with some genes’ expression levelsbeing compromised more than others. This observation isfurther strengthened by determining the relationship be-tween median and maximum contrast within a probeset(Figure 4C), which shows a tight linear dependence in-dicating that the most compromised probes belong toprobesets with high median contrast. Collectively, theseresults show that whereas probe behavior in FFPE versusFF varies from probeset to probeset, probe behavior isconsistent within probesets and therefore specific genes.

To further study probesets with the poorest probe per-formance in FFPE tissues we analyzed a subset of probe-sets that showed a minimum probe contrast of �2 withineach probeset [i.e., all of the probe intensities withinthese probesets are at least 4 times smaller (on thenatural scale) in FFPE samples versus FF samples (sup-plemental Figure S2 at http://jmd.amjpathol.org)]. To de-termine the reproducibility of observed FFPE bias acrossdifferent tissue types, probe contrast metrics were calcu-

lated for lung and colon quadsets. These quadsets weregenerated from RNA extracted using different protocolsfrom the Wilm quadset samples but used the same am-plification protocol. We observe that probe contrast met-ric in lung and colon follow a characteristic asymmetricaldensity shape, as observed in kidney tissue (supplemen-tal Figure S3 at http://jmd.amjpathol.org). Moreover, de-spite differences in expression between tumor and nor-mal pairs in quadsets and between tissues, mediancontrast within probeset has a high degree of similarityand concordance across all tissues (Figure 5A). As de-picted in the Venn diagram (Figure 5B), more than 3000probesets had a median contrast of �2 in all three quad-sets (supplemental Table S2 at http://jmd.amjpathol.org).This represents approximately 15% of the probe sets incommon between the two platforms studied (AffymetrixU133A and Plus2.0 GeneChips). At least 1/3 of theprobesets with a median contrast of �2 overlap in allthree tissue quadsets (86% of lung, 36% of colon, and33% of Wilm’s median contrast probesets). In two-waycomparisons, between 78 and 95% of the median con-trast �2 probesets were detected in at least two of thethree tissue quadsets.

Given the overlap between quadset tissues and thestrong bias of specific genes across multiple tissues, wenext used overrepresentation analysis of gene ontologycategories and biological pathways46 to determinewhether any of these genes are involved in commonbiological processes, cellular localization, or share simi-

Figure 4. A: Density function of probe contrast (i.e., median pairwisedifference) in log2 FF and FFPE signal across normal (i.e., nontumorous)samples. Similar contrast function was observed in tumor samples. B: Densityfunction of median probe contrast within probesets. C: Scatterplot of medianversus maximum probe contrast within a probeset.

Figure 5. A: Density of median probe contrast within a probeset in kidney,lung, and colon tissues. B: Venn diagram of median within-probeset contrastlarger than two in kidney, lung, and colon data sets.

Microarray Expression Profiling in FFPE 413JMD July 2010, Vol. 12, No. 4

lar molecular functions. The subset of probesets thatshowed the most pronounced FFPE bias (i.e., contrastmetric of �2 in all three quadsets) revealed highly signif-icant enrichment of specific groups of genes (supple-mental Table S3 at http://jmd.amjpathol.org). Most signif-icant biological categories were genes involved in proteinbiosynthesis such as translation initiation, elongation, andribosomal subunit genes. Seventy-four percent of theGene Ontology cellular component category of ‘cytosolicribosome’ (57/77 genes with this tag in GO) were over-represented in this list (P � 9e-25). In addition, we foundenrichment of specific protein domains such as the ubiq-uitous eukaryotic RNA recognition motif (RRM) and nu-cleotide-binding, �-� plait domains, which are present inproteins that bind single stranded RNA molecules suchas splicing factors.53 Other intriguing gene ontology cat-egories revealed in this analysis included molecularchaperonins involved in unfolded protein binding andgenes localized to the endoplasmic reticulum and innermitochondrial membranes. The overrepresentation anal-ysis suggests that despite differences in tissue type,sample processing, and RNA extraction there exists anonrandom bias in FFPE-derived expression profiles.Therefore, investigators can expect to observe depletedexpression signal from transcripts associated with RNAbinding, especially the ribosomal machinery itself, per-haps because these RNA transcripts are more tightlybound to protein (than other RNA transcripts) when thesamples are submerged in the formalin fixative and/or arechemically cross-linked to a greater extent than othertranscripts.

Gene-Level Analysis

Next, we study gene-level data to determine the capabil-ities of FFPE microarray data in studying differential geneexpression. Probe signal intensities were quantile nor-malized and gene-level expressions were summarizedusing the robust multiarray average (RMA), where probe-modeling was performed independently for FFPE and FFsamples. Independent t tests were performed at the genelevel to compare Wilm’s tumor and normal kidney ex-pressing in FFPE and FF samples (supplemental TableS4 at http://jmd.amjpathol.org). Considerable overlap wasobserved between differentially expressed gene lists de-tected in FFPE and FF under widely used and nonstrin-gent mean-fold difference and t test P value thresholds(FD �2 & P value �0.01), the overlap representing morethan 50% of the total number of differentially expressedgenes in FFPE tissues (Figure 6A). Although the overlapobserved is significant, the concordance in magnitude offold change was only marginal with fold changes varyingsignificantly in FF and FFPE comparisons (Figure 6B).Importantly, we find that proportion of genes concor-dantly over- or underexpressed in both FFPE and FFtissues increases significantly as a function of observedmean-fold difference between tumor and normal (Figure6C). For example, when the threshold for differential ex-pression was increased to detect genes differentially ex-pressed with a mean fold-difference of �5, the concor-

dance between FFPE and FF was 90%. Recognizing thefact that FF data are of higher quality and considered asgold standard, we calculated the degree of overlap be-tween FF and FFPE data at different P value cut-offs forFFPE tumor/normal comparison while keeping FF cut-offat non-stringent thresholds stated above (Figure 7). Wefind that concordance increases significantly with morestringent P value cut-off while number of differentiallyexpressed genes is reduced. For example, at P valuecut-off of 0.005, we report only 60% of differentially ex-pressed genes compared with nonstringent cut-off, butthe overlap between FFPE and FF tumor/normal compar-ison increases significantly to 80%. Therefore, applyingmore stringent thresholds than are customary for freshfrozen samples studies (e.g., 10-fold increase in P valuestringency) will likely decrease the false-positive rate and

Figure 6. A: Venn diagram, showing concordance of differential expressionin gene expression of Wilm’s tumor and normal adjacent kidney in FF andFFPE assays. B: Scatterplot of mean fold-difference between normal kidneyand Wilm’s tumor found significant in both FF and FFPE comparisons. C:Percent concordance in mean fold-difference between FF and FFPE as afunction of mean fold-difference between Wilm’s tumor and normal kidneyin respective FF and FFPE assays.

414 Abdueva et alJMD July 2010, Vol. 12, No. 4

yield higher accuracy for ‘noisier’ FFPE sample expres-sion profiling studies.

Discussion

This study protocol using recent improvements to nucleicacid extraction,12,16 including more effective reversal ofthe chemical cross-links in FFPE RNA54 and the use of acombined random and poly-A priming approach (i.e.,Ovation FFPE system20), demonstrates that whole-tran-scriptome microarray data of sufficient quality for down-stream analysis can be obtained from FFPE specimens.Additionally, using such a protocol for whole transcrip-tome amplification avoids the inherent bias of usinggene-specific priming2,30,33,37,40,55,56 to look at only asmall subset of the transcriptome and greatly enablesstudies from samples with low RNA yields (e.g., FFPEcore biopsies).

To understand the comparability of FFPE and FF ex-pression profiles, we studied lists of differentially ex-pressed transcripts in the tumor and normal adjacenttissue across various tissue types. We looked at individ-ual probe performance and confirmed that in FFPE sam-ples, probe performance was more significantly affectedby proximity to the 3� and declined sharply toward 5� end,which could reflect the fact that the FFPE preservationinduces some 5� to 3� chemical degradation to RNA fromthe fixation and paraffin embedding process itself (e.g.,chemical oxidation and heating).26,28 We observed asignificant overlap between differentially expressed genelists detected in FFPE and FF tumor normal pairs usingstandard fold change/P value thresholds. The concor-dance increases significantly when a higher fold changeand P value cut-off is applied, resulting in smaller numberof genes reported but gauged to FF samples as bench-marks, presumably fewer false positives. Finally, wefound that independent of both RNA extraction protocol(column or magnetic bead based) and tissue type (e.g.,Wilm’s, colon and lung) we found a nonrandom system-

atic gene-specific ‘FFPE bias.’ Some of this bias may beaccounted for by differences in amplification efficiency ofRNAs extracted from FF and FFPE samples (yields were�9-fold greater in FF when adjusting for input RNAamount). Intriguingly, the genes most affected by theFFPE bias (i.e., contrast metric �2) were overrepre-sented with gene ontology terms for proteins involved in‘translation initiation,’ ‘translation elongation,’ ‘RNA-pro-tein complex assembly,’ ‘structural constituent of ribo-some,’ and ‘RNA binding.’ Further investigations with theAffymetrix Human Exon 1.0 ST microarrays are underwayto better characterize the ‘FFPE’ bias. These microarrayshave probes arranged throughout the length of the tran-script and are better suited for evaluating the effects oftranscript length and location of probes in the RNA tran-script than the 3�-biased microarrays used in the presentstudy.

Although, it remains unclear why the detection of tran-scripts representing translational machinery protein com-ponents is most compromised in FFPE sample analysis,these data suggest that these RNA transcripts were mosttightly bound to proteins at fixation, which could makecross-linking or chemical modifications to RNA less effi-ciently reversed in a nonrandom and reproducible man-ner in multiple tissues. Although further experimentationis required on a larger sampling of FFPE specimens (e.g.,additional tissue types and representation of older spec-imens), these efforts will be useful for devising reliable‘mask’ sets to filter out probesets most affected by theFFPE bias before downstream analysis to minimize falsediscovery.

Limitations of FFPE RNA in terms of nucleic acid integ-rity and suitability as template for amplification are out-weighed by the benefits of having almost unlimited sup-ply of study material for retrospective analyses. Withseveral commercially available kits for nucleic acid ex-traction that appear to work equally well,16 and a robustRNA amplification kit such as the NuGEN Ovation FFPEsystem,20,41 we achieved functional, reproducible, andreliable microarray data from FFPE material from multipletissues.

Conclusions

Despite the highly cross-linked and degraded nature ofRNA in FFPE tissue blocks, functional gene array datacan be routinely generated using commercially availableRNA extraction and amplification kits.

Acknowledgments

We thank Drs. Victor Sementchenko, Gianfranco de Feo,and Hana Gage (NuGEN Technologies, Inc.) for lung andcolon quadset data as well as insights and valuablediscussions, and Drs. Hiroyuki Shimada, Ignacio Gonza-lez, and Minerva Mongeotti (CHLA Department of Pathol-ogy) for providing Wilm’s tissue samples used in thisstudy and invaluable pathology expertise. We acknowl-edge the late Dr. James W. Jacobson, Ph.D., of the

Figure 7. A side-by-side view of P value threshold (x axis) in FFPE tumor/normal comparison versus concordance with FF differentially expressedgene list under nonstringent cut-offs (bottom) and fraction of genes reported(top).

Microarray Expression Profiling in FFPE 415JMD July 2010, Vol. 12, No. 4

diagnostic biomarkers and technology branch of the Na-tional Cancer Institute.

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