Subpar reporting of pre-analytical variables in RNA-focused blood plasma studies

Extracellular RNA (cell-free RNA; exRNA) from blood-derived liquid biopsies is an appealing, minimally invasive source of disease biomarkers. As pre-analytical variables strongly influence exRNA measurements, their reporting is essential for meaningful interpretation and replication of results. The aim of this review was to chart to what extent pre-analytical variables are documented, to pinpoint shortcomings and to improve future reporting. In total, 200 blood plasma exRNA studies published in 2018 or 2023 were reviewed for annotation of 22 variables associated with blood collection, plasma preparation, and RNA purification. Our results show that pre-analytical variables are poorly documented, with only three out of 22 variables described in over half of the publications. The percentage of variables reported ranged from 4.6% to 54.6% (mean 24.84%) in 2023 and from 4.6% to 57.1% (mean 28.60%) in 2018. Recommendations and guidelines (i.e., BRISQ, ASCO-CAP, BloodPAC, PPMPT, and CEN standards) have currently not resulted in improved reporting. In conclusion, our results highlight the lack of reporting pre-analytical variables in exRNA studies and advocate for a consistent use of available standards, endorsed by funders and journals.

Whole transcriptome profiling of liquid biopsies from tumour xenografted mouse models enables specific monitoring of tumour-derived extracellular RNA

While cell-free DNA (cfDNA) is widely being investigated, free circulating RNA (extracellular RNA, exRNA) has the potential to improve cancer therapy response monitoring and detection due to its dynamic nature. However, it remains unclear in which blood subcompartment tumour-derived exRNAs primarily reside. We developed a host-xenograft deconvolution framework, exRNAxeno, with mapping strategies to either a combined human-mouse reference genome or both species genomes in parallel, applicable to exRNA sequencing data from liquid biopsies of human xenograft mouse models. The tool enables to distinguish (human) tumoural RNA from (murine) host RNA, to specifically analyse tumour-derived exRNA. We applied the combined pipeline to total exRNA sequencing data from 95 blood-derived liquid biopsy samples from 30 mice, xenografted with 11 different tumours. Tumoural exRNA concentrations are not determined by plasma platelet levels, while host exRNA concentrations increase with platelet content. Furthermore, a large variability in exRNA abundance and transcript content across individual mice is observed. The tumoural gene detectability in plasma is largely correlated with the RNA expression levels in the tumour tissue or cell line. These findings unravel new aspects of tumour-derived exRNA biology in xenograft models and open new avenues to further investigate the role of exRNA in cancer.

CiLiQuant: Quantification of RNA Junction Reads Based on Their Circular or Linear Transcript Origin

Distinguishing circular RNA reads from reads derived from the linear host transcript is a challenging task because of sequence overlap. We developed a computational approach, CiLiQuant, that determines the relative circular and linear abundance of transcripts and gene loci using back-splice and unambiguous forward-splice junction reads generated by existing mapping and circular RNA discovery tools.

RNA biomarkers from proximal liquid biopsy for diagnosis of ovarian cancer

BACKGROUND

Most ovarian cancer patients are diagnosed at an advanced stage and have a high mortality rate. Current screening strategies fail to improve prognosis because markers that are sensitive for early stage disease are lacking. This medical need justifies the search for novel approaches using utero-tubal lavage as a proximal liquid biopsy.

METHODS

In this study, we explore the extracellular transcriptome of utero-tubal lavage fluid obtained from 26 ovarian cancer patients and 48 controls using messenger RNA (mRNA) capture and small RNA sequencing.

RESULTS

We observed an enrichment of ovarian and fallopian tube specific messenger RNAs in utero-tubal lavage fluid compared to other human biofluids. Over 300 mRNAs and 41 miRNAs were upregulated in ovarian cancer samples compared with controls. Upregulated genes were enriched for genes involved in cell cycle activation and proliferation, hinting at a tumor-derived signal.

CONCLUSION

This is a proof-of-principle that mRNA capture sequencing of utero-tubal lavage fluid is technically feasible, and that the extracellular transcriptome of utero-tubal lavage should be further explored in larger cohorts to assess the diagnostic value of the biomarkers identified in this study.

IMPACT

Proximal liquid biopsy from the gynecologic tract is a promising source for mRNA and miRNA biomarkers for diagnosis of early-stage ovarian cancer.

Custom long non-coding RNA capture enhances detection sensitivity in different human sample types

Long non-coding RNAs (lncRNAs) are a heterogeneous group of transcripts that lack protein coding potential and display regulatory functions in various cellular processes. As a result of their cell- and cancer-specific expression patterns, lncRNAs have emerged as potential diagnostic and therapeutic targets. The accurate characterization of lncRNAs in bulk transcriptome data remains challenging due to their low abundance compared to protein coding genes. To tackle this issue, we describe a unique short-read custom lncRNA capture sequencing approach that relies on a comprehensive set of 565,878 capture probes for 49,372 human lncRNA genes. This custom lncRNA capture approach was evaluated on various sample types ranging from artificial high-quality RNA mixtures to more challenging formalin-fixed paraffin-embedded tissue and biofluid material. The custom enrichment approach allows the detection of a more diverse repertoire of lncRNAs, with better reproducibility and higher coverage compared to classic total RNA-sequencing.

Messenger RNA capture sequencing of extracellular RNA from human biofluids using a comprehensive set of spike-in controls

Comprehensive transcriptome analysis of extracellular RNA (exRNA) purified from human biofluids is challenging because of the low RNA concentration and compromised RNA integrity. Here, we describe an optimized workflow to (1) isolate exRNA from different types of biofluids and (2) to prepare messenger RNA (mRNA)-enriched sequencing libraries using complementary hybridization probes. Importantly, the workflow includes 2 sets of synthetic spike-in RNA molecules as processing controls for RNA purification and sequencing library preparation and as an alternative data normalization strategy.

For complete details on the use and execution of this protocol, please refer to Hulstaert et al. (2020).

•

Extracellular RNA from biofluids has a low concentration and a compromised integrity

•

An optimized workflow for mRNA capture sequencing of human biofluids is provided

•

Synthetic spike-in RNA molecules serve as processing controls

•

Spike-in RNAs allow for data normalization and calculation of mRNA concentration

Candidate RNA biomarkers in biofluids for early diagnosis of ovarian cancer: A systematic review

Ovarian cancer is often diagnosed in an advanced stage and is associated with a high mortality rate. It is assumed that early detection of ovarian cancer could improve patient outcomes. Unfortunately, effective screening methods for early diagnosis of ovarian cancer are still lacking. Extracellular RNAs circulating in human biofluids can reliably be measured and are emerging as potential biomarkers in cancer. In this systematic review, we present 75 RNA biomarkers detectable in human biofluids that have been studied for early diagnosis of ovarian cancer. The majority of these markers are microRNAs identified using RT-qPCR or microarrays in blood-based fluids. A handful of studies used RNA-sequencing and explored alternative fluids, such as urine and ascites. Candidate RNA biomarkers that were more abundant in biofluids of ovarian cancer patients compared to controls in at least two independent studies include miR-21, the miR-200 family, miR-205, miR-10a and miR-346. Amongst the markers confirmed to be lower in at least two studies are miR-122, miR-193a, miR-223, miR-126 and miR-106b. While these biomarkers show promising diagnostic potential, further validation is required before implementation in routine clinical care. Challenges related to biomarker validation and reflections on future perspectives to accelerate progress in this field are discussed.

Genome-wide study of the effect of blood collection tubes on the cell-free DNA methylome

The methylation pattern of cfDNA, isolated from liquid biopsies, is gaining substantial interest for diagnosis and monitoring of diseases. We have evaluated the impact of type of blood collection tube and time delay between blood draw and plasma preparation on bisulphite-based cfDNA methylation profiling. Fifteen tubes of blood were drawn from three healthy volunteer subjects (BD Vacutainer K2E EDTA spray tubes, Streck Cell-Free DNA BCT tubes, PAXgene Blood ccfDNA tubes, Roche Cell-Free DNA Collection tubes and Biomatrica LBgard blood tubes in triplicate). Samples were either immediately processed or stored at room temperature for 24 or 72 hours before plasma preparation. DNA fragment size was evaluated by capillary electrophoresis. Reduced representation bisulphite sequencing was performed on the cell-free DNA isolated from these plasma samples. We evaluated the impact of blood tube and time delay on several quality control metrics. All preservation tubes performed similar on the quality metrics that were evaluated. Furthermore, a considerable increase in cfDNA concentration and the fraction of it derived from NK cells was observed after a 72-hour time delay in EDTA tubes. The methylation pattern of cfDNA is robust and reproducible in between the different preservation tubes. EDTA tubes processed as soon as possible, preferably within 24 hours, are the most cost effective. If immediate processing is not possible, preservation tubes are valid alternatives.

Abstract B49: Substantial performance differences among RNA purification kits and blood collection tubes in the Extracellular RNA Quality Control study—important considerations for liquid biopsies

Cancer biomarker studies require procedures that provide accurate and precise test results with high analytical sensitivity. Consequently, the growing use of extracellular RNA from human biofluids as clinically relevant biomarker requires the implementation of benchmarked methods for sample collection, processing, and profiling. While several small-scale studies have pointed at the impact of individual preanalytical factors, no comprehensive study has addressed the many preanalytical variables affecting downstream sequencing of blood-derived exRNAs. In the Extracellular RNA Quality Control study, we have systematically evaluated the type of blood collection tube (n=10, including 5 so-called preservation tubes), the time between blood draw and plasma preparation (n=3), different plasma types (n=3, i.e., platelet-free, -poor, and -rich plasma), and RNA purification methods using the supplier-specified minimum and maximum plasma input volumes (n=15). The impact of these preanalytical factors is assessed by deep transcriptome profiling of all small and messenger RNAs from healthy donors’ plasma, using TruSeq Small RNA sequencing and TruSeq RNA Exome sequencing, respectively. All experiments are conducted in triplicate (for a total of 270 transcriptomes) using 191 synthetic RNA spike-in molecules as processing controls over a relevant dynamic range. When comparing blood collection tubes, serum mRNA seems very similar to EDTA plasma mRNA, but serum-derived small RNAs are markedly different in biotype composition compared to their plasma counterparts. Furthermore, several plasma tubes with preservation reagents do not stabilize RNA very well, as is reflected by increasing RNA concentrations and number of detected genes over time. Also, their reproducibility is generally compromised. In addition, we demonstrate large differences in RNA purification kit performance in terms of reproducibility, sensitivity, and observed transcriptome complexity. Among others, we note a 50-fold difference in mRNA yield and a 5-fold difference in the number of detected mRNAs. We summarized the results in 12 performance parameters that enable an informed selection of the most optimal sample processing workflow. In conclusion, using a systematic approach, we put forward robust quality control metrics for exRNA quantification methods with validated SOPs for sample collection, processing, and profiling. Our results are crucially important for all future RNA-based liquid biopsy-guided precision oncology applications. Authors in random order; abstract submitted on behalf of the exRNAQC Consortium.

Citation Format: Anneleen Decock, Jasper Anckaert, Celine Everaert, Justine Nuytens, Scott Kuersten, An Hendrix, Jo Vandesompele, Pieter Mestdagh, Bert Dhondt, Ruben Van Paemel, Kimberly Verniers, Gary Schroth, Carolina Fierro, Nurten Yigit, Kathleen Schoofs, Annelien Morlion, Jill Deleu, Eva Hulstaert, Francisco Avila Cobos, Nele Nijs, Eveline Vanden Eynde, Hetty Hilde Helsmoortel, Olivier De Wever, Annouck Philippron. Substantial performance differences among RNA purification kits and blood collection tubes in the Extracellular RNA Quality Control study—important considerations for liquid biopsies [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr B49.

SPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups

BACKGROUND

To understand biology and differences among various tissues or cell types, one typically searches for molecular features that display characteristic abundance patterns. Several specificity metrics have been introduced to identify tissue-specific molecular features, but these either require an equal number of replicates per tissue or they can't handle replicates at all.

RESULTS

We describe a non-parametric specificity score that is compatible with unequal sample group sizes. To demonstrate its usefulness, the specificity score was calculated on all GTEx samples, detecting known and novel tissue-specific genes. A webtool was developed to browse these results for genes or tissues of interest. An example python implementation of SPECS is available at https://github.com/celineeveraert/SPECS. The precalculated SPECS results on the GTEx data are available through a user-friendly browser at specs.cmgg.be.

CONCLUSIONS

SPECS is a non-parametric method that identifies known and novel specific-expressed genes. In addition, SPECS could be adopted for other features and applications.