Accurate data processing improves the reliability of Affymetrix gene expression profiles from FFPE samples

Fundamentals of RNA Analysis on Biobanked Specimens

Compared to DNA, analysis of RNA is one step closer on the central dogma of biology to assessing cellular function. This makes it an extremely valuable target for research and clinical testing in nearly all areas of molecular biology. Most RNA molecules are ephemeral by nature. They exist as temporary intermediates, ostensibly enabling data transfer between the genome and the organism. Their ribose backbone renders them sensitive to simple degradation over time and they are the target molecule for numerous and abundant ribonucleases which have evolved to chop them to pieces with extreme efficiency. At the biochemical level, this means that they degrade rapidly in most physiological and laboratory conditions and are thus challenging to study. When considering specimen banking, it is critical to keep this reality in mind, as some commonly used banking modalities will not adequately preserve the relevant RNA molecules in a measureable state.In this chapter, we explore the broad range of RNA testing methodologies in current use, with particular focus on how specimen preparation impacts analysis. Following an overview in the introduction, Subheading 2 covers the major specimen types amenable to RNA analysis in the context of biobanking. Subheading 3 discusses the applications of various RNA analysis modalities to research and clinical testing.

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

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.

Systematic evaluation of RNA quality, microarray data reliability and pathway analysis in fresh, fresh frozen and formalin-fixed paraffin-embedded tissue samples

Formalin-fixed paraffin-embedded (FFPE) tissues are valuable resources commonly used in pathology. However, formalin fixation modifies nucleic acids challenging the isolation of high-quality RNA for genetic profiling. Here, we assessed feasibility and reliability of microarray studies analysing transcriptome data from fresh, fresh-frozen (FF) and FFPE tissues. We show that reproducible microarray data can be generated from only 2 ng FFPE-derived RNA. For RNA quality assessment, fragment size distribution (DV200) and qPCR proved most suitable. During RNA isolation, extending tissue lysis time to 10 hours reduced high-molecular-weight species, while additional incubation at 70 °C markedly increased RNA yields. Since FF- and FFPE-derived microarrays constitute different data entities, we used indirect measures to investigate gene signal variation and relative gene expression. Whole-genome analyses revealed high concordance rates, while reviewing on single-genes basis showed higher data variation in FFPE than FF arrays. Using an experimental model, gene set enrichment analysis (GSEA) of FFPE-derived microarrays and fresh tissue-derived RNA-Seq datasets yielded similarly affected pathways confirming the applicability of FFPE tissue in global gene expression analysis. Our study provides a workflow comprising RNA isolation, quality assessment and microarray profiling using minimal RNA input, thus enabling hypothesis-generating pathway analyses from limited amounts of precious, pathologically significant FFPE tissues.

Robust transcriptional tumor signatures applicable to both formalin-fixed paraffin-embedded and fresh-frozen samples

Formalin-fixed paraffin-embedded (FFPE) samples represent a valuable resource for clinical researches. However, FFPE samples are usually considered an unreliable source for gene expression analysis due to the partial RNA degradation. In this study, through comparing gene expression profiles between FFPE samples and paired fresh-frozen (FF) samples for three cancer types, we firstly showed that expression measurements of thousands of genes had at least two-fold change in FFPE samples compared with paired FF samples. Therefore, for a transcriptional signature based on risk scores summarized from the expression levels of the signature genes, the risk score thresholds trained from FFPE (or FF) samples could not be applied to FF (or FFPE) samples. On the other hand, we found that more than 90% of the relative expression orderings (REOs) of gene pairs in the FF samples were maintained in their paired FFPE samples and largely unaffected by the storage time. The result suggested that the REOs of gene pairs were highly robust against partial RNA degradation in FFPE samples. Finally, as a case study, we developed a REOs-based signature to distinguish liver cirrhosis from hepatocellular carcinoma (HCC) using FFPE samples. The signature was validated in four datasets of FFPE samples and eight datasets of FF samples. In conclusion, the valuable FFPE samples can be fully exploited to identify REOs-based diagnostic and prognostic signatures which could be robustly applicable to both FF samples and FFPE samples with degraded RNA.

RNA-seq transcriptome analysis of formalin fixed, paraffin-embedded canine meningioma

Meningiomas are the most commonly reported primary intracranial tumor in dogs and humans and between the two species there are similarities in histology and biologic behavior. Due to these similarities, dogs have been proposed as models for meningioma pathobiology. However, little is known about specific pathways and individual genes that are involved in the development and progression of canine meningioma. In addition, studies are lacking that utilize RNAseq to characterize gene expression in clinical cases of canine meningioma. The primary objective of this study was to develop a technique for which high quality RNA can be extracted from formalin-fixed, paraffin embedded tissue and then used for transcriptome analysis to determine patterns of gene expression. RNA was extracted from thirteen canine meningiomas-eleven from formalin fixed and two flash-frozen. These represented six grade I and seven grade II meningiomas based on the World Health Organization classification system for human meningioma. RNA was also extracted from fresh frozen leptomeninges from three control dogs for comparison. RNAseq libraries made from formalin fixed tissue were of sufficient quality to successfully identify 125 significantly differentially expressed genes, the majority of which were related to oncogenic processes. Twelve genes (AQP1, BMPER, FBLN2, FRZB, MEDAG, MYC, PAMR1, PDGFRL, PDPN, PECAM1, PERP, ZC2HC1C) were validated using qPCR. Among the differentially expressed genes were oncogenes, tumor suppressors, transcription factors, VEGF-related genes, and members of the WNT pathway. Our work demonstrates that RNA of sufficient quality can be extracted from FFPE canine meningioma samples to provide biologically relevant transcriptome analyses using a next-generation sequencing technique, such as RNA-seq.

Comparison of Nanostring nCounter® Data on FFPE Colon Cancer Samples and Affymetrix Microarray Data on Matched Frozen Tissues

The prognosis of colorectal cancer (CRC) stage II and III patients remains a challenge due to the difficulties of finding robust biomarkers suitable for testing clinical samples. The majority of published gene signatures of CRC have been generated on fresh frozen colorectal tissues. Because collection of frozen tissue is not practical for routine surgical pathology practice, a clinical test that improves prognostic capabilities beyond standard pathological staging of colon cancer will need to be designed for formalin-fixed paraffin-embedded (FFPE) tissues. The NanoString nCounter^® platform is a gene expression analysis tool developed for use with FFPE-derived samples. We designed a custom nCounter^® codeset based on elements from multiple published fresh frozen tissue microarray-based prognostic gene signatures for colon cancer, and we used this platform to systematically compare gene expression data from FFPE with matched microarray array data from frozen tissues. Our results show moderate correlation of gene expression between two platforms and discovery of a small subset of genes as candidate biomarkers for colon cancer prognosis that are detectable and quantifiable in FFPE tissue sections.

Subtype-Specific Metagene-Based Prediction of Outcome after Neoadjuvant and Adjuvant Treatment in Breast Cancer

PURPOSE

In spite of improvements of average benefit from adjuvant/neoadjuvant treatments, there are still individual patients with early breast cancer at high risk of relapse. We explored the association with outcome of robust gene cluster-based metagenes linked to proliferation, ER-related genes, and immune response to identify those high-risk patients.

EXPERIMENTAL DESIGN

A total of 3,847 publicly available gene-expression profiles were analyzed (untreated, N = 826; tamoxifen-treated, N = 685; chemotherapy-treated, N = 1,150). Genes poorly performing in formalin-fixed samples were removed. Outcomes of interest were pathologic-complete response (pCR) and distant metastasis-free survival (DMFS). In ER(+)HER2(-), the proliferation and ER-related metagenes were combined to define three risk groups. In HER2(+) and ER(-)HER2(-) risk groups were defined by tertiles of an immune-related metagene.

RESULTS

The high-proliferation/low-ER group of ER(+)HER2(-) breast cancer had significantly higher pCR rate [OR, 5.01 (1.76-17.99), P = 0.005], but poorer outcome [HR = 3.73 (1.63-8.51), P = 0.0018] than the low-proliferation/high-ER. A similar association with outcome applied to patients with residual disease (RD) after neoadjuvant chemotherapy (P = 0.01). In ER(-)HER2(-) and HER2(+) breast cancer, immune metagene in the high tertile was linked to higher pCR [33.7% vs. 11.6% in high and low tertile, respectively; OR, 3.87 (1.79-8.95); P = 0.0009]. In ER(-)HER2(-), after adjuvant/neoadjuvant chemotherapy, 5-year DMFS was 85.4% for high-tertile immune metagene, and 43.9% for low tertile. The outcome association was similar in patients with RD (P = 0.0055). In HER2(+) breast cancer treated with chemotherapy the association with risk of relapse was not significant.

CONCLUSIONS

We developed metagene-based predictors able to define low and high risk of relapse after adjuvant/neoadjuvant therapy. High-risk patients so defined should be preferably considered for trials with investigational agents.

Use of formalin-fixed paraffin-embedded samples for gene expression studies in breast cancer patients

To obtain gene expression profiles from samples collected in clinical trials, we conducted a pilot study to assess feasibility and estimate sample attrition rates when profiling formalin-fixed, paraffin-embedded specimens. Ten matched fresh-frozen and fixed breast cancer samples were profiled using the Illumina HT-12 and Ref-8 chips, respectively. The profiles obtained with Ref 8, were neither technically nor biologically reliable since they failed to yield the expected separation between estrogen receptor positive and negative samples. With the use of Affymetrix HG-U133 2.0 Plus chips on fixed samples and a quantitative polymerase chain reaction -based sample pre-assessment step, results were satisfactory in terms of biological reliability, despite the low number of present calls (M = 21%±5). Compared with the Illumina DASL WG platform, Affymetrix data showed a wider interquartile range (1.32 vs 0.57, P<2.2 E-16,) and larger fold changes. The Affymetrix chips were used to run a pilot study on 60 fixed breast cancers. By including in the workflow the sample pre-assessment steps, 96% of the samples predicted to give good results (44/46), were in fact rated as satisfactory from the point of view of technical and biological meaningfulness. Our gene expression profiles showed strong agreement with immunohistochemistry data, were able to reproduce breast cancer molecular subtypes, and allowed the validation of an estrogen receptor status classifier derived in frozen samples. The approach is therefore suitable to profile formalin-fixed paraffin-embedded samples collected in clinical trials, provided that quality controls are run both before (sample pre-assessment) and after hybridization on the array.