1. Introduction
Clinical evaluation of tumors creates and uses primarily formalin-fixed and paraffin-embedded (FFPE) tissue [1]. Therefore, archives of FFPE tissue blocks represent an immeasurable resource for translational research. Analysis of molecular markers on FFPE materials, however, is challenging, since fixation treatment of the tissue causes degradation of nucleic acids [2] In particular, accurate transcriptome profiling on FFPE material has been an undefeatable obstacle for many years [3,4,5,6,7,8].
RNA sequencing (RNA-Seq) is the gold standard to measure whole transcriptomes of samples, but the reliability is limited with this method when FFPE samples are used. Recently, a new method for transcriptome analysis on FFPE tissue was established by HTG molecular, named the “HTG transcriptome profiling” (HTP). It is based on the Edge Seq System, which uses an extraction-free, nuclease protection assay. The analysis could be performed directly from FFPE slides in less than three days. Measuring 19,398 targets, this assay covers a large scale of relevant genes.
To test the robustness and reliability of this new method, we performed it on 64 patient samples, with 20 patients suffering from spindle cell sarcoma not otherwise specified (NOS) and 44 patients suffering from synovial sarcoma, which are characterized by the chromosomal translocation involving SS18 and either SSX1, SSX2, or rarely SSX4 [9]. These gene fusions orchestrate a specific transcriptional program, which determines morphologic appearance and biologic behavior of synovial sarcomas. There are, however, spindle cell sarcomas that share tumor morphology but behave clinically differently. Thus, we compared a genetically determined tumor entity with morphologic mimickers by applying a genome-wide transcriptional analysis.
2. Materials and Methods
2.1. Study Design
In this study, we aimed to test the robustness and reliability of the HTG transcriptome panel assay (HTG Molecular, Tucson, AZ, USA). Therefore, we used FFPE samples derived from 20 spindle cell sarcoma patients and 44 synovial sarcoma patients (see Figure 1). Patient samples exhibiting synovial sarcoma were pre-characterized by either Fluorescence-in-situ-hybridization (FISH) or the Archer® FusionPlex® Sarcoma panel (Invitae, San Francisco, CA, USA), a targeted sequencing method using the RNA of patient samples.
2.2. RNA-Isolation and Quantification for Archer® FusionPlex® Analysis
FFPE sections were stained with hematoxylin and eosin (H&E) and regions of interest were marked by two experienced pathologists for subsequent macrodissection. Macrodissection was performed on 2–6 10 µm thick FFPE sections and RNA was isolated by using the Maxwell® RSC RNA FFPE Kit (AS1440, Promega, WI, USA), according to the manufacturer’s instructions. RNA was eluted in 50 µL RNase-free water and stored at −40 °C.
Measurement of RNA concentration was performed via fluorometric quantification (Qubit, Thermo Fisher Scientific, Waltham, MA, USA) using the RNA High sensitivity assay kit according to the manufacturer’s instructions. One µL of each isolated RNA sample was applied for measurement.
2.3. Archer® FusionPlex® Targeted RNA Sequencing
The detection of fusions in the tested samples was performed by using the Archer® FusionPlex® Sarcoma (ARSAR) Panel (Invitae, San Francisco, CA, USA). A total of 150 ng of each RNA sample were applied for cDNA synthesis. cDNA libraries were prepared via anchored multiplex PCR according to the manufacturers’ instructions (Archer® FusionPlex® Protocol for Illumina®, AB0005-8-reactions), leaving out the PreSeq RNA QC assay. The Archer® FusionPlex® Protocol for Illumina® includes Archer® Universal RNA Reagent Kit for Illumina® (Catalog #SK-0093-8), Archer MBC adapters (Catalog #SA0043), and custom designed gene-specific primer (GSP) pool kit. The quality and quantity of cDNA libraries were assessed by using the D5000 Screen Tape assay on the Agilent 2200 TapeStation system (Santa Clara, CA, USA). Libraries were pooled (8–24 samples per run) and sequenced on an Illumina MiSeq® system (SY-410-1003, Illumina, San Diego, CA, USA).
2.4. RNA-Fusion Detection via Archer® FusionPlex® Analysis Software
Fusion detection of raw sequence data was analyzed by using the Archer® Analysis 6.2.1 User Software (Invitae, San Francisco, CA, USA). Fusions were verified bioinformatically by validation of the breakpoints.
2.5. Fluorescence In Situ Hybridization
Fluorescence in situ hybridization (FISH) was performed with formalin-fixed, paraffin-embedded, 1.5 µm thick sections. The tumor area was marked on the back of the slide by using a diamond pin. Sections were processed by using the VP2000 Processor (Abbott Laboratories, Chicago, IL, USA) as follows:
Day 1: Deparaffinization in xylene for 10 min for three times (3×), two times (2×) 5 min 100% ethanol, 96% ethanol (1 min), 80% ethanol (1 min), 70% ethanol (1 min). Slides were air-dried at 37 °C for 5 min and subsequently incubated in 0.2 M HCL (20 min), de-ionized (DI) water (3 min), 2× SSC wash buffer (3 min), pretreatment solution at 80 °C (30 min), DI water (1 min), 2× 5 min 2× SSC wash buffer, Protease I at 37 °C (90 min), 2× 5 min 2× SSC wash buffer, 4.5%-buffered formalin (10 min), 2× 5 min 2× SSC wash buffer, DI water (1 min), air-drying at 37 °C (5 min).
Subsequently, FISH-probe was applied on sections and covered with cover glasses. For detection of translocations involving the FUS gene, the ZytoLight® 2C SPEC FUS Dual Color Break Apart Probe was used (ZytoVision GmbH, Bremerhaven, Germany, Z-2130-50). Fixogum (Marabu, Tamm, Germany) was used to seal the cover glass. Slides were placed in a hybridizer (Agilent Dako, Santa Clara, CA, USA). Denaturation was performed at 75 °C for 10 min followed by hybridization at 37° for 17–18 h.
Day 2: After hybridization, fixogum and cover glasses were removed, and post-hybridization wash was performed by using the VP2000 Processor: 2× SSC + 0.3% NP40 wash buffer at 72 °C (2 min), 2× SSC wash buffer at room temperature (1 min), DI water (1 min), 96% ethanol (1 min), air-drying (5 min).
DAPI counterstain was applied to dry specimens. Slides were covered with a cover glass and subsequently, signal enumeration was performed on the microscope (Leica DM6 B, Leica Microsystems CMS GmbH, Wetzlar, Germany.
2.6. Immunohistochemistry
Two selected samples of SpcS and SyS respectively, were stained immunohistochemically (IHC) against TLE1. For IHC staining, the deparaffinized and rehydrated tissue slides were incubated with the TLE1 antibody, (clone 1F5, monoclonal, diluted 1:50; CellMarque, Sigma-Aldrich, St. Louis, CA, USA). Subsequently, after antigen retrieval and washing steps, the tissue slides were incubated with a secondary antibody needed for DAB (3,3′-Diaminobenzidin) staining. TLE1 staining was evaluated by an experienced pathologist (HUS).
2.7. HTG Transcriptome Panel
Transcriptome analysis was performed by using the HTG Transcriptome Panel (HTG Molecular) according to the manufacturers’ instructions. Approximately 11 mm2 of tissue tumor area from each sample (FFPE slide of 10 µm thickness) were used to perform lysis reaction. Lysis reaction was performed by using the Lysis buffer A and included an additional DNase treatment step. After sample preparation, quantitative nuclease protection assay (qNPA) was performed on the HTG EdgeSeq Processor. In the next step, protection probes that hybridized to mRNA were amplified and tagged to generate sequencing libraries. After preparation of libraries, its concentration was determined by quantitative PCR using the Kapa Library Quantification Kit (Roche, Basel, Switzerland). Libraries were sequenced on an Illumina NextSeq 500 (Illumina, San Diego, CA, USA). Within 1 run, 24 sample libraries were sequenced by using the NextSeq 500/550 High Output Kit v2.5 (75 Cycles, Illumina, San Diego, CA, USA). Sequences were parsed directly from Fastq-files using the HTG EdgeSeq Parser software (HTG Molecular). Parsed data were analyzed via the Analysis platform Edge Seq REVEAL (HTG Molecular). Median normalization and subsequent differential expression (DE) analysis was performed. For DE analysis, the test method “DESeq2” was chosen. This differential expression analysis has been completed using the DESeq2 package (version 1.30.1, HTG molecular, Tucson, AZ, USA) available from Bioconductor. The DESeq2 package provides methods for estimating and testing differential expression using negative-binomial generalized linear models. Empirical Bayes methods are used to estimate dispersion and log2 (fold change) with data-driven prior distributions. See
No pre-filtering is applied to the data prior to analysis. The DESeq2 model corrects for library size using the median ratio method from Anders and Huber (2010). Dispersions are estimated with the Cox Reid-adjusted profile likelihood method developed by McCarthy et al. (2012). Log2 fold change is estimated via Tikhonov/ridge regularization with a zero-centered normal prior distribution with variance calculated using the observed distribution of maximum likelihood coefficients (see DESeq2 documentation for details). DESeq2 performs independent filtering on probes prior to applying the false discovery rate p-value adjustment in order to increase power. This will cause some probes to have no p-value.
3. Results
We analyzed the expression of around 20,000 gene targets in a cohort of 20 patients suffering from spindle cell sarcoma (SpcS) not otherwise specified (NOS) and 44 patients suffering from synovial sarcoma (SyS), exhibiting the classifying chromosomal translocation involving SS18 and either SSX1 or SSX2. The analysis was performed and visualized by using the Reveal software. The classifying chromosomal translocation involving SS18 and either SSX1 or SSX2 could also have been detected in each SyS sample. The samples have been pre-characterized by either FISH or targeted RNA Sequencing
The first step of the expression analysis was a look at the QC metrics. These are helpful to evaluate if the samples failed the QC. The software generates four plots: QC0, QC1, QC2, and QC3, which are explained in Table 1.
In our analysis, two samples failed the QC3, indicating an incomplete digestion of gDNA by DNase (Figure 2). These samples were excluded from further analysis.
SpcS and SyS are morphologically similar to each other, regarding the H&E staining (Figure 3A,B). However, the analysis of all tested samples revealed a distinct gene expression pattern, as shown in the principle component analysis (PCA) plot (Figure 3C).
The Volcano plot indicates 5964 genes being differently expressed with high significance (p < 0.001). This high number of genes showed distinct expression patterns in SpcS compared to SyS (Figure 3D).
TLE1, CPTC1, CCDC171, CA11, DLG4, SYP, ZNF608, KAT2A, RFX3, and CES4A are upregulated in SyS. PLOD2, PLOD3, KDELR2, LIMS1:LIMS4, MYDGF, GOLT1B, TSPAN5, C12orf75, ARNTL2, and SLC31A1 are upregulated in SpcS. The top 10 down-regulated and up-regulated probes in SpcS and SyS, are shown in Figure 4A,B. All genes shown in Figure 4A,B showed p-values < 0.001. Table 2 indicates fold-change and FDR-adjusted p-values of the top 10 differentially expressed genes.
Especially expression of TLE1 showed highly significant differences (6.14 × 1043) between SyS and SpcS. Since TLE1 is a well-established marker for SyS, it is an interesting observation that this marker is one of the mostly significant genes. For this reason, we immunohistochemically stained SyS and SpcS against TLE1 (Figure 5B,C) and additionally generated a Bar plot, showing the expression of TLE1 in two samples of Sys and SpcS, respectively (Figure 5A).
To visualize the top 10 differentially expressed genes in SpcS and SyS, a heatmap was created (Figure 6). Yellow bars indicate SpcS samples, and blue bars indicate SyS samples. Red designates low expression, whereas blue specifies an elevated expression of genes. This heatmap visualizes the clear differences in gene expression of the 10 genes, including the established marker TLE1 for SyS.
4. Discussion
Transcriptome profiling makes it possible to give an overview of gene expression in a large scale and thus simplifies the search for molecular biomarkers. For this purpose, RNA sequencing is considered the gold standard [10]. However, this method has its limitations using FFPE tissue. Nonetheless, FFPE tissue is the only available material, as it is routinely used and could be easily stored at room temperature. For this reason, sequencing methodologies using FFPE tissue are constantly under optimization to improve analysis on these challenging samples. We tested the HTP panel from HTG for sensitivity, robustness, and reliability of transcriptome-wide gene expression analysis on FFPE tissue.
4.1. Assay Performance
The extraction-free and nuclease-protection-based workflow simplified the practical handling. Additionally, with 11 mm2 of a 10 µm tissue slide, very few sample inputs are needed. This is especially important for samples where only biopsy material is available.
Our analyses revealed genes showing distinct expression patterns in SpcS compared to SyS. Regarding Figure 3C, the expression patterns of SyS compared to SpcS differ significantly and the volcano plot (Figure 3D) shows that 5964 genes are differentially expressed in these 2 entities. As we generally expected different gene expression patterns when comparing two different entities, these findings underline the robustness and reliability of the assay. In SyS, the most significantly result of differences in gene expression was shown for TLE1, being upregulated in SyS. This reflects the reliability of this assay, as TLE1 is an already established marker for SyS, being overexpressed in the nuclei of synovial sarcoma cells [11]. The immunohistochemical staining in Figure 5 is shown to validate the results gained by the novel nuclease-protection assay. As IHC is routinely performed in pathological diagnostic of SyS, we added this figure to underline the reliability of these results. In addition, a significantly different expression of other genes relevant for tumor progression could be found, e.g., Carnitine palmitoyltransferase 1 C (CPT1C), which promotes tumor growth and drug resistance, being reported for gastric cancer [12]. CPT1C was identified to be involved in fatty acid metabolism, due to its enzyme activity, which allows the entry of long-chain fatty acids [13]. Reilly and Mak reported unusual and enhanced expression of CPT1C in brain cancers and several sarcomas of soft-tissues and lung [14]. However, the detailed role or CPT1C in cancer remains indefinite [13]. Procollagen-lysine, 2-oxoglutarate 5-dioxygenase (PLOD) 3 was overexpressed in SpcS and is reported to promote tumor progression and poor prognosis in gliomas [15]. In addition, PLOD2 expression is elevated in SpcS. Qi and Xu reported PLODs to have enhancing effects of cell migration, invasion, and proliferation potentially by the modulation of collagen cross-link and maturation [16]. Our analyses have shown that the HTP panel is a suitable method to perform gene expression profiling on a large scale.
4.2. Comparison of HTP with Other Existing mRNA Profiling Systems
Currently, there are several established techniques including Lexogen QuantSeq, Qiagen QiaSeq, BioSpyder TempO-Seq, Ion AmpliSeq, Nanostring, Affymetrix Clariom S or U133A, Illumina BeadChip and RNA-seq enabling the analysis on FFPE or fresh frozen (FF) tissue [17]. Turnbull et al. compared these techniques in 2020 in terms of biochemistry, sample throughput, number of primers and mapped gene IDs (Ensembl), read depths, input of FFPE sample RNA, costs per sample, success rate of FF samples, and success rate of FFPE samples. To supplement this table with info about the HTP panel performed in this study, we listed these parameters in Table 3.
The HTP Panel of HTG Molecular has a moderate sample throughput (24 samples per 3 days), but the number of targets (19398) as well as the sequencing depth (8 M) is close to comparable methods such as BioSpyder TempO-Seq [17]. However, the costs per sample are very high in comparison to the other technologies. Nevertheless, its sensitivity and success rate of FFPE samples is the highest compared to others, considering the low RNA input needed for HTP. In addition, FFPE blocks used in this study were stored for up to five years. HTG Molecular itself reports robust results for FFPE material older than 10 years [10].
5. Conclusions
The HTG transcriptome profiling is a feasible method for gene expression analysis in a large scale. Our performed transcriptome profiling revealed robust results, and we were able to retrieve the known significant overexpression of TLE1 in synovial sarcoma. Therefore, this method of transcriptome profiling has proven to be a very robust method for the search of biomarkers, especially in limited tumor material. Reproducible results were achieved, while very small amounts of RNA are needed.
Data curation, S.B. (Sabrina Borchert); formal analysis, S.B. (Sabrina Borchert), T.H., S.K., R.H., W.K.G., A.S., J.H., M.G., S.B. (Sebastian Bauer) and H.-U.S.; methodology, Y.K. and S.B. (Sophia Berger); project administration, S.B. (Sabrina Borchert) and H.-U.S.; supervision, H.-U.S.; writing—original draft, S.B. (Sabrina Borchert); writing—review and editing, T.H., S.K., R.H., Y.K., S.B. (Sophia Berger), W.K.G., A.S., J.H., M.G., S.B. (Sebastian Bauer) and H.-U.S. All authors have read and agreed to the published version of the manuscript.
This retrospective study conforms to the principles outlined in the Declaration of Helsinki (as revised in 2013) and was approved by the Ethics Committee of the Medical Faculty of the University Duisburg-Essen (permits no. 20-9558-BO).
Patient consent was waived due to the retrospective nature of this study.
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.
This work was supported by HTG Molecular.
This work was supported by HTG Molecular. Rainer Hamacher reports travel grants from Lilly, Novartis, and PharmaMar as well as personal fees from Lilly and PharmaMar outside of the submitted work. Sabrina Borchert reports fees from Brystol-Myers Squibb (research funding) outside of the submitted work. The other authors declare no potential conflicts of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
QC plots and their meaning.
| QC Metric | Failure Mode Detected | Criteria |
|---|---|---|
| QC0 | Insufficient RNA | %POS > 4% is a failure |
| QC1 | Insufficient read depth | Total aligned reads < 7 million per sample is a failure |
| QC2 | High background signal | Median log2 (CPM) negative control probes > 2 is a failure |
| QC3 | Incomplete digestion of gDNA by DNase | Median adjusted log2 (CPM) of gDNA probes > 1 is a failure |
Data of the top 10 differentially expressed genes between SyS and SpcS. Mean normalized values for SyS and SpcS, average expression, fold change, raw p-value, and false-discovery-rate (FDR)-adjusted p-values are shown.
| Probe | Mean Normalized SyS | Mean Normalized SpcS | AveExpr | Fold Change SpcS. Vs. SyS | rawP SpcS. Vs. SyS | adjP SpcS. Vs. SyS |
|---|---|---|---|---|---|---|
| CPT1C | 769 | 87 | 9.07 | −8.89 | 6.29 × 10−48 | 7.57 × 10−44 |
| TLE1 | 1520 | 123 | 10.03 | −12.38 | 1.02 × 10−46 | 6.14 × 10−43 |
| CCDC171 | 870 | 242 | 9.36 | −3.60 | 1.73 × 10−43 | 6.94 × 10−40 |
| CA11 | 1593 | 162 | 10.11 | −9.87 | 2.09 × 10−42 | 6.29 × 10−39 |
| DLG4 | 1472 | 392 | 10.11 | −3.75 | 5.39 × 10−39 | 1.30 × 10−35 |
| PLOD2 | 1366 | 8094 | 11.83 | 5.93 | 3.08 × 10−36 | 6.17 × 10−33 |
| PLOD3 | 1170 | 2982 | 10.80 | 2.55 | 1.49 × 10−35 | 2.56 × 10−32 |
| SYP | 611 | 94 | 8.77 | −6.48 | 1.43 × 10−34 | 2.16 × 10−31 |
| KAT2A | 3135 | 1225 | 11.28 | −2.56 | 1.39 × 10−30 | 1.51 × 10−27 |
| ZNF608 | 4609 | 691 | 11.68 | −6.67 | 1.50 × 10−30 | 1.51 × 10−27 |
Table of technology parameters from the comparison of Turnbull et al. [
| Technology | Technology |
Biochemistry | Approx. Throughput | Max. no. probes/Primer Pairs | No. of Mapped ENSG IDs | Read Depths | Input FFPE RNA (ng) | Approx. Cost per Sample (£) | Success Rate of FFPE Samples (n) |
|---|---|---|---|---|---|---|---|---|---|
| 3′ RNA |
Lexogen |
RNA → RT, oligodT priming from 3′ |
96 samples |
55,765 | 25,610 | 10 M | 500 | 90 | 98% |
| QiaSeq UPX |
RNA → RT, oligodT priming for |
96 samples |
42,553 | 20,000 | 15 M | 10 | 50 | 94% |
|
| Specific Targeted Sequencing | BioSpyder |
RNA → annealed 50 bp detector |
192 |
19,300 | 19,300 | 12 M | 20 μm |
160 | 95% |
| Ion Ampliseq |
RNA → RT, multiplex PCR → |
96 samples |
20,802 | 19,059 | 8 M | 10 | 160 | 76% |
|
| HTG Edge Seq, Illumina Next Seq 500 | FFPE slide → nuclease protection and probe hybridization → S1 nuclease digestion → barcoding and amplification → Library Cleanup → sequencing → Data Parsing | 24 samples per 3 days | 19,398 | 19,398 | 8 M |
11 mm2
|
1159,095 |
96.88
|
|
| Targeted |
Nanostring | RNA → hybridisation to fluorescent |
12 samples |
800 | 800 | N/A | 50 | 250 | 100% |
| Newer |
Affymetrix |
RNA → cRNA amplification → |
192 |
211,300 | >20,000 | N/A | 50 | 100 | 100% |
| Traditional |
Affymetrix |
192 per |
250,833 | 11,827 | N/A | 50 | 360 | 100% |
|
| Illumina |
RNA → RT, amplification, |
96 samples |
47,323 | 22,571 | N/A | 1500 | 195 | 21% |
|
| Full RNA |
RNA-seq | RNA → fragmentation → RT → |
8 samples |
20,025 | 18,571 | 136 M |
2000 | 250-500 | 100% |
References
1. Gaffney, E.F.; Riegman, P.H.; Grizzle, W.E.; Watson, P.H. Factors that drive the increasing use of FFPE tissue in basic and translational cancer research. Biotech. Histochem.; 2018; 93, pp. 373-386. [DOI: https://dx.doi.org/10.1080/10520295.2018.1446101] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30113239]
2. Schmeller, J.; Wessolly, M.; Mairinger, E.; Borchert, S.; Hager, T.; Mairinger, T.; Schmid, K.W.; Wohlschlaeger, J.; Walter, R.F.H.; Mairinger, F.D. Setting out the frame conditions for feasible use of FFPE derived RNA. Pathol. Res. Pract.; 2019; 215, pp. 381-386. [DOI: https://dx.doi.org/10.1016/j.prp.2018.12.027]
3. Esteve-Codina, A.; Arpi, O.; Martinez-García, M.; Pineda, E.; Mallo, M.; Gut, M.; Carrato, C.; Rovira, A.; Lopez, R.; Tortosa, A. et al. A Comparison of RNA-Seq Results from Paired Formalin-Fixed Paraffin-Embedded and Fresh-Frozen Glioblastoma Tissue Samples. PLoS ONE; 2017; 12, e0170632. [DOI: https://dx.doi.org/10.1371/journal.pone.0170632]
4. Lüder Ripoli, F.; Mohr, A.; Conradine Hammer, S.; Willenbrock, S.; Hewicker-Trautwein, M.; Hennecke, S.; Murua Escobar, H.; Nolte, I. A Comparison of Fresh Frozen vs. Formalin-Fixed, Paraffin-Embedded Specimens of Canine Mammary Tumors via Branched-DNA Assay. Int. J. Mol. Sci.; 2016; 17, 724. [DOI: https://dx.doi.org/10.3390/ijms17050724] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27187374]
5. Kashofer, K.; Viertler, C.; Pichler, M.; Zatloukal, K. Quality control of RNA preservation and extraction from paraffin-embedded tissue: Implications for RT-PCR and microarray analysis. PLoS ONE; 2013; 8, e70714. [DOI: https://dx.doi.org/10.1371/journal.pone.0070714] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23936242]
6. von Ahlfen, S.; Missel, A.; Bendrat, K.; Schlumpberger, M. Determinants of RNA quality from FFPE samples. PLoS ONE; 2007; 2, e1261. [DOI: https://dx.doi.org/10.1371/journal.pone.0001261] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18060057]
7. Castiglione, F.; Rossi Degl’Innocenti, D.; Taddei, A.; Garbini, F.; Buccoliero, A.M.; Raspollini, M.R.; Pepi, M.; Paglierani, M.; Asirelli, G.; Freschi, G. et al. Real-time PCR analysis of RNA extracted from formalin-fixed and paraffin-embeded tissues: Effects of the fixation on outcome reliability. Appl. Immunohistochem. Mol. Morphol.; 2007; 15, pp. 338-342. [DOI: https://dx.doi.org/10.1097/01.pai.0000213119.81343.7b] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17721281]
8. Trejo, C.L.; Babić, M.; Imler, E.; Gonzalez, M.; Bibikov, S.I.; Shepard, P.J.; VanSteenhouse, H.C.; Yeakley, J.M.; Seligmann, B.E. Extraction-free whole transcriptome gene expression analysis of FFPE sections and histology-directed subareas of tissue. PLoS ONE; 2019; 14, e0212031. [DOI: https://dx.doi.org/10.1371/journal.pone.0212031]
9. Jones, K.B.; Barrott, J.J.; Xie, M.; Haldar, M.; Jin, H.; Zhu, J.F.; Monument, M.J.; Mosbruger, T.L.; Langer, E.M.; Randall, R.L. et al. The impact of chromosomal translocation locus and fusion oncogene coding sequence in synovial sarcomagenesis. Oncogene; 2016; 35, pp. 5021-5032. [DOI: https://dx.doi.org/10.1038/onc.2016.38] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26947017]
10. Molecular, H. White Paper: Comparison of the Prototype HTG Transcriptome Panel to RNA-Seq. Available online: https://www.htgmolecular.com/assays/htp (accessed on 24 March 2021).
11. El Beaino, M.; Jupiter, D.C.; Assi, T.; Rassy, E.; Lazar, A.J.; Araujo, D.M.; Lin, P.P. Diagnostic Value of TLE1 in Synovial Sarcoma: A Systematic Review and Meta-Analysis. Sarcoma; 2020; 2020, 7192347. [DOI: https://dx.doi.org/10.1155/2020/7192347] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32322158]
12. Chen, T.; Wu, G.; Hu, H.; Wu, C. Enhanced fatty acid oxidation mediated by CPT1C promotes gastric cancer progression. J. Gastrointest. Oncol.; 2020; 11, pp. 695-707. [DOI: https://dx.doi.org/10.21037/jgo-20-157] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32953153]
13. Casals Farré, N.; Zammit, V.; Herrero, L.; Fadó, R.; Rodriguez-Rodriguez, R.; Serra, D. Carnitine palmitoyltransferase 1C: From cognition to cancer. Prog. Lipid Res.; 2015; 61, pp. 134-148. [DOI: https://dx.doi.org/10.1016/j.plipres.2015.11.004] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26708865]
14. Reilly, P.T.; Mak, T.W. Molecular Pathways: Tumor Cells Co-opt the Brain-Specific Metabolism Gene CPT1C to Promote Survival. Clin. Cancer Res.; 2012; 18, 5850. [DOI: https://dx.doi.org/10.1158/1078-0432.CCR-11-3281] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22952346]
15. Tsai, C.K.; Huang, L.C.; Tsai, W.C.; Huang, S.M.; Lee, J.T.; Hueng, D.Y. Overexpression of PLOD3 promotes tumor progression and poor prognosis in gliomas. Oncotarget; 2018; 9, pp. 15705-15720. [DOI: https://dx.doi.org/10.18632/oncotarget.24594] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29644003]
16. Qi, Y.; Xu, R. Roles of PLODs in Collagen Synthesis and Cancer Progression. Front. Cell Dev. Biol.; 2018; 6, 66. [DOI: https://dx.doi.org/10.3389/fcell.2018.00066] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30003082]
17. Turnbull, A.K.; Selli, C.; Martinez-Perez, C.; Fernando, A.; Renshaw, L.; Keys, J.; Figueroa, J.D.; He, X.; Tanioka, M.; Munro, A.F. et al. Unlocking the transcriptomic potential of formalin-fixed paraffin embedded clinical tissues: Comparison of gene expression profiling approaches. BMC Bioinform.; 2020; 21, 30. [DOI: https://dx.doi.org/10.1186/s12859-020-3365-5] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31992186]
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Abstract
Simple Summary
The following study aimed to validate and test the feasibility, reliability, technical applicability robustness, and reliability of a new commercially available transcriptome profiling assay, being performed on formalin-fixed, paraffin-embedded (FFPE) patient samples. The patients were suffering from either synovial sarcoma or spindle cell sarcoma, which are morphologically similar tumors, but differ in molecular characteristics. Transcriptome analysis of FFPE material is still challenging. However, it is the most available material in pathological routine and therefore valuable for translational research approaches. This new commercially available assay is based on a nuclease protection assay and has shown to be a feasible method for adequate transcriptome profiling with low sample input and therefore is suitable for further research of biomarkers.
AbstractBackground: Transcriptome profiling provides large data on tumor biology, which is particularly valuable in translational research and is becoming more and more important for clinical decision-making as well. RNA sequencing is considered to be the gold standard for this. However, FFPE material, as the most available material in routine pathology, has been an undefeatable obstacle for RNAseq. Extraction-free nuclease protection assays have the potential to be a reliable alternative method for large-scale expression profiling. The aim of this study was to validate and test the basic feasibility, technical applicability robustness, and reliability of the HTG transcriptome profiling (HTP) assay on clinical tumor samples. Methods: FFPE samples from 44 synovial sarcomas (SyS) and 20 spindle cell sarcomas (SpcS) were used. The HTP assay was performed on 10 µm thin FFPE slides. After nuclease protection in the HTG Edge Seq System, libraries were generated for sequencing on an Illumina NextSeq 500 platform. Fastq data were parsed and then analyzed by using the HTG analysis platform EdgeSeq REVEAL. Immunohistochemistry was performed to validate the expression of TLE1. Results: The technical application of the HTP Panel revealed robust and reliable results with 62 samples, and only 2 samples failed due to an incomplete digestion of gDNA. The analysis, performed at the analysis platform REVEAL, showed 5964 genes being significantly differentially expressed between SpcS and SyS. In particular, overexpression of the known marker TLE1 in synovial sarcoma could be recovered, which underlines the reliability of this system. Discussion: Transcriptome profiling gets more and more important for tumor research and diagnostics. Among other established technologies, the HTP Panel has shown to be a feasible method to get robust and reliable results. Thereby, this method needs very few sample-input by getting a success-rate of 96.88%, which indicates the upper average range, compared to other technologies working with FFPE tissue. Conclusion: The nuclease protection assay-based HTP Panel is a feasible method for adequate transcriptome profiling with low sample input and therefore is suitable for further research of biomarkers.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
; Herold, Thomas 1 ; Kalbourtzis, Stavros 1 ; Hamacher, Rainer 2 ; Krause, Yvonne 1 ; Berger, Sophia 1 ; Guder, Wiebke K 3
; Streitbuerger, Arne 4 ; Hardes, Jendrik 5 ; Goetz, Moritz 6 ; Bauer, Sebastian 2 ; Hans-Ulrich Schildhaus 7
1 Institute of Pathology, University Hospital Essen, Hufelandstraße 55, D-45147 Essen, Germany; German Cancer Consortium (DKTK), Partner Site University Hospital Essen, D-45147 Essen, Germany
2 German Cancer Consortium (DKTK), Partner Site University Hospital Essen, D-45147 Essen, Germany; Department of Medical Oncology, Sarcoma Center, West German Cancer Center, University Hospital Essen, D-45147 Essen, Germany
3 German Cancer Consortium (DKTK), Partner Site University Hospital Essen, D-45147 Essen, Germany; Department of Orthopedics and Tumor Orthopedics, University Hospital Muenster, 48149 Muenster, Germany; Department of Orthopedic Oncology, University Hospital Essen, D-45147 Essen, Germany
4 German Cancer Consortium (DKTK), Partner Site University Hospital Essen, D-45147 Essen, Germany; Department of Orthopedic Oncology, University Hospital Essen, D-45147 Essen, Germany
5 German Cancer Consortium (DKTK), Partner Site University Hospital Essen, D-45147 Essen, Germany; Department of Orthopedic Oncology, University Hospital Essen, D-45147 Essen, Germany; Department of Musculoskeletal Oncology, University Hospital Essen, D-45147 Essen, Germany
6 Center of Pathology, Cytology and Molecular Pathology, D-41462 Neuss, Germany
7 Institute of Pathology, University Hospital Essen, Hufelandstraße 55, D-45147 Essen, Germany; German Cancer Consortium (DKTK), Partner Site University Hospital Essen, D-45147 Essen, Germany; Department of Medical Oncology, Sarcoma Center, West German Cancer Center, University Hospital Essen, D-45147 Essen, Germany