Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision.

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is high and similar (median of 98.8%, 96.3% and 95.5% for qPCR, RNase R and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation.

Interactome of the HIV-1 proteome and human host RNA.

The human immunodeficiency virus (HIV-1) is highly dependent on a variety of host factors. Beside proteins, host RNA molecules are reported to aid HIV-1 replication and latency maintenance. Here, we implement multiple workflows of native RNA immunoprecipitation and sequencing (nRIPseq) to determine direct host RNA interaction partners of all 18 HIV-1 (poly)proteins. We identify 1,727 HIV-1 protein - human RNA interactions in the Jurkat cell line and 1,558 interactions in SupT1 cells for a subset of proteins, and discover distinct cellular pathways that seem to be used or controlled by HIV-1 on the RNA level: Tat binds mRNAs of proteins involved in the super elongation complex (AFF1-4, Cyclin-T1). Correlation of the interaction scores (based on binding abundancy) allows identifying the highest confidence interactions, for which we perform a small-scale knockdown screen that leads to the identification of three HIV-1 protein binding RNA interactors involved in HIV-1 replication (AFF2, H4C9 and RPLP0).

Characterizing the Cell-Free Transcriptome in a Humanized Diffuse Large B-Cell Lymphoma Patient-Derived Tumor Xenograft Model for RNA-Based Liquid Biopsy in a Preclinical Setting.

The potential of RNA-based liquid biopsy is increasingly being recognized in diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin's lymphoma. This study explores the cell-free transcriptome in a humanized DLBCL patient-derived tumor xenograft (PDTX) model. Blood plasma samples (n = 171) derived from a DLBCL PDTX model, including 27 humanized (HIS) PDTX, 8 HIS non-PDTX, and 21 non-HIS PDTX non-obese diabetic (NOD)-scid IL2Rgnull (NSG) mice were collected during humanization, xenografting, treatment, and sacrifice. The mice were treated with either rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), CD20-targeted human IFNα2-based AcTaferon combined with CHOP (huCD20-Fc-AFN-CHOP), or phosphate-buffered saline (PBS). RNA was extracted using the miRNeasy serum/plasma kit and sequenced on the NovaSeq 6000 platform. RNA sequencing data of the formalin-fixed paraffin-embedded (FFPE) tissue and blood plasma samples of the original patient were included. Flow cytometry was performed on immune cells isolated from whole blood, spleen, and bone marrow. Bulk deconvolution was performed using the Tabula Sapiens v1 basis matrix. Both R-CHOP and huCD20-Fc-AFN-CHOP were able to control tumor growth in most mice. Xenograft tumor volume was strongly associated with circulating tumor RNA (ctRNA) concentration (p < 0.001, R = 0.89), as well as with the number of detected human genes (p < 0.001, R = 0.79). Abundance analysis identified tumor-specific biomarkers that were dynamically tracked during tumor growth or treatment. An 8-gene signature demonstrated high accuracy for assessing therapy response (AUC 0.92). The tumoral gene detectability in the ctRNA of the PDTX-derived plasma was associated with RNA abundance levels in the patient's tumor tissue and blood plasma (p < 0.001), confirming that tumoral gene abundance contributes to the cell-free RNA (cfRNA) profile. Decomposing the transcriptome, however, revealed high inter- and intra-mouse variability, which was lower in the HIS PDTX mice, indicating an impact of human engraftment on the stability and profile of cfRNA. Immunochemotherapy resulted in B cell depletion, and tumor clearance was reflected by a decrease in the fraction of human CD45+ cells. Lastly, bulk deconvolution provided complementary biological insights into the composition of the tumor and circulating immune system. In conclusion, the blood plasma-derived transcriptome serves as a biomarker source in a preclinical PDTX model, enables the assessment of biological pathways, and enhances the understanding of cfRNA dynamics.

RNA Extraction Method Impacts Quality Metrics and Sequencing Results in Formalin-Fixed, Paraffin-Embedded Tissue Samples.

Archived formalin-fixed, paraffin-embedded (FFPE) tissue samples are being increasingly used in molecular cancer research. Compared with fresh-frozen tissue, the nucleic acid analysis of FFPE tissue is technically more challenging. This study aimed to compare the impact of 3 different RNA extraction methods on yield, quality, and sequencing-based gene expression results in FFPE samples. RNA extraction was performed in 16 FFPE tumor specimens from patients with diffuse large B-cell lymphoma and in reference FFPE material from microsatellite-stable and microsatellite-instable cell lines (3 replicates each) using 2 silica-based procedures (A, miRNeasy FFPE; C, iCatcher FFPE Tissue RNA) and 1 isotachophoresis-based procedure (B, Ionic FFPE to Pure RNA). The RNA yield; RNA integrity, as reflected by the distribution value 200; and RNA purity, as reflected by the 260/280 and the 260/230 nm absorbance ratios, were determined. The RNA was sequenced on the NovaSeq 6000 instrument using the TruSeq RNA Exome and SMARTer Stranded Total RNA-Seq Pico v3 library preparations kits. Our results highlight the impact of RNA extraction methodology on both preanalytical and sequencing-based gene expression results. Overall, methods B and C outperformed method A because these showed significantly higher fractions of uniquely mapped reads, an increased number of detectable genes, a lower fraction of duplicated reads, and better representation of the B-cell receptor repertoire. Differences among the extraction methods were generally more explicit for the total RNA sequencing method than for the exome-capture sequencing method. Importantly, the predicative value of quality metrics varies among extraction kits, and caution should be applied when comparing and interpreting results obtained using different methods.

Blocking Abundant RNA Transcripts by High-Affinity Oligonucleotides during Transcriptome Library Preparation.

BACKGROUND: RNA sequencing has become the gold standard for transcriptome analysis but has an inherent limitation of challenging quantification of low-abundant transcripts. In contrast to microarray technology, RNA sequencing reads are proportionally divided in function of transcript abundance. Therefore, low-abundant RNAs compete against highly abundant - and sometimes non-informative - RNA species. RESULTS: We developed an easy-to-use strategy based on high-affinity RNA-binding oligonucleotides to block reverse transcription and PCR amplification of specific RNA transcripts, thereby substantially reducing their abundance in the final sequencing library. To demonstrate the broad application potential of our method, we applied it to different transcripts and library preparation strategies, including YRNAs in small RNA sequencing of human blood plasma, mitochondrial rRNAs in both 3' end sequencing and long-read sequencing, and MALAT1 in single-cell 3' end sequencing. We demonstrate that the blocking strategy is highly efficient, reproducible, specific, and generally results in better transcriptome coverage and complexity. CONCLUSION: Our method does not require modifications of the library preparation procedure apart from simply adding blocking oligonucleotides to the RT reaction and can thus be easily integrated into virtually any RNA sequencing library preparation protocol.

Digital PCR-based evaluation of nucleic acid extraction kit performance for the co-purification of cell-free DNA and RNA.

BACKGROUND: Blood plasma, one of the most studied liquid biopsies, contains various molecules that have biomarker potential for cancer detection, including cell-free DNA (cfDNA) and cell-free RNA (cfRNA). As the vast majority of cell-free nucleic acids in circulation are non-cancerous, a laboratory workflow with a high detection sensitivity of tumor-derived nucleic acids is a prerequisite for precision oncology. One way to meet this requirement is by the combined analysis of cfDNA and cfRNA from the same liquid biopsy sample. So far, no study has systematically compared the performance of cfDNA and cfRNA co-purification to increase sensitivity. RESULTS: First, we set up a framework using digital PCR (dPCR) technology to quantify cfDNA and cfRNA from human blood plasma in order to compare cfDNA/cfRNA co-purification kit performance. To that end, we optimized two dPCR duplex assays, designed to quantify both cfDNA and cfRNA with the same assays, by ensuring that primers and probes are located within a highly abundant exon. Next, we applied our optimized workflow to evaluate the co-purification performance of two manual and two semi-automated methods over a range of plasma input volumes (0.06-4 mL). Some kits result in higher nucleic acid concentrations in the eluate, while consuming only half of the plasma volume. The combined nucleic acid quantification systematically results in higher nucleic acid concentrations as compared to a parallel quantification of cfDNA and cfRNA in the eluate. CONCLUSIONS: We provide a framework to evaluate the performance of cfDNA/cfRNA co-purification kits and have tested two manual and two semi-automated co-purification kits in function of the available plasma input amount and the intended use of the nucleic acid eluate. We demonstrate that the combined quantification of cfDNA and cfRNA has a benefit compared to separate quantification. We foresee that the results of this study are instrumental for clinical applications to help increase mutation detection sensitivity, allowing improved disease detection and monitoring.

mRNA Capture Sequencing and RT-qPCR for the Detection of Pathognomonic, Novel, and Secondary Fusion Transcripts in FFPE Tissue: A Sarcoma Showcase.

We assess the performance of mRNA capture sequencing to identify fusion transcripts in FFPE tissue of different sarcoma types, followed by RT-qPCR confirmation. To validate our workflow, six positive control tumors with a specific chromosomal rearrangement were analyzed using the TruSight RNA Pan-Cancer Panel. Fusion transcript calling by FusionCatcher confirmed these aberrations and enabled the identification of both fusion gene partners and breakpoints. Next, whole-transcriptome TruSeq RNA Exome sequencing was applied to 17 fusion gene-negative alveolar rhabdomyosarcoma (ARMS) or undifferentiated round cell sarcoma (URCS) tumors, for whom fluorescence in situ hybridization (FISH) did not identify the classical pathognomonic rearrangements. For six patients, a pathognomonic fusion transcript was readily detected, i.e., PAX3-FOXO1 in two ARMS patients, and EWSR1-FLI1, EWSR1-ERG, or EWSR1-NFATC2 in four URCS patients. For the 11 remaining patients, 11 newly identified fusion transcripts were confirmed by RT-qPCR, including COPS3-TOM1L2, NCOA1-DTNB, WWTR1-LINC01986, PLAA-MOB3B, AP1B1-CHEK2, and BRD4-LEUTX fusion transcripts in ARMS patients. Additionally, recurrently detected secondary fusion transcripts in patients diagnosed with EWSR1-NFATC2-positive sarcoma were confirmed (COPS4-TBC1D9, PICALM-SYTL2, SMG6-VPS53, and UBE2F-ALS2). In conclusion, this study shows that mRNA capture sequencing enhances the detection rate of pathognomonic fusions and enables the identification of novel and secondary fusion transcripts in sarcomas.

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.

Exploring the cell-free total RNA transcriptome in diffuse large B-cell lymphoma and primary mediastinal B-cell lymphoma patients as biomarker source in blood plasma liquid biopsies.

Introduction: Diffuse large B-cell lymphoma (DLBCL) and primary mediastinal B-cell lymphoma (PMBCL) are aggressive histological subtypes of non-Hodgkin's lymphoma. Improved understanding of the underlying molecular pathogenesis has led to new classification and risk stratification tools, including the development of cell-free biomarkers through liquid biopsies. The goal of this study was to investigate cell-free RNA (cfRNA) biomarkers in DLBCL and PMBCL patients. Materials and methods: Blood plasma samples (n=168) and matched diagnostic formalin-fixed paraffin-embedded (FFPE) tissue samples (n=69) of DLBCL patients, PMBCL patients and healthy controls were collected between 2016-2021. Plasma samples were collected at diagnosis, at interim evaluation, after treatment, and in case of refractory or relapsed disease. RNA was extracted from 200 µl plasma using the miRNeasy serum/plasma kit and from FFPE tissue using the miRNeasy FFPE kit. RNA was subsequently sequenced on a NovaSeq 6000 instrument using the SMARTer Stranded Total RNA-seq pico v3 library preparation kit. Results: Higher cfRNA concentrations were demonstrated in lymphoma patients compared to healthy controls. A large number of differentially abundant genes were identified between the cell-free transcriptomes of DLBCL patients, PMBCL patients, and healthy controls. Overlap analyses with matched FFPE samples showed that blood plasma has a unique transcriptomic profile that significantly differs from that of the tumor tissue. As a good concordance between tissue-derived gene expression and the immunohistochemistry Hans algorithm for cell-of-origin (COO) classification was demonstrated in the FFPE samples, but not in the plasma samples, a 64-gene cfRNA classifier was developed that can accurately determine COO in plasma. High plasma levels of a 9-gene signature (BECN1, PRKCB, COPA, TSC22D3, MAP2K3, UQCRHL, PTMAP4, EHD1, NAP1L1 pseudogene) and a 5-gene signature (FTH1P7, PTMAP4, ATF4, FTH1P8, ARMC7) were significantly associated with inferior progression-free and overall survival in DLBCL patients, respectively, independent of the NCCN-IPI score. Conclusion: Total RNA sequencing of blood plasma samples allows the analysis of the cell-free transcriptome in DLBCL and PMBCL patients and demonstrates its unexplored potential in identifying diagnostic, cell-of-origin, and prognostic cfRNA biomarkers.

A 3'-end capture sequencing method for high-throughput targeted gene expression profiling.

Molecular phenotyping through shallow 3'-end RNA-sequencing workflows is increasingly applied in the context of large-scale chemical or genetic perturbation screens to study disease biology or support drug discovery. While these workflows enable accurate quantification of the most abundant genes, they are less effective for applications that require expression profiling of low abundant transcripts, like long noncoding RNAs (lncRNAs), or selected gene panels. To tackle these issues, we describe a workflow combining 3'-end library preparation with 3'-end hybrid capture probes and shallow RNA-sequencing for cost-effective, targeted quantification of subsets of (low abundant) genes across hundreds to thousands of samples. To assess the performance of the method, we designed a capture probe set for more than 100 mRNA and lncRNA target genes and applied the workflow to a cohort of 360 samples. When compared to standard 3'-end RNA-sequencing, 3'-end capture sequencing resulted in a more than 200-fold enrichment of target gene abundance while conserving relative intergene and intersample abundances. 3'-end RNA capture sequencing enables accurate targeted gene expression profiling at extremely shallow sequencing depth.

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.

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.

Validation of Circular RNAs Using RT-qPCR After Effective Removal of Linear RNAs by Ribonuclease R.

Circular RNAs (circRNAs) are a class of endogenous noncoding RNAs that have been shown to play a role in normal development, homeostasis, and disease, including cancer. CircRNAs are formed through a process called back-splicing, which results in a covalently closed loop with a nonlinear back-spliced junction (BSJ). In general, circRNA BSJs are predicted in RNA sequencing data using one of numerous circRNA detection algorithms. Selected circRNAs are then typically validated using an orthogonal method such as reverse transcription quantitative PCR (RT-qPCR) with circRNA-specific primers. However, linear transcripts originating from endogenous trans-splicing can lead to false-positive signals both in RNA sequencing and in RT-qPCR experiments. Therefore, it is essential to perform the RT-qPCR validation step only after linear RNAs have been degraded using an exonuclease such as ribonuclease R (RNase R). Several RNase R protocols are available for circRNA detection using RNA sequencing or RT-qPCR. These protocols-which vary in enzyme concentration, RNA input amount, incubation times, and cleanup steps-typically lack a detailed validated standard protocol and fail to provide a range of conditions that deliver accurate results. As such, some protocols use RNase R concentrations that are too high, resulting in partial degradation of the target circRNAs. Here, we describe an optimized workflow for circRNA validation, combining RNase R treatment and RT-qPCR. First, we outline the steps for circRNA primer design and qPCR assay validation. Then, we describe RNase R treatment of total RNA and, importantly, a subsequent essential buffer cleanup step. Lastly, we outline the steps to perform the RT-qPCR and discuss the downstream data analyses. © 2021 Wiley Periodicals LLC. Basic Protocol 1: CircRNA primer design and qPCR assay validation Basic Protocol 2: RNase R treatment, cleanup, and RT-qPCR.

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).

Charting Extracellular Transcriptomes in The Human Biofluid RNA Atlas.

Extracellular RNAs present in biofluids have emerged as potential biomarkers for disease. Where most studies focus on blood-derived fluids, other biofluids may be more informative. We present an atlas of messenger, circular, and small RNA transcriptomes of a comprehensive collection of 20 human biofluids. By means of synthetic spike-in controls, we compare RNA content across biofluids, revealing a 10,000-fold difference in concentration. The circular RNA fraction is increased in most biofluids compared to tissues. Each biofluid transcriptome is enriched for RNA molecules derived from specific tissues and cell types. Our atlas enables an informed selection of the most relevant biofluid to monitor particular diseases. To verify the biomarker potential in these biofluids, four validation cohorts representing a broad spectrum of diseases were profiled, revealing numerous differential RNAs between case and control subjects. Spike-normalized data are publicly available in the R2 web portal for further exploration.

Performance assessment of total RNA sequencing of human biofluids and extracellular vesicles.

RNA profiling has emerged as a powerful tool to investigate the biomarker potential of human biofluids. However, despite enormous interest in extracellular nucleic acids, RNA sequencing methods to quantify the total RNA content outside cells are rare. Here, we evaluate the performance of the SMARTer Stranded Total RNA-Seq method in human platelet-rich plasma, platelet-free plasma, urine, conditioned medium, and extracellular vesicles (EVs) from these biofluids. We found the method to be accurate, precise, compatible with low-input volumes and able to quantify a few thousand genes. We picked up distinct classes of RNA molecules, including mRNA, lncRNA, circRNA, miscRNA and pseudogenes. Notably, the read distribution and gene content drastically differ among biofluids. In conclusion, we are the first to show that the SMARTer method can be used for unbiased unraveling of the complete transcriptome of a wide range of biofluids and their extracellular vesicles.

Targeted resequencing and variant validation using pxlence PCR assays.

The advent of next-generation sequencing technologies had a profound impact on molecular diagnostics. PCR is a popular method for target enrichment of disease gene panels. Using our proprietary primer-design pipeline, primerXL, we have created almost one million assays covering over 98% of the human exome. Here we describe the assay specification and both in silico and wet-lab validation of a selected set of 2294 assays using both next-generation sequencing and Sanger sequencing. Using a universal PCR protocol without optimization, these assays result in high coverage uniformity and limited non-specific coverage. In addition, data indicates a positive correlation between the predictive in silico specificity score and the amount of assay non-specific coverage.

Methyl-CpG-binding domain sequencing reveals a prognostic methylation signature in neuroblastoma.

Accurate assessment of neuroblastoma outcome prediction remains challenging. Therefore, this study aims at establishing novel prognostic tumor DNA methylation biomarkers. In total, 396 low- and high-risk primary tumors were analyzed, of which 87 were profiled using methyl-CpG-binding domain (MBD) sequencing for differential methylation analysis between prognostic patient groups. Subsequently, methylation-specific PCR (MSP) assays were developed for 78 top-ranking differentially methylated regions and tested on two independent cohorts of 132 and 177 samples, respectively. Further, a new statistical framework was used to identify a robust set of MSP assays of which the methylation score (i.e. the percentage of methylated assays) allows accurate outcome prediction. Survival analyses were performed on the individual target level, as well as on the combined multimarker signature. As a result of the differential DNA methylation assessment by MBD sequencing, 58 of the 78 MSP assays were designed in regions previously unexplored in neuroblastoma, and 36 are located in non-promoter or non-coding regions. In total, 5 individual MSP assays (located in CCDC177, NXPH1, lnc-MRPL3-2, lnc-TREX1-1 and one on a region from chromosome 8 with no further annotation) predict event-free survival and 4 additional assays (located in SPRED3, TNFAIP2, NPM2 and CYYR1) also predict overall survival. Furthermore, a robust 58-marker methylation signature predicting overall and event-free survival was established. In conclusion, this study encompasses the largest DNA methylation biomarker study in neuroblastoma so far. We identified and independently validated several novel prognostic biomarkers, as well as a prognostic 58-marker methylation signature.