Impact of therapy on gene expression in high-risk prostate cancer (PCA) treated with neoadjuvant docetaxel and androgen deprivation therapy.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 8-8
Author(s):  
Himisha Beltran ◽  
Alexander Wyatt ◽  
Edmund Chedgy ◽  
Ladan Fazli ◽  
Andrea Sboner ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Hormone Receptors ◽  
Dna Analysis ◽  
Rna Extraction ◽  
Post Treatment ◽  
Rna Seq ◽  
Sequencing Data ◽  
Quantitative Expression ◽  
High Risk Prostate Cancer

8 Background: Molecular analyses of neoadjuvant post-treatment radical prostatectomy (RP) specimens has been challenging as often times only microscopic foci remain present at time of RP precluding RNA-seq. DNA analysis alone in the absence of expression may be suboptimal in elucidating complex mechanisms of resistance and/or prognostic risk stratification. We therefore set out to develop an assay that could quantify mRNA expression in treated and untreated PCA using formalin fixed paraffin embedded (FFPE) tissues. Methods: We evaluated 40 untreated and post-treatment FFPE specimens as well as patient-matched pre-treated needle biopsies and baseline clinical data from patients enrolled on CALGB 90203: a randomized phase 3 trial comparing noeadjuvant docetaxel and ADT followed by RP vs RP alone for men with high risk localized PCA. High-density tumor areas were selected for RNA extraction (min 50ng RNA). We used NanoString nCounter to quantify gene expression of a custom panel of 75 genes including AR and androgen regulated, neural/neuroendocrine (NE), EMT, cell cycle, hormone receptors, TMPRSS-ERG, ARv7 splice variant, and housekeeper genes. mRNA data was integrated with matched whole exome sequencing data. Frozen specimens and RNA-Seq (n = 7) were used for QC and comparative analysis. Results: Quantitative expression using Nanostring showed high correlation with RNA-seq of patient-matched frozen tissue (Spearman coefficient 0.9). There was significant upregulation of AR and the ARv7 expression following treatment, as well as a subset of NE and EMT genes; three high chromogranin A outlier cases were identified in the treatment arm. There was an overall higher AR score in treated cases (based on expression of 30 AR signaling genes) compared to untreated, along the spectrum of CRPC. Conclusions: These data support the feasibility of quantifying gene expression in neoadjuvant-treated PCA cases with limited FFPE tissue requirement. Extensive characterization of AR status and NE/EMT genes identifies molecular outliers that can arise post-treatment and provides new insight into the heterogeneity of treatment response and potential early markers of resistance. Clinical trial information: NCT00430183.

scholarly journals Impact of RNA Extraction and Target Capture Methods on RNA Sequencing Using Formalin-Fixed, Paraffin Embedded Tissues

2019 ◽  
Author(s):  
Christopher A. Hilker ◽  
Aditya V. Bhagwate ◽  
Jin Sung Jang ◽  
Jeffrey G Meyer ◽  
Asha A. Nair ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fusion Gene ◽  
Rna Extraction ◽  
Extraction Methods ◽  
Rna Seq ◽  
Gene Detection ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded ◽  
Whole Transcriptome ◽  
Formalin Fixed

AbstractFormalin fixed paraffin embedded (FFPE) tissues are commonly used biospecimen for clinical diagnosis. However, RNA degradation is extensive when isolated from FFPE blocks making it challenging for whole transcriptome profiling (RNA-seq). Here, we examined RNA isolation methods, quality metrics, and the performance of RNA-seq using different approaches with RNA isolated from FFPE and fresh frozen (FF) tissues. We evaluated FFPE RNA extraction methods using six different tissues and five different methods. The reproducibility and quality of the prepared libraries from these RNAs were assessed by RNA-seq. We next examined the performance and reproducibility of RNA-seq for gene expression profiling with FFPE and FF samples using targeted (Kinome capture) and whole transcriptome capture based sequencing. Finally, we assessed Agilent SureSelect All-Exon V6+UTR capture and the Illumina TruSeq RNA Access protocols for their ability to detect known gene fusions in FFPE RNA samples. Although the overall yield of RNA varied among extraction methods, gene expression profiles generated by RNA-seq were highly correlated (>90%) when the input RNA was of sufficient quality (≥DV200 30%) and quantity (≥ 100 ng). Using gene capture, we observed a linear relationship between gene expression levels for shared genes that were captured using either All-Exon or Kinome kits. Gene expression correlations between the two capture-based approaches were similar using RNA from FFPE and FF samples. However, TruSeq RNA Access protocol provided significantly higher exon and junction reads when compared to the SureSelect All-Exon capture kit and was more sensitive for fusion gene detection. Our study established pre and post library construction QC parameters that are essential to reproducible RNA-seq profiling using FFPE samples. We show that gene capture based NGS sequencing is an efficient and highly reproducible strategy for gene expression measurements as well as fusion gene detection.

198 PROGESTERONE, ESTROGEN (ER-α AND ER-β), AND OXYTOCIN RECEPTOR GENE EXPRESSION IN CANINE EMBRYOS

2013 ◽  
Vol 25 (1) ◽  
pp. 248
Author(s):  
A. A. P. Derussi ◽  
A. C. S. Castilho ◽  
R. W. A. Souza ◽  
R. Volpato ◽  
C. R. F. Guaitolini ◽  
...  
Keyword(s):  
Gene Expression ◽  
Relative Abundance ◽  
Hormone Receptors ◽  
Target Genes ◽  
Receptor Gene ◽  
Rna Extraction ◽  
Amplification Efficiency ◽  
Mrna Levels ◽  
Lh Surge ◽  
Total Rna

The aim of this study was to compare the mRNA levels of hormone receptor for progesterone (PR), oestrogen α (ER-α), oestrogen β (ER-β), and oxytocin (OTR) in canine morulae and blastocysts. Ten healthy mature bitches were inseminated based on monitoring vaginal cytology and progesterone concentration. The first insemination was performed on Day 2 after the preovulatory LH surge (progesterone 4 ng mL–1), and the second was performed 48 h later. All females were submitted to ovariohysterectomy (OVH), and the oviduct as well the uterurs were flushed with PBS solution to obtain the embryos. The females were divided into two groups: Group A (n = 5), morulae were collected 8 days after the LH surge and Group B (n = 5), blastocysts were collected 12 days after the LH surge. The pools (n = 10) of embryos (5 embryos/pool) were stored in RNAlater® (Ambion, Life Technologies, USA) at –80°C. The samples were analysed together. The RNA later was removed used PBS calcium free and the total RNA extraction was performed using the Qiagen RNeasy micro-kit (Hildesheim, Germany). Before reverse-transcription (RT) reaction, the total RNA was treated with DNase I Amplification Grade (Invitrogen Life Technologies, Carlsbad, CA, USA). The gene expression of target genes was assessed by real-time RT-qPCR, using SuperScript III for RT and power SYBR Green PCR Master Mix (Applied Biosystems, USA) for cDNA for PCR. The primers for target genes were designed using the software Primer Express® (Applied Biosystems, USA). The gene expression of target genes was normalized by HPRT gene and the relative abundance of mRNA was determined by the ΔΔct method corrected by amplification efficiency using Pffafl’s equation. The means of mRNA relative abundance were compared by t-test. The PR mRNA expression only in blastocysts is similar to the results obtained by Hou et al. (1997) in rat embryos. It is believed that the absence of PR in the early stages of cleavage is due to the indirect action of progesterone by growth factors produced by the maternal reproductive tract (2). Apparently, ER-β action does not occur in the embryo canine phases analysed; however, the action of ER-α seems related to the deployment signal as seen by Hou et al. (1996) in rats. Similarly to findings in the literature, OTR expression decreased in canine embryonic development. This receptor was produced by blastocysts while present in the uterus, which may represent an incidental mechanism to the embryo control of endometrial receptivity, such as also to prevent the development of endometrial luteolytic mechanism. The variation in hormone receptors gene expression in canine embryos can be influencing the transition from morula to blastocyst. In addition, a hormonal influence on these structures can occur in different ways.

scholarly journals Multiplex Gene Expression Profiling of In Vivo Isolated Circulating Tumor Cells in High-Risk Prostate Cancer Patients

2018 ◽  
Vol 64 (2) ◽  
pp. 297-306 ◽  
Author(s):  
Athina Markou ◽  
Marifili Lazaridou ◽  
Panagiotis Paraskevopoulos ◽  
Shukun Chen ◽  
Monika Świerczewska ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
High Risk ◽  
Expression Profiling ◽  
Stem Cell Markers ◽  
Epithelial Markers ◽  
High Risk Prostate Cancer ◽  
Risk Prostate Cancer ◽  
Emt Markers

Abstract BACKGROUND Molecular characterization of circulating tumor cells (CTCs) is important for selecting patients for targeted treatments. We present, for the first time, results on gene expression profiling of CTCs isolated in vivo from high-risk prostate cancer (PCa) patients compared with CTC detected by 3 protein-based assays—CellSearch®, PSA-EPISPOT, and immunofluorescence of CellCollector® in vivo-captured CTCs—using the same blood draw. METHODS EpCAM-positive CTCs were isolated in vivo using the CellCollector from 108 high-risk PCa patients and 36 healthy volunteers. For 27 patients, samples were available before and after treatment. We developed highly sensitive multiplex RT-qPCR assays for 14 genes (KRT19, EpCAM, CDH1, HMBS, PSCA, ALDH1A1, PROM1, HPRT1, TWIST1, VIM, CDH2, B2M, PLS3, and PSA), including epithelial markers, stem cell markers, and epithelial-to-mesenchymal-transition (EMT) markers. RESULTS We observed high heterogeneity in gene expression in the captured CTCs for each patient. At least 1 marker was detected in 74 of 105 patients (70.5%), 2 markers in 45 of 105 (40.9%), and 3 markers in 16 of 105 (15.2%). Epithelial markers were detected in 31 of 105 (29.5%) patients, EMT markers in 46 of 105 (43.8%), and stem cell markers in 15 of 105 (14.3%) patients. EMT-marker positivity was very low before therapy (2 of 27, 7.4%), but it increased after therapy (17 of 27, 63.0%), whereas epithelial markers tended to decrease after therapy (2 of 27, 7.4%) compared with before therapy (13 of 27, 48.1%). At least 2 markers were expressed in 40.9% of patients, whereas the positivity was 19.6% for CellSearch, 38.1% for EPISPOT, and 43.8% for CellCollector-based IF-staining. CONCLUSIONS The combination of in vivo CTC isolation with downstream RNA analysis is highly promising as a high-throughput, specific, and ultrasensitive approach for multiplex liquid biopsy-based molecular diagnostics.

RNA sequencing analysis for profiling activation of cancer-associated molecular pathways.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e13032-e13032 ◽  
Author(s):  
Anton Buzdin ◽  
Andrew Garazha ◽  
Maxim Sorokin ◽  
Alex Glusker ◽  
Alexey Aleshin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Original Data ◽  
Tissue Expression ◽  
Molecular Pathways ◽  
Sequencing Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Healthy Human ◽  
Tissue Samples ◽  
Normal Tissues

e13032 Background: Intracellular molecular pathways (IMPs) control all major events in the living cell. They are considered hotspots in contemporary oncology because knowledge of IMPs activation is essential for understanding mechanisms of molecular pathogenesis in oncology. Profiling IMPs requires RNA-seq data for tumors and for a collection of reference normal tissues. However, there is a shortage now in such profiles for normal tissues from healthy human donors, uniformly profiled in a single series of experiments. Access to the largest dataset of normal profiles GTEx is only partly available through the dbGaP. In TCGA database, norms are adjacent to surgically removed tumors and may be affected by tumor-linked growth factors, inflammation and altered vascularization. ENCODE datasets were for the autopsies of normal tissues, but they can’t form statistically significant reference groups. Methods: Tissue samples representing 20 organs were taken from post-mortal human healthy donors killed in road accidents no later than 36 hours after death, blood samples were taken from healthy volunteers. Gene expression was profiled in RNA-seq experiments using the same reagents, equipment and protocols. Bioinformatic algorithms for IMP analysis were developed and validated using experimental and public gene expression datasets. Results: From original sequencing data we constructed the biggest fully open reference expression database of normal human tissues including 465 profiles termed Oncobox Atlas of Normal Tissue Expression (ANTE, original data: GSE120795). We next developed a method termed Oncobox for interrogating activation of IMPs in human cancers. It includes modules of expression data harmonization and comparison and an algorithm for automatic annotation of molecular pathways. The Oncobox system enables accurate scoring of thousands molecular pathways using RNA-seq data. Oncobox pathway analysis is also applicable for quantitative proteomics and microRNA data in oncology. Conclusions: The Oncobox system can be used for a plethora of applications in cancer research including finding differentially regulated genes and IMPs, and for discovery of new pathway-related diagnostic and prognostic biomarkers.

scholarly journals Impact of Gene Annotation Choice on the Quantification of RNA-Seq Data

2021 ◽  
Author(s):  
David Chisanga ◽  
Yang Liao ◽  
Wei Shi
Keyword(s):  
Gene Expression ◽  
Gene Annotation ◽  
Expression Data ◽  
Refseq Gene ◽  
Rna Seq ◽  
Sequencing Data ◽  
Microarray Expression Data ◽  
Sequencing Quality ◽  
Gene Expression Quantification ◽  
Expression Quantification

Abstract Background: RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis.Results: In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEquencing Quality Control (SEQC) consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.Conclusion: In conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification.

scholarly journals SPARTA: Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis

2015 ◽  
Author(s):  
Benjamin K Johnson ◽  
Matthew B Scholz ◽  
Tracy K Teal ◽  
Robert B Abramovitch
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Quality Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Data Set ◽  
Bacterial Rna ◽  
Analysis Workflow ◽  
Differential Gene ◽  
Reference Counting

Summary: SPARTA is a reference-based bacterial RNA-seq analysis workflow application for single-end Illumina reads. SPARTA is turnkey software that simplifies the process of analyzing RNA-seq data sets, making bacterial RNA-seq analysis a routine process that can be undertaken on a personal computer or in the classroom. The easy-to-install, complete workflow processes whole transcriptome shotgun sequencing data files by trimming reads and removing adapters, mapping reads to a reference, counting gene features, calculating differential gene expression, and, importantly, checking for potential batch effects within the data set. SPARTA outputs quality analysis reports, gene feature counts and differential gene expression tables and scatterplots. The workflow is implemented in Python for file management and sequential execution of each analysis step and is available for Mac OS X, Microsoft Windows, and Linux. To promote the use of SPARTA as a teaching platform, a web-based tutorial is available explaining how RNA-seq data are processed and analyzed by the software. Availability and Implementation: Tutorial and workflow can be found at sparta.readthedocs.org. Teaching materials are located at sparta-teaching.readthedocs.org. Source code can be downloaded at www.github.com/abramovitchMSU/, implemented in Python and supported on Mac OS X, Linux, and MS Windows. Contact: Robert B. Abramovitch ([email protected]) Supplemental Information: Supplementary data are available online

scholarly journals RNA sequencing data: hitchhiker's guide to expression analysis

2018 ◽  
Author(s):  
Koen Van Den Berge ◽  
Katharina Hembach ◽  
Charlotte Soneson ◽  
Simone Tiberi ◽  
Lieven Clement ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Large Scale ◽  
Science Studies ◽  
Rna Seq ◽  
Sequencing Data ◽  
Data Types ◽  
The Past ◽  
Long Read ◽  
Statistical Approaches

Gene expression is the fundamental level at which the result of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq datasets as well as the performance of the myriad of methods developed. In this review, we give an overall view of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on quantification of gene expression and statistical approaches for differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

scholarly journals GEO2RNAseq: An easy-to-use R pipeline for complete pre-processing of RNA-seq data

2019 ◽  
Author(s):  
Bastian Seelbinder ◽  
Thomas Wolf ◽  
Steffen Priebe ◽  
Sylvie McNamara ◽  
Silvia Gerber ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Species ◽  
Gene Expression Omnibus ◽  
Rna Seq ◽  
Sequencing Data ◽  
Interacting Species ◽  
Link Type ◽  
Fastq Format ◽  
Standard Tool ◽  
Processing Steps

ABSTRACTIn transcriptomics, the study of the total set of RNAs transcribed by the cell, RNA sequencing (RNA-seq) has become the standard tool for analysing gene expression. The primary goal is the detection of genes whose expression changes significantly between two or more conditions, either for a single species or for two or more interacting species at the same time (dual RNA-seq, triple RNA-seq and so forth). The analysis of RNA-seq can be simplified as many steps of the data pre-processing can be standardised in a pipeline.In this publication we present the “GEO2RNAseq” pipeline for complete, quick and concurrent pre-processing of single, dual, and triple RNA-seq data. It covers all pre-processing steps starting from raw sequencing data to the analysis of differentially expressed genes, including various tables and figures to report intermediate and final results. Raw data may be provided in FASTQ format or can be downloaded automatically from the Gene Expression Omnibus repository. GEO2RNAseq strongly incorporates experimental as well as computational metadata. GEO2RNAseq is implemented in R, lightweight, easy to install via Conda and easy to use, but still very flexible through using modular programming and offering many extensions and alternative workflows.GEO2RNAseq is publicly available at https://anaconda.org/xentrics/r-geo2rnaseq and https://bitbucket.org/thomas_wolf/geo2rnaseq/overview, including source code, installation instruction, and comprehensive package documentation.

scholarly journals Transcriptome diversity is a systematic source of bias in RNA-sequencing data

2021 ◽  
Author(s):  
Pablo E. García-Nieto ◽  
Ban Wang ◽  
Hunter B. Fraser
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Systematic Bias ◽  
Simple Explanation ◽  
Rna Seq ◽  
Sequencing Data ◽  
Biological Variables ◽  
Systematic Effects ◽  
Standard Practices ◽  
Transcriptome Diversity

ABSTRACTBackgroundRNA sequencing has been widely used as an essential tool to probe gene expression. While standard practices have been established to analyze RNA-seq data, it is still challenging to detect and remove artifactual signals. Several factors such as sex, age, and sequencing technology have been found to bias these estimates. Probabilistic estimation of expression residuals (PEER) has been used to account for some systematic effects, but it has remained challenging to interpret these PEER factors.ResultsHere we show that transcriptome diversity – a simple metric based on Shannon entropy – explains a large portion of variability in gene expression, and is a major factor detected by PEER. We then show that transcriptome diversity has significant associations with multiple technical and biological variables across diverse organisms and datasets. This prevalent confounding factor provides a simple explanation for a major source of systematic biases in gene expression estimates.ConclusionsOur results show that transcriptome diversity is a metric that captures a systematic bias in RNA-seq and is the strongest known factor encoded in PEER covariates.

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