Ffpe white paper

Biomarker Discovery from Paraffin Embedded SamplesSuccessful Principles for Addressing FFPE-associated Real-time PCR Challenges

Yexun Wang Ph.D., Emi Arikawa Ph.D., Shankar Sellappan Ph.D., and Li Shen Ph.D.

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Abstract: Traditional histopathological analysis of formalin-fixed paraffin-embedded (FFPE) tissue was capable of providing expression status on a few targeted proteins or genes. Although fixed samples could serve as valuable sources for biomarker identification studies, much of their value is hidden at the molecular level. Analysis of gene expression profiles using genetic material contained within FFPE samples is further limited by the chemical modifications that decrease the quality and availability of recovered RNA for molecular analysis. In this article, we present the successful principles and results of the new RT2 FFPE PreAMPTM technology that combines a novel RNA extraction protocol that exploits thermodynamic and structural characteristics of FFPE RNA with a powerful preamplification step of a pathway-focused set of genes for real-time gene expression analysis. With this new technology, FFPE RNA can finally serve as a powerful tool for restrospective and future gene profiling studies for biomarker identification.

IntroductionBiomarkers, as defined by the NIH Working Group, are “ a character-istic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” [1]. Since all biological processes are carried out by genes at the molecular level, gene expression profiles, as measured by RNA quantiation through real-time PCR analysis, have the potential to serve as predictors of diseased conditions or response to therapeutic interventions. Biomarker discovery through gene expression analysis has been widely used in basic and clinical research, with freshly prepared tissue or cultured cells most often used as starting material. However, when fresh samples are not readily available, as often the case with human tissue samples, researchers rely on previously prepared, archived samples.

Most archived samples have been prepared in neutrally-buffered formalin solutions and embedded in paraffin. Paraffin blocks serve as excellent substrates for morphological and immunological studies; unfortunately their use in molecular and genomic studies has proven challenging. Technical obstacles hindering the exploitation of these samples can be attributed to extensive chemical cross-linkings among proteins, DNA, and RNA, as well as from RNA degradation. Cross-linking is primarily caused by formaldehyde fixation, which adds mono-methylols (-CH2OH) to the amino groups of all four nucleic acid bases and proteins, resulting in base modifications, methylene bridging (N-CH2-N) between neighboring bases, and DNA-protein and RNA-protein cross-linkages [2]. In addition, RNA degradation begins with tissue anoxia during fixation and continues throughout the storage period [3, 4]. These damages compromise data consistency and sensitivity in molecular studies.

TECHNICAL ARTICLE

In spite of these difficulties, using formalin-fixed paraffin-embedded (FFPE) samples in gene expression analysis studies also presents great advantages. First, FFPE samples are available in vast amounts and are readily accessible. It is estimated that there are over 400 million FFPE samples stored worldwide, with the number of samples expected to increase annually [5]. Second, almost all FFPE samples have associated pathological and clinical annotations. Although faced with similar issues like institutional approval, sample quantity acquisition, and sample heterogeneity, it is still much easier to gather FFPE samples with different clinical outcomes as compared to collecting fresh samples. This makes association and classification studies much easier. Thirdly, applying biomarkers developed using FFPE samples fits well with the traditional workflow of clinical practices.

Contents

Introduction ........................................................................................1Experimental Protocol .......................................................................2 Component 1: FFPE RNA Extraction .......................................2 Component 2: Reverse Transcription .....................................3 Component 3: qPCR Primer Design .........................................4 Component 4: PreAMP Signal Amplification .........................5FFPE PCR Array Performance ..........................................................5Application Examples .............................................................................6Summary ...................................................................................................6References ..............................................................................................7

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Figure 1: Expression rank order of 84 genes (Cancer PathwayFinder PCR Array) in RNA extracted from one 20 mm section of a 5-year old normal human intestine FFPE block using the RT2 FFPE RNA Extraction Kit. MicroRNA data is not shown.

Reverse Transcription

Real-time PCR is the simplest and most accurate method to study gene expression and validate expression biomarkers. Before RNA can be analyzed by real-time PCR, it has to be converted to cDNA in a reverse transcription step. The common strategy for reverse transcription uses the MMLV reverse transcriptase after RNA is primed with random hexamers or oligo dT primers. These two priming strategies are equally efficient for most RNA when fresh samples are used. However, when RNA from FFPE sample is used, neither strategy can deliver reverse transcription efficiencies high enough for later processes. Since RNA from FFPE samples is highly fragmented, the intended amplicon region in PCR is most likely disconnected from the poly(A) tail. Thus the oligo dT primer based reverse transcription cannot reach the amplicon region, which leads to lower PCR detection (Figure 2). For the same reason, most of the random hexamers primed upstream of the target region cannot convert RNA amplicons into cDNAs. It is expected that some random hexamers primed immediately upstream of the target region will convert RNA amplicons into cDNA, thereby detectable in PCR. However, the very low concentration of those specific hexamers (the concentration of one particular hexamer is only 1/4096th, or 0.02%, of the total concentration) significantly limits priming efficiency, as it is known that oligonucleotide annealing is highly concentration- dependent. From this discussion, it becomes obvious that using a single gene-specific primer closely upstream of the target region will have the highest efficiency in converting an RNA amplicon into cDNA from FFPE samples.

Figure 2: A) For intact RNA from fresh samples, any of the primers (1, 2, 3, or 4) can generate cDNAs which contain the target amplicon (in gray). B) For fragmented RNA from FFPE samples, only primer 4 can generate cDNA which contains the target amplicon.

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4 RT2 FFPE RNA Extraction and PreAMPlification

To demonstrate that gene-specific primers will have a higher RT efficiency than random hexamers for FFPE RNA, we designed gene-specific RT primers for 89 genes present on the human Cancer PathwayFinderTM PCR array (PAHS-033). We pooled 89 RT primers together (0.225 uM each) and applied them to the product of the RT reaction of RNA extracted from one 20 mm section of a five-year old normal human spleen FFPE block, followed by RT2 PCR Array analysis. For all the genes tested, the Ct values decreased an average of 3.3 cycles, i.e. a ~10X increase in sensitivity (Figure 3). While performing the analysis, we also noticed that some assays showed non-specific amplification. This is likely due to non-specific primer annealing events resulting from the low temperatures employed during the RT step; other groups have reported similar problems [6]. While this problem with non-specific amplification can be solved through individual assay optimization, it limits the genome-wide application of this approach for gene expression analysis on FFPE samples. Therefore we sought alternative strategies to improve assay sensitivity.

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Figure 3: Gene Specific RT Primer Improves Reverse Transcription Efficiency. 500 ng RNA extracted from one 20 mm section of a five-year old human spleen FFPE block were reversed transcribed using either random hexamers or a pool of 89 gene specific primers located upstream of the PCR amplicons for the human Cancer PathwayFinderTM PCR Array. Equal volumes of cDNA were later used on the same PCR array. Shown here are the box plots of raw Ct values for each condition.

qPCR Primer Design

The sizes of RNA fragments extracted from FFPE samples usually range from 50 to 300 nt, with most around 100 nt. This average size correlates well with the age of the block, with older blocks yielding shorter RNA fragments [4]. This indicates RNA fragmentation continues even after the sample is completely fixed and embedded. These short RNA fragments necessitate specialized PCR primer designs, with an important caveat being the PCR amplicon should not be much longer than the average size of the RNA fragments. This stipulation can be explained by recognizing that there is less probability to amplify a sequence fragment with a size much longer than the average size of all RNA fragments. For example, when initial RNA samples are randomly fragmented, the percentage of 300 ntlong fragments is much lower than that of 100 nt long fragments, when the average size of fragments is 100 nt. It can be inferred that shorter amplicon designs should yield better sensitivity in real-time PCR analysis for FFPE samples.

To test this hypothesis, we selected 22 genes which have existing RT2 primer designs with amplicon sizes longer than 130 bp. The average size was 162 bp, with the longest at 191 bp. We then redesigned primers for these genes so the new amplicon sizes range from 53 to 81 bp (average = 67 bp), and confirmed the new designs work as efficiently as the old ones. We then generated cDNA from a normal five-year old human spleen FFPE block and compared the real-time PCR results of these two sets of primers. As expected, the shorter amplicon designs, in general, yielded Ct values much lower than the longer designs for the same amount of cDNA (Figure 4A). The average Ct gain is 2.8 cycles, with the largest gain of over 5 cycles for some genes. Thus, by simply designing shorter amplicons, we can increase PCR sensitivity by almost 10-fold. In contrast, shorter amplicon designs do not improve Cts for RNAs of high quality (Figure 4B).

Figure 4: Primers Designed for Shorter PCR Amplicons Generally Yield Lower Cts for Fragmented RNA. A) 500 ng RNA extracted from one 20 mm section of a five-year old human spleen block was reversed transcribed using random hexamers. Equal amounts of cDNA were used on two customized RT2 PCR Arrays. For one array, primers were designed to generate regular PCR amplicons (132 bp to 191 bp). For the second array, primers were designed to specifically generate shorter amplicons (53 to 81 bp) for the same genes. Shown here are the before and after raw Ct values for each gene. B) cDNA transcribed from 1 ug high quality human universal RNA was used on 45 genes (from PAHS-060) with two different primer designs. For one array, primers were designed to generate regular PCR amplicons (130 bp to 195 bp). For the second array, primers were designed to specifically generate shorter amplicons (50 to 89 bp) for the same genes. Shown here are the before and after raw Ct values for each gene.

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PreAMP Signal Amplification When scientists try to look at the expression profile of multiple genes on the same sample by qPCR, the easiest approach is to run multiple individual qPCR assays corresponding to those genes. This creates the problem of sample division or dilution. For example, if one tries to use qPCR to study 100 genes including gene A using 1 mg of RNA, he would have to divide 1 mg RNA into 100 assays, one assay for each gene. If gene A only has 100 copies in 1 mg RNA, then its copy number in the individual assay designated for gene A will drop down to one, which cannot be reliably detected. From another perspective, the reverse transcription reaction usually has to be diluted before cDNA can be used in qPCR due to the interference of RT chemistry with qPCR chemistry. This also causes the number of original gene copies to be diluted and fall off the qPCR detection range. This problem can be avoided by starting with much more RNA in the experiment. However, this is not always feasible, especially with FFPE samples.

Another discouraging factor for FFPE samples is that while one can extract RNA in the microgram range, due to damages, the amount of RNA fragments which can serve as effective templates in RT-qPCR is much less. A pre-amplification approach has been used to enhance detection of RNA extracted from small fresh samples (e.g. from LCM, FNB, cell sorting), and it is expected that pre-amplification can also be used to boost the overall qPCR assay sensitivity in FFPE samples.

FFPE PCR Array Performance

In order to demonstrate the value of pre-amplification when working with FFPE samples yielding low effective RNA template, we have developed the RT2 FFPE PreAMP technology. When used together with the RT2 PCR array platform, multi-gene expression analysis on FFPE samples can be successfully performed. After RNA extracted from FFPE samples is first converted to cDNA, a quarter of the cDNA is used in a tightly-controlled multiplex PCR reaction comprised of a mixture of primers specific to those genes on a particular PCR Array. After the PreAMP reaction is complete and excess primers are removed by enzyme digestion, the pre-amplified cDNA is distributed across a PCR array for individual gene detection.

Compared to the previously discussed strategies for RT-qPCR primer design, the FFPE PreAMP technology yields the best improvement in sensitivity. Depending on the cycling numbers of the multiplex PCR, qPCR assay sensitivity can be easily improved by at least 100-fold or more for FFPE RNA. As an example, we extracted RNA from a five-year old normal human intestine FFPE block, converted it into cDNA, and ran the cDNA on the RT2 Human Cancer PathwayFinder PCR Array with or without the FFPE PreAMP technology. The results from our regular PCR array protocol without FFPE PreAMPlification showed 18 genes (20% of the array) as “absent” due to Ct values greater than 35. On the other hand, addition of the FFPE PreAMP technology greatly improved the detection of those 18 genes (Figure 5), increasing the positive call rate to 100%.

Figure 5: PreAMPTM Process Makes More Genes Detectable in FFPE Samples. RNA extracted from a five-year old human intestine FFPE block was reverse transcribed with or without the PreAMP process. Both cDNAs were run on Human Cancer PathwayFinder arrays (PAHS-033). Shown here are 18 genes which are called “Absent” using standard RT-PCR array procedures.

Any signal or sample amplification approach needs to have high fidelity in order to maintain the original profile of the genes being studied. We evaluated the consistency of the PreAMP technology and the fidelity of gene expression profiles between the original sample and samples undergoing PreAMPlification. The PreAMPlified samples showed very high consistency between two independent runs (Figure 6).

Figure 6: Inter-assay Consistency of the PreAMP Process. Two independent PreAMP reactions were set up using the same amount of mouse cDNA. Mouse Toll-Like Receptor Signaling Pathway PCR Array (PAMM-018) was used to evaluate the inter-run consistency. DCt (Ct(GOI) – Ct(HKG)) of 89 genes from two separate runs shows R > 0.99.

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Application Examples

To evaluate the biological significance of the fold changes, we used RNAs from human spleen and intestine FFPE blocks and examined the differential expression of 89 genes in the Human Cancer PathwayFinder PCR Array. We compared the results obtained using our PCR Array protocol with those incorporating the FFPE PreAMP protocol. When looking at those genes which are reliably detected as “present” (Ct < 31.5) with the PCR array, their raw Ct values without PreAMP are much higher, but highly correlated with the raw Ct values after the PreAMP process (Figure 7).

Figure 7: Highly comparable Ct values between PreAMP and non-PreAMP FFPE Spleen RNA. RNA extracted from human spleen FFPE block was reverse transcribed with or without the PreAMP process. Both cDNAs were run on the Human Cancer PathwayFinder Array. A) Shown here is the scatter plot of raw Cts. B) Every assay is improved by approximately the same number of Cts after the PreAMP process. Each pair of data points (PreAMP and no PreAMP) represent a different gene. Only genes which have Cts lower than 31.5 in the reaction without PreAMP are shown here.

The fold changes, or the difference in gene expression between spleen and intestine samples, also correlate well between the two protocols (Figure 8). This suggests two conclusions that establish full confidence in providing biologically relevant and accurate data. First, the original gene expression profile is faithfully maintained during the PreAMP process. In a population of genes with both high and low expressers, preamplification amplifies all genes equally such that overall sensitivity of detection increases, while the differential patterning is maintained. Second, the PreAMP technology allows one to compare multiple genes simultaneously, some of which cannot be detected by traditional means.

Figure 8: Comparable Fold Change Results Between PreAMP and Unamplified FFPE Samples. RNA extracted from human spleen and intestine FFPE blocks were reverse transcribed with or without the PreAMP process. All four cDNAs were run on PAHS-033. Shown here is the DDCt comparison. All the genes show significantly changed in original FFPE samples were also detected as significantly changed in the same direction after the PreAMP process (in white regions A and B). Although some genes showed fold change values (as identified by the arrow) in the opposite directions, the values were not significant in either condition. Only genes which have Cts lower than 31.5 in both unamplified spleen and intestine samples are shown here.

Summary

The RT2 FFPE PreAMP technology solves the major challenge in real-time PCR based gene expression analysis using FFPE samples. It greatly improves the detection limit of FFPE RNA. It also allows for easy integration with the popular RT2 Profiler PCR Array platform for analysis of a pathway-focused set of genes.

As millions of FFPE samples are currently stored in archives with thorough pathological information and clinical history of the patient available, using them as a source for biomarker discovery has gained increasing importance. With the improvement in assay design and the introduction of a novel workflow tailored to the nature of these samples, we can now expect to have better utilization of these valuable resources. We believe and demonstrate that our complete FFPE RNA extraction protocol, FFPE PreAMP technology and PCR Array system will be a valuable tool in this quest.

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References

1. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Biomarkers Definitions Working Group. Clin Pharmacol Ther. 2001 Mar;69(3):89-95.

2. Analysis of chemical modification of RNA from formalin-fixed samples and optimization of molecular biology applications for such samples. Masuda N, Ohnishi T, Kawamoto S, Monden M, Okubo K. Nucleic Acids Res. 1999 Nov 15;27(22):4436-43.

3. Determinants of RNA quality from FFPE samples. von Ahlfen S, Missel A, Bendrat K, Schlumpberger M. PLoS One. 2007 Dec 5;2(12):e1261.

4. Measurement of gene expression in archival paraffin-embedded tissues: development and performance of a 92-gene reverse transcriptase-polymerase chain reaction assay. Cronin M, Pho M, Dutta D, Stephans JC, Shak S, Kiefer MC, Esteban JM, Baker JB. Am J Pathol. 2004 Jan;164(1):35-42.

5. Applying aCGH to molecular diagnostics. Kathy Liszewski. GEN 2008 May 15; 28(10).

6. Enhanced detection of RNA from paraffin-embedded tissue using a panel of truncated gene-specific primers for reverse transcription. Mikhitarian K, Reott S, Hoover L, Allen A, Cole DJ, Gillanders WE, Mitas M. Biotechniques. 2004 Mar;36(3):474-8.

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