MACE RNA Sequencing Analysis of Conjunctival Squamous Cell Carcinoma and Papilloma Using Formalin-Fixed Paraffin-Embedded Tumor Tissue
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ABSTRACT:
PROVIDER: PRJNA614903 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11402474.fastq.gz | Fastqsanger.gz | |||
| SRR11402475.fastq.gz | Fastqsanger.gz | |||
| SRR11402476.fastq.gz | Fastqsanger.gz | |||
| SRR11402477.fastq.gz | Fastqsanger.gz | |||
| SRR11402478.fastq.gz | Fastqsanger.gz |
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Project description:The aim of this study was to elucidate the molecular mediators of conjunctival squamous cell carcinoma (SCC) and Papilloma (Pap) and to define their expression signature as an aid to the histologic classification. The transcriptional signatures of both conjunctival SCC and Pap presented in this study may improve our understanding of the diseases and their diagnosis and eventually contribute to successful personalized therapeutic approaches.
2020-12-09 | GSE147449 | GEO
Project description:Recent advances in the field of biomedical research allow for elucidation of the transcriptional signature of rare tumors such as conjunctival squamous cell carcinoma (SCC). In this study we compare its expression profile to conjunctival papilloma (Pap) and healthy conjunctival tissue (Ctrl) and develop a classification tool to differentiate these entities. Seven conjunctival SCC, seven Pap and ten Ctrl were formalin-fixed and paraffin-embedded (FFPE) and analyzed using Massive Analysis of cDNA Ends (MACE) RNA sequencing. Differentially expressed genes (DEG) and gene ontology (GO) clusters were explored and the abundance of involved cell types was quantified by xCell. Finally, a classification model was developed to distinguish SCC from Pap and Ctrl. Among the most prominent DEG in SCC a plethora of keratins were upregulated when compared to Pap and Ctrl. xCell analysis revealed an enrichment of immune cells, including activated dendritic cells and T-helper type 1 cells (Th1), in SCC when compared to Ctrl. The generated classification model could reliably discriminate between the three entities according to the expression pattern of 30 factors. This study provides a transcriptome-wide gene expression profile of rare conjunctival SCC. The analysis identifies distinct keratins, as well as dendritic and Th1 cells as important mediators in SCC. Finally, the provided gene expression classifier may become an aid to the conventional histological classification of conjunctival tumors in uncertain cases.
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Project description:Archived formalin-fixed paraffin-embedded (FFPE) samples are the global standard format for preservation of the majority of biopsies in both basic research and translational cancer studies, and profiling chromatin accessibility in the archived FFPE tissues is fundamental to understanding gene regulation. Accurate mapping of chromatin accessibility from FFPE specimens is challenging because of the high degree of DNA damage. Here, we first showed that standard ATAC-seq can be applied to purified FFPE nuclei but yields lower library complexity and a smaller proportion of long DNA fragments. We then present FFPE-ATAC, the first highly sensitive method for decoding chromatin accessibility in FFPE tissues that combines Tn5-mediated transposition and T7 in vitro transcription. The FFPE-ATAC generates high-quality chromatin accessibility profiles with 500 nuclei from a single FFPE tissue section, enables the dissection of chromatin profiles from the regions of interest with the aid of hematoxylin and eosin (H&E) staining, and reveals disease-associated chromatin regulation from the human colorectal cancer FFPE tissue archived for >10 yr. In summary, the approach allows decoding of the chromatin states that regulate gene expression in archival FFPE tissues, thereby permitting investigators to better understand epigenetic regulation in cancer and precision medicine.
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Project description:Detection and characterization of novel viruses is hampered frequently by the lack of properly stored materials. Especially for the retrospective identification of viruses responsible for past disease outbreaks, often only formalin-fixed paraffin-embedded (FFPE) tissue samples are available. Although FFPE tissues can be used to detect known viral sequences, the application of FFPE tissues for detection of novel viruses is currently unclear. In the present study it was shown that sequence-independent amplification in combination with next-generation sequencing can be used to detect sequences of known and unknown viruses, although with relatively low sensitivity. These findings indicate that this technique could be useful for detecting novel viral sequences in FFPE tissues collected from humans and animals with disease of unknown origin, when other samples are not available. In addition, application of this method to FFPE tissues allows to correlate with the presence of histopathological changes in the corresponding tissue sections.
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Project description:Formalin fixed paraffin embedded (FFPE) tissues are a vast resource of annotated clinical samples. As such, they represent highly desirable and informative materials for the application of high definition genomics for improved patient management and to advance the development of personalized therapeutics. However, a limitation of FFPE tissues is the variable quality of DNA extracted for analyses. Furthermore, admixtures of non-tumor and polyclonal neoplastic cell populations limit the number of biopsies that can be studied and make it difficult to define cancer genomes in patient samples. To exploit these valuable tissues we applied flow cytometry-based methods to isolate pure populations of tumor cell nuclei from FFPE tissues and developed a methodology compatible with oligonucleotide array CGH and whole exome sequencing analyses. These were used to profile a variety of tumors (breast, brain, bladder, ovarian and pancreas) including the genomes and exomes of matching fresh frozen and FFPE pancreatic adenocarcinoma samples.
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Project description:BackgroundInvestigating the expression of candidate genes in tissue samples usually involves either immunohistochemical labelling of formalin-fixed paraffin-embedded (FFPE) sections or immunofluorescence labelling of cryosections. Although both of these methods provide essential data, both have important limitations as research tools. Consequently, there is a demand in the research community to be able to perform routine, high quality immunofluorescence labelling of FFPE tissues.ResultsWe present here a robust optimised method for high resolution immunofluorescence labelling of FFPE tissues, which involves the combination of antigen retrieval, indirect immunofluorescence and confocal laser scanning microscopy. We demonstrate the utility of this method with examples of immunofluorescence labelling of human kidney, human breast and a tissue microarray of invasive human breast cancers. Finally, we demonstrate that stained slides can be stored in the short term at 4 degrees C or in the longer term at -20 degrees C prior to images being collected. This approach has the potential to unlock a large in vivo database for immunofluorescence investigations and has the major advantages over immunohistochemistry in that it provides higher resolution imaging of antigen localization and the ability to label multiple antigens simultaneously.ConclusionThis method provides a link between the cell biology and pathology communities. For the cell biologist, it will enable them to utilise the vast archive of pathology specimens to advance their in vitro data into in vivo samples, in particular archival material and tissue microarrays. For the pathologist, it will enable them to utilise multiple antibodies on a single section to characterise particular cell populations or to test multiple biomarkers in limited samples and define with greater accuracy cellular heterogeneity in tissue samples.
| S-EPMC2288605 | biostudies-literature
Project description:BackgroundMolecular characterization of breast and other cancers by gene expression profiling has corroborated existing classifications and revealed novel subtypes. Most profiling studies are based on fresh frozen (FF) tumor material which is available only for a limited number of samples while thousands of tumor samples exist as formalin-fixed, paraffin-embedded (FFPE) blocks. Unfortunately, RNA derived of FFPE material is fragmented and chemically modified impairing expression measurements by standard procedures. Robust protocols for isolation of RNA from FFPE material suitable for stable and reproducible measurement of gene expression (e.g. by quantitative reverse transcriptase PCR, QPCR) remain a major challenge.ResultsWe present a simple procedure for RNA isolation from FFPE material of diagnostic samples. The RNA is suitable for expression measurement by QPCR when used in combination with an optimized cDNA synthesis protocol and TaqMan assays specific for short amplicons. The FFPE derived RNA was compared to intact RNA isolated from the same tumors. Preliminary scores were computed from genes related to the ER response, HER2 signaling and proliferation. Correlation coefficients between intact and partially fragmented RNA from FFPE material were 0.83 to 0.97.ConclusionWe developed a simple and robust method for isolating RNA from FFPE material. The RNA can be used for gene expression profiling. Expression measurements from several genes can be combined to robust scores representing the hormonal or the proliferation status of the tumor.
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Project description:Metabolite profiling has significantly contributed to a deeper understanding of the biochemical metabolic networks and pathways in cancer cells. Metabolomics-based biomarker discovery would greatly benefit from the ability to interrogate retrospective annotated clinical specimens archived as formalin-fixed, paraffin-embedded (FFPE) material. Mass spectrometry-based metabolomic analysis was performed in matched frozen and FFPE human prostate cancers as well as isogenic prostate cancer cell lines. A total of 352 and 460 metabolites were profiled in human tissues and cell lines, respectively. Classes and physical-chemical characteristics of the metabolites preserved in FFPE material were characterized and related to their preservation or loss following fixation and embedding. Metabolite classes were differentially preserved in archival FFPE tissues, regardless of the age of the block, compared with matched frozen specimen, ranging from maximal preservation of fatty acids (78%) to loss of the majority of peptides and steroids. Generally, FFPE samples showed a decrease of metabolites with functional groups, such as carboxamide. As an adjunct technique, metabolic profiles were also obtained in situ from FFPE tissue sections where metabolites were extracted in a manner that preserves tissue architecture. Despite the fact that selected metabolites were not retained after processing, global metabolic profiles obtained from FFPE can be used to predict biologic states and study biologic pathways. These results pave the way for metabolomics-based biomarker discovery/validation utilizing retrospective and clinically annotated FFPE collections.Implications: Metabolic profiles can be performed in archival tissue and may be used to complement other profiling methods such as gene expression for biomarker discovery or pathway analysis in the assessment of biologic states. Mol Cancer Res; 15(4); 439-47. ©2017 AACR.
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Project description:MotivationRecent studies have shown that RNA-sequencing (RNA-seq) can be used to measure mRNA of sufficient quality extracted from formalin-fixed paraffin-embedded (FFPE) tissues to provide whole-genome transcriptome analysis. However, little attention has been given to the normalization of FFPE RNA-seq data, a key step that adjusts for unwanted biological and technical effects that can bias the signal of interest. Existing methods, developed based on fresh-frozen or similar-type samples, may cause suboptimal performance.ResultsWe proposed a new normalization method, labeled MIXnorm, for FFPE RNA-seq data. MIXnorm relies on a two-component mixture model, which models non-expressed genes by zero-inflated Poisson distributions and models expressed genes by truncated normal distributions. To obtain maximum likelihood estimates, we developed a nested EM algorithm, in which closed-form updates are available in each iteration. By eliminating the need for numerical optimization in the M-step, the algorithm is easy to implement and computationally efficient. We evaluated MIXnorm through simulations and cancer studies. MIXnorm makes a significant improvement over commonly used methods for RNA-seq expression data.Availability and implementationR code available at https://github.com/S-YIN/MIXnorm.Contactswang@smu.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:BackgroundWe have recently demonstrated that expression profiling is a more accurate and objective method to classify gliomas than histology. Similar to most expression profiling studies, our experiments were performed using fresh frozen (FF) glioma samples whereas most archival samples are fixed in formalin and embedded in paraffin (FFPE). Identification of the same, expression-based intrinsic subtypes in FFPE-stored samples would enable validation of the prognostic value of these subtypes on these archival samples. In this study, we have therefore determined whether the intrinsic subtypes identified using FF material can be reproduced in FFPE-stored samples.MethodsWe have performed expression profiling on 55 paired FF-FFPE glioma samples using HU133 plus 2.0 arrays (FF) and Exon 1.0 ST arrays (FFPE). The median time in paraffin of the FFPE samples was 14.1 years (range 6.6-26.4 years).ResultsIn general, the correlation between FF and FFPE expression in a single sample was poor. We then selected the most variable probe sets per gene (n=17,583), and of these, the 5000 most variable probe sets on FFPE expression profiles. This unsupervised selection resulted in a better concordance (R(2)=0.54) between expression of FF and FFPE samples. Importantly, this probe set selection resulted in a correct assignment of 87% of FFPE samples into one of seven intrinsic subtypes identified using FF samples. Assignment to the same molecular cluster as the paired FF tissue was not correlated to time in paraffin.ConclusionWe are the first to examine a large cohort of paired FF and FFPE samples. We show that expression data from FFPE material can be used to assign samples to intrinsic molecular subtypes identified using FF material. This assignment allows the use of archival material, including material derived from large-randomised clinical trials, to determine the predictive and/or prognostic value of 'intrinsic glioma subtypes' on Exon arrays. This would enable clinicians to provide patients with an objective and accurate diagnosis and prognosis, and a personalised treatment strategy.
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