Exploiting RNA in Liquid Biopsies for Precision Medicine Purposes

Jo Vandesompele1,2
1) Ghent University, Belgium;
2) Biogazelle, Ghent, Belgium;

Abstract
In contrast to general belief, a substantial part of the human transcriptome is abundantly present in the blood and other biofluids as extracellular messenger RNA, long non-coding RNAs and various small RNAs, ready to exploited. I will discuss various workflows for RNA sequencing of biofluid derived RNA, including probe-based target capture and unbiased total RNA library prep as sensitive RNA sequencing workflow to study thousands of mRNA and lncRNA genes in cell-free RNA from patients’ plasma and other biofluids. Apart from RNA abundance profiling, this type of data can also be used to detect structural RNA variants, such as somatic mutations, fusion genes and RNA editing events, all known to play an important role in disease, including cancer. The resulting RNA profiles can be deconvoluted to enumerate the cells, tissues and organs that contribute to the extracellular RNA. Human biofluid RNA sequencing enables liquid biopsy guided precision oncology, such as therapy stratification, treatment response monitoring and early detection of relapse. I will also discuss the pre-analytical jungle of RNA targeted liquid biopsies and need for standardization, as part of the ongoing extracellular RNA quality control study. I will end with the first insights of the Human Biofluid RNA Atlas, in which we have deeply probed into the extracellular transcriptome of 22 human biofluids, providing a solid foundation for exploiting biofluids for diagnostic purposes. (on behalf of Extracellular RNA Quality Control consortium, Human Biofluid RNA Atlas consortium).

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HTPathwaySeq, a Novel Application for High-throughput RNA Sequencing Based Pathway Phenotyping

Pieter Mestdagh , Tom Maes, Manuel Luypaert, Nele Nys, Gert Van Peer, Ariane De Ganck, Carolina Fierro, Jan Hellemans, Jo Vandesompele
Biogazelle, Belgium

Abstract
Different technologies support researchers in probing the transcriptome. The choice among these technologies is guided in part by the balance between the amount of data one wishes to obtain for a given sample and the number of samples being tested. Typically, these parameters are inversely correlated. At the opposite ends of this spectrum, deep RNA sequencing and qPCR yield in depth data for tens of samples starting at a total cost of at least 300€/sample or very directed information for thousands of samples at a cost below 10€ per sample, respectively. We here present HTPathwaySeq, a technology situated in the middle of this spectrum, tailored towards researchers looking for maximal molecular insights for their in vitrostudies.
At a cost below 100 EUR/sample, HTPathwaySeq processes 384 cell lysates with RNA seq to generate expression data analyzed at pathway level. Our data shows that shallow sequencing of crude cell lysates reproducibly detects over 5000 genes with at least 10 reads. Subsampling of deep sequencing datasets demonstrated that differential pathway analysis is largely unaffected when reducing the number of genes to this level. Consequently, reliable pathway insights can be obtained at high throughput and relatively low cost while not being limited to a predefined set of genes or pathways. In cell perturbation screenings (small molecules, RNAi, antisense or CRISPR), HTPathwaySeq can provide in depth information on the mode of action underlying the induced cellular phenotypes as well as molecular similarity scores to identify those perturbations acting similar to a reference condition or via shared molecular mechanisms. We will show results from a lead compound dose response study, illustrating the potential of HTPathwaySeq.


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mRNA capture sequencing enabled liquid biopsy precision oncology

Jo Vandesompele 1,
1Biogazelle, Belgium; 

Abstract
In contrast to general belief, a substantial part of the human protein coding transcriptome is abundantly present in the blood as extracellular mRNA, ready to exploited. Here, I present probe based mRNA capture as a sensitive RNA sequencing workflow to study thousands of mRNA genes in cell-free RNA from cancer patients’ plasma. Apart from RNA abundance profiling, this type of data can also be use to detect structural RNA variants, such as somatic muta- tions, and RNA editing events, all known to play an important role in cancer. RNA capture sequencing enables liquid biopsy guided pre- cision oncology, such as therapy stratification, treatment response monitoring and early detection of relapse. I will also discuss the preanalytical jungle of RNA targeted liquid biopsies and need for standardization, as part of the ongoing exRNAQC study.
http://dx.doi.org/10.1016/j.bdq.2017.02.022

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RDML qPCR Data Format – Ready For The Next Level?

Andreas Untergasser1, Steve Lefever2, Jasper Anckaert2, Jan M Ruijter3, Jan Hellemans4, Jo Vandesompele2,4
1University of Heidelberg, Heidelberg, Germany; 
2Ghent University, Ghent, Belgium; 
3Academic Medical Center, Amsterdam, The Netherlands; 
4Biogazelle, Zwijnaarde, Belgium

Abstract
Quantitative PCR (qPCR) is the gold standard method for accurate and sensitive nucleic acid quantification. To improve the quality and transparency of experiment design, data-analysis and reporting of results, the MIQE guidelines were established in 2009 (Bustin et al., Clinical Chemistry). The Real-time PCR Data Markup Language (RDML) was designed to establish a vendor independent, freely available XML based file format to store and exchange qPCR data (Lefever et al., NAR). RDML stores the raw data acquired by the machine as well as the information required for its interpretation, such as sample annotation, primer and probe sequences and cycling protocol.
Today, several instrument manufacturers realized its potential and implemented functionality to export data in the RDML-format. Third party software (LinRegPCR and qbasePLUS) uses this information for advanced data analysis. Due to the flexibility of RDML, the majority of the current software uses only parts of the format. Furthermore, with different RDML versions available, the need to convert between versions became obvious. The open source editor RDML-Ninja was designed to edit RDML-files and convert between different versions (sourceforge.net/projects/qpcr-ninja/). It should serve as reference implementation of the RDML-format and assist researchers, reviewers as well as software developers by offering access to all data in an RDML-file.
Ultimately, RDML could be extended to store all information required by MIQE. Currently the information required by MIQE seems overwhelming to a researcher, but RDML offers an easy way out. All the information would be only entered once and stored in a basic RDML file. Researchers would not have to re-enter this information with every qPCR run, but will import from this RDML file only the parts needed for the current qPCR run. Furthermore integration of MIQE in RDML and RDML-Ninja would allow checking to which extend MIQE information is provided by calculating the checklist completeness based on a provided RDML-file. We would like to discuss this vision, its chances and its applicability.

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Removal of Between-Plate Variation in qPCR with Factor Correction: Completion of the Analysis Pipeline Supported by RDML

Jan Ruijter1, Jan Hellemans2, Adrian Ruiz-Villalba1, Maurice Van Den Hoff1, Andreas Untergasser3
1Academic Medical Center, the Netherlands; 
2Biogazelle, Belgium; 
3Heidelberg University, Heidelberg, Germany

Abstract
Quantitative PCR is the method of choice in gene expression analysis. However, the number of experimental conditions, target genes and technical replicates quickly exceeds the capacity of the qPCR machines. Statistical analysis of the resulting data then requires the correction of between-plate variation. Application of calibrator samples, with replicate measurements distributed over the plates assumes a multiplicative difference between plates. However, random and technical errors in these calibrators will propagate to all samples on the plate. To avoid this effect, the systematic bias can better be corrected when there is a maximal overlap between plates using Factor Correction [Ruijter et al. Retrovirology, 2006]. The original Factor Correction program is based on Excel input and calculates corrected target quantities. To implement this correction into the analysis pipeline from raw data through LinRegPCR into qbase-plus, a new version of the program was created to handle RDML files. This version saves the corrected N0 values as efficiency-corrected Cq values to be used in further calculations. This program thus completes the analysis pipeline of qPCR data supported by RDML.

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Decoding lncRNA functions using high-throughput pathway perturbation.

Pieter Mestdagh, Jan Hellemans, Ariane De Ganck, Jo Vandesompele
Biogazelle, Belgium

Abstract
Genome-wide studies have shown that our genome is pervasively transcribed, producing a complex pool of coding and non-coding transcripts that shape a cell’s transcriptome. Long non-coding RNAs or lncRNAs dominate the non-coding transcriptome and are emerging as key regulatory factors in human disease and development. Still, only a fraction of lncRNAs has been studied experimentally. In order to gain insights in lncRNA functions on a genome-wide scale, we performed high-throughput pathway perturbations followed by total RNA sequencing. Cells were treated with 90 targeted compounds and 90 transcription factor siRNAs, yielding a total of 180 individual perturbations. For each perturbation, differentially expressed lncRNAs were identified and mapped to pathways using matching protein-coding gene expression data. We define a functional context for thousands of lncRNAs that can serve as a starting point to guide more focused experimental studies and accelerate lncRNA research.

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Benchmarking of RNA-seq data processing pipelines using whole transcriptome qPCR expression data

Jan Hellemans1, Jo Vandesompele1,2, Pieter Mestdagh1,2
1Biogazelle, Belgium; 
2CMGG, UGent, Belgium

Abstract
RNA sequencing is becoming increasingly popular to perform transcriptome wide gene expression analyses. The recently published SEQC study assessed the performance and key characteristics of RNA-seq by sequencing the MAQC samples to very deep coverage. We have extended this study by detailed comparison of the results generated by different data processing pipelines against those obtained by transcriptome wide qPCR measurements. The relative performance and differences between 4 pipelines (Sailfish, tophat-HTseq, star-HTseq and tophat-cufflinks) as well as their concordance to qPCR data will be presented.

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Updated evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study

Pieter Mestdagh, Jo Vandesompele
Ghent University / Biogazelle, Belgium (on behalf of the microRNA quality control study consortium)

Abstract
MicroRNAs are important negative regulators of protein-coding gene expression and have been studied intensively over the past years. Several measurement platforms have been developed to determine relative miRNA abundance in biological samples using different technologies such as small RNA sequencing, reverse transcription–quantitative PCR (RT-qPCR) and (microarray) hybridization. In this study, we systematically compared 14 commercially available platforms for analysis of microRNA expression. We measured an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples and synthetic spikes from microRNA family members with varying homology. We developed robust quality metrics to objectively assess platform performance in terms of reproducibility, sensitivity, accuracy, specificity and concordance of differential expression. The results indicate that each method has its strengths and weaknesses, which help to guide informed selection of a quantitative microRNA gene expression platform for particular study goals.

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qbasePLUS to speed up the analysis of your qPCR data and to improve the accuracy of your experiments

Barbara D’haene
Biogazelle, Belgium

Abstract
Are you struggling to get your qPCR data-analysis right? Do you want to speed up your analysis?
Join Barbara D’haene, PhD, for a lunch talk and get access to qbasePLUS.
During this session Barbara will show how to analyse a qPCR experiment using qbasePLUS. The key points demonstrated will be quality control, normalization and easy biostatistical analysis.
qbasePLUS is based on the proven geNorm and qBase technology. The software is developed at Biogazelle by recognized qPCR experts Jo Vandesompele and Jan Hellemans. Biogazelle is a young and dynamic PCR company, eager to accelerate the discoveries in the PCR community.


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High-throughput lncRNA expression profiling identifies candidate cancer lncRNAs

Pieter Mestdagh1, Steve Lefever1, Kristina Althoff2, Carina Leonelli1, Jan Hellemans3, Marine Jean-Christophe4, Johannes Schulte2, Jo Vandesompele1
1Center for Medical Genetics, Ghent University, Belgium; 2Department of Pediatric-Oncology, University Hospital Essen, Germany; 3Biogazelle, Ghent, Belgium; 4VIB Laboratory for Molecular Cancer Biology, Leuven, Belgium

Abstract

Recent studies suggest that our genome is pervasively transcribed and produces many tens of thousands of long non-coding RNAs (lncRNAs). These lncRNAs have been implicated in gene expression regulation through direct interaction with chromatin modifying complexes and their subsequent recruitment to target loci in the genome. To date, only a handful of lncRNAs have been described with dcoumented functions in cancer biology. However, their critical role as regulators of gene expression suggests that lncRNAs, much like microRNAs, might be key components of different cancer pathways.
In order to study lncRNAs in cancer, we designed and extensively validated a high-throughput RT-qPCR lncRNA expression profiling platform capable of quantifying over 1700 lncRNAs in a single run. This platform has been applied to identify lncRNAs downstream of two major cancer genes, MYC and TP53, by means of inducible model systems. Furthermore, we measured lncRNA expression of the entire NCI60 cancer cell line panel.
Both MYC and TP53 were found to directly induce lncRNA expression through direct promoter binding. The findings were validated in primary samples and model systems of different tumour entities. From the NCI60 panel, we identified cancer specific lncRNA signatures, reminiscent of lineage survival oncogene expression patterns. Together, our results suggest that RT-qPCR is a valid screening approach for high-throughput lncRNA quantification revealing multiple candidate cancer lncRNAs.


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