Comparative Analyses of Liquid-Biopsy MicroRNA371a-3p Isolation Protocols for Serum and Plasma.
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ABSTRACT:
SUBMITTER: Timmerman DM
PROVIDER: S-EPMC8428229 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Lung cancer is the leading cause of cancer-related deaths worldwide. Recent implementation of low-dose computed tomography (LDCT) screening is predicted to lead to diagnosis of lung cancer at an earlier stage, with survival benefit. However, there is still a pressing need for biomarkers that will identify individuals eligible for screening, as well as improve the diagnostic accuracy of LDCT. In addition, biomarkers for prognostic stratification of patients with early stage disease, and those that can be used as surrogates to monitor tumor evolution, will greatly improve clinical management. Molecular alterations found in the DNA of tumor cells, such as mutations, translocations and methylation, are reflected in DNA that is released from the tumor into the bloodstream. Thus, in recent years, circulating tumor DNA (ctDNA) has gained increasing attention as a noninvasive alternative to tissue biopsies and potential surrogate for the entire tumor genome. Activating gene mutations found in ctDNA have been proven effective in predicting response to targeted therapy. Analysis of ctDNA is also a valuable tool for longitudinal follow-up of cancer patients that does not require serial biopsies and may anticipate the acquisition of resistance. DNA methylation has also emerged as a promising marker for early detection, prognosis and real-time follow-up of tumor dynamics that is independent of the genomic composition of the primary tumor. This review summarizes the various investigational applications of methylated ctDNA in lung cancer reported to date. It also provides a brief overview of the technologies for analysis of DNA methylation in liquid biopsies, and the challenges that befall the implementation of methylated ctDNA into routine clinical practice.
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Project description:With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.
| S-EPMC8308091 | biostudies-literature
Project description:Purpose: We compare small noncoding RNA (ncRNA) profiles between exosomes and whole plasma to test a better source of small ncRNA biomarkers. We hypothesize that exosome is a better source of small ncRNA biomarkers compared to whole plasma. Methods: Small ncRNA sequencings were done using Illumina NextSeq500. Single-end 76 bp reads were explored using FASTQC. GeneGlobe Data Analysis Center, an online platform from Qiagen was used for the small ncRNA-seq data analysis. The mapped read counts for miRNA, piRNA and tRNA were used for differential expression analysis using DESeq2 Bioconductor in R. The small RNA species with logFC > |1| and adj.P-value <0.05 were called differentially expressed. Conclusion: Small ncRNAs extracted from exosomes were found to have the most consistent profiles among healthy individuals. Whole plasma RNA profiles contain high concentrations of blood-derived miRNAs. These miRNA levels can create batch effects among samples due to different levels of hemolysis. Our finding suggests the importance of using purified exosomes as a better source of small ncRNA biomarkers to avoid the batch effect.
2022-10-28 | GSE216556 | GEO
Project description: Not available
| S-EPMC6058782 | biostudies-literature
Project description:microRNAs are promising biomarkers in biological fluids in several diseases. Different plasma RNA isolation protocols and carriers are available, but their efficiencies have been scarcely compared. Plasma microRNAs were isolated using a phenol and column-based procedure and a column-based procedure, in the presence or absence of two RNA carriers (yeast RNA and MS2 RNA). We evaluated the presence of PCR inhibitors and the relative abundance of certain microRNAs by qRT-PCR. Furthermore, we analyzed the association between different isolation protocols, the relative abundance of the miRNAs in the sample, the GC content and the free energy of microRNAs. In all microRNAs analyzed, the addition of yeast RNA as a carrier in the different isolation protocols used gave lower raw Cq values, indicating higher microRNA recovery. Moreover, this increase in microRNAs recovery was dependent on their own relative abundance in the sample, their GC content and the free-energy of their own most stable secondary structure. Furthermore, the normalization of microRNA levels by an endogenous microRNA is more reliable than the normalization by plasma volume, as it reduced the difference in microRNA fold abundance between the different isolation protocols evaluated. Our thorough study indicates that a standardization of pre- and analytical conditions is necessary to obtain reproducible inter-laboratory results in plasma microRNA studies.
| S-EPMC5659774 | biostudies-literature
Project description:BackgroundSingle liquid biopsy analytes (LBAs) have been utilized for therapy selection in metastatic breast cancer (MBC). We performed integrative statistical analyses to examine the clinical relevance of using multiple LBAs: matched circulating tumor cell (CTC) mRNA, CTC genomic DNA (gDNA), extracellular vesicle (EV) mRNA, and cell-free DNA (cfDNA).MethodsBlood was drawn from 26 hormone receptor-positive, HER2-negative MBC patients. CTC mRNA and EV mRNA were analyzed using a multi-marker qPCR. Plasma from CTC-depleted blood was utilized for cfDNA isolation. gDNA from CTCs was isolated from mRNA-depleted CTC lysates. CTC gDNA and cfDNA were analyzed by targeted sequencing. Hierarchical clustering was performed within each analyte, and its results were combined into a score termed Evaluation of multiple Liquid biopsy analytes In Metastatic breast cancer patients All from one blood sample (ELIMA.score), which calculates the contribution of each analyte to the overall survival prediction. Singular value decomposition (SVD), mutual information calculation, k-means clustering, and graph-theoretic analysis were conducted to elucidate the dependence between individual analytes.ResultsA combination of two/three/four LBAs increased the prevalence of patients with actionable signals. Aggregating the results of hierarchical clustering of individual LBAs into the ELIMA.score resulted in a highly significant correlation with overall survival, thereby bolstering evidence for the additive value of using multiple LBAs. Computation of mutual information indicated that none of the LBAs is independent of the others, but the ability of a single LBA to describe the others is rather limited-only CTC gDNA could partially describe the other three LBAs. SVD revealed that the strongest singular vectors originate from all four LBAs, but a majority originated from CTC gDNA. After k-means clustering of patients based on parameters of all four LBAs, the graph-theoretic analysis revealed CTC ERBB2 variants only in patients belonging to one particular cluster.ConclusionsThe additional benefits of using all four LBAs were objectively demonstrated in this pilot study, which also indicated a relative dominance of CTC gDNA over the other LBAs. Consequently, a multi-parametric liquid biopsy approach deconvolutes the genomic and transcriptomic complexity and should be considered in clinical practice.
| S-EPMC8130163 | biostudies-literature
Project description:BackgroundCirculating tumor DNA has emerging clinical applications in several cancers; however, previous studies have shown low sensitivity in glioma. We investigated if 3 key glioma gene mutations IDH1, TERTp, and EGFRvIII could be reliably detected in plasma by droplet digital polymerase chain reaction (ddPCR) thereby demonstrating the potential of this technique for glioma liquid biopsy.MethodsWe analyzed 110 glioma patients from our biobank with a total of 359 plasma samples (median 4 samples per patient). DNA was isolated from plasma and analyzed for IDH1, TERTp, and EGFRvIII mutations using ddPCR.ResultsTotal cfDNA was significantly associated with tumor grade, tumor volume, and both overall and progression-free survival for all gliomas as well as the grade 4 glioblastoma subgroup, but was not reliably associated with changes in tumor volume/progression during the patients' postoperative time course. IDH1 mutation was detected with 84% overall sensitivity across all plasma samples and 77% in the preoperative samples alone; however, IDH1 mutation plasma levels were not associated with tumor progression or survival. IDH1m plasma levels were not associated with pre- or postsurgery progression or survival. The TERTp C228T mutation was detected in the plasma ctDNA in 88% but the C250T variant in only 49% of samples. The EGFRvIII mutation was detected in plasma in 5 out of 7 patients (71%) with tissue EGFRvIII mutations in tumor tissue.ConclusionsPlasma ctDNA mutations detected with ddPCR provide excellent diagnostic sensitivity for IDH1, TERTp-C228T, and EGFRvIII mutations in glioma patients. Total cfDNA may also assist with prognostic information. Further studies are needed to validate these findings and the clinical role of ctDNA in glioma.
| S-EPMC10989869 | biostudies-literature
Project description:IntroductionMolecular profiling of tumor tissue is the gold standard for treatment decision-making in advanced non-small cell lung cancer (NSCLC). Results may be delayed or unavailable due to insufficient tissue, prolonged wait times for biopsy, pathology assessment and testing. We piloted the use of plasma testing in the initial diagnostic workup for patients with suspected advanced lung cancer.MethodsPatients with ⩽15 pack-year smoking history and suspected advanced lung cancer referred to the lung cancer rapid diagnostic program underwent plasma circulating-tumor DNA testing using a DNA-based mutation panel. Tissue testing was performed per standard of care, including comprehensive next-generation sequencing (NGS). The primary endpoint was time from diagnostic program referral to cancer treatment in stage IV NSCLC patients (Cohort A) compared to a contemporary cohort not enrolled in the study (Cohort B) and an historical pre-COVID cohort referred to the program between 2018 and 2019 (Cohort C).ResultsFrom January to June 2021, 20 patients were enrolled in Cohort A; median age was 70.5 years (range 33-87), 70% were female, 55% Caucasian, 85% never smokers, and 75% were diagnosed with NSCLC. Seven had actionable alterations detected in plasma or tissue (4/7 concordant). Fusions, not tested in plasma, were identified by immunohistochemistry for three patients. Mean result turnaround time was 17.8 days for plasma NGS and 23.6 days for tissue (p = 0.10). Mean time from referral to treatment initiation was significantly shorter in cohort A at 32.6 days (SD 13.1) versus 62.2 days (SD 31.2) in cohort B and 61.5 days (SD 29.1) in cohort C, both p < 0.0001.ConclusionLiquid biopsy in the initial diagnostic workup of patients with suspected advanced NSCLC can lead to faster molecular results and shorten time to treatment even with smaller DNA panels. An expansion study using comprehensive NGS plasma testing with faster turnaround time is ongoing (NCT04862924).
| S-EPMC9500258 | biostudies-literature
Project description:INTRODUCTION:When analyzing the human plasma metabolome with Nuclear Magnetic Resonance (NMR) spectroscopy, the Carr-Purcell-Meiboom-Gill (CPMG) experiment is commonly employed for large studies. However, this process can lead to compromised statistical analyses due to residual macromolecule signals. In addition, the utilization of Trimethylsilylpropanoic acid (TSP) as an internal standard often leads to quantification issues, and binning, as a spectral summarization step, can result in features not clearly assignable to metabolites. OBJECTIVES:Our aim was to establish a new complete protocol for large plasma cohorts collected with the purpose of describing the comparative metabolic profile of groups of samples. METHODS:We compared the conventional CPMG approach to a novel procedure that involves diffusion NMR, using the Longitudinal Eddy-Current Delay (LED) experiment, maleic acid (MA) as the quantification reference and peak picking for spectral reduction. This comparison was carried out using the ultrafiltration method as a gold standard in a simple sample classification experiment, with Partial Least Squares-Discriminant Analysis (PLS-DA) and the resulting metabolic signatures for multivariate data analysis. In addition, the quantification capabilities of the method were evaluated. RESULTS:We found that the LED method applied was able to detect more metabolites than CPMG and suppress macromolecule signals more efficiently. The complete protocol was able to yield PLS-DA models with enhanced classification accuracy as well as a more reliable set of important features than the conventional CPMG approach. Assessment of the quantitative capabilities of the method resulted in good linearity, recovery and agreement with an established amino acid assay for the majority of the metabolites tested. Regarding repeatability,?~?85% of all peaks had an adequately low coefficient of variation (<?30%) in replicate samples. CONCLUSION:Overall, our comparison yielded a high-throughput untargeted plasma NMR protocol for optimized data acquisition and processing that is expected to be a valuable contribution in the field of metabolic biomarker discovery.
| S-EPMC7196944 | biostudies-literature
Project description:PURPOSE:Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with a 5-year survival of < 10% because of diffuse symptoms leading to late-stage diagnosis. That survival could increase significantly if localized tumors could be detected early. Therefore, we used multiparametric analysis of blood samples to obtain a novel biomarker signature of early-stage PDAC. The signature was derived from a large patient cohort, including patients with well-defined early-stage (I and II) PDAC. This biomarker signature was validated subsequently in an independent patient cohort. PATIENTS AND METHODS:The biomarker signature was derived from a case-control study, using a Scandinavian cohort, consisting of 16 patients with stage I, 132 patients with stage II, 65 patients with stage III, and 230 patients with stage IV PDAC, and 888 controls. This signature was validated subsequently in an independent case-control cohort in the United States with 15 patients with stage I, 75 patients with stage II, 15 patients with stage III, and 38 patients with stage IV PDAC, and 219 controls. An antibody microarray platform was used to identify the serum biomarker signature associated with early-stage PDAC. RESULTS:Using the Scandinavian case-control study, a biomarker signature was created, discriminating samples derived from patients with stage I and II from those from controls with a receiver operating characteristic area under the curve value of 0.96. This signature, consisting of 29 biomarkers, was then validated in an independent case-control study in the United States. The biomarker signature could discriminate patients with stage I and II PDAC from controls in this independent patient cohort with a receiver operating characteristic area under the curve value of 0.96. CONCLUSION:This serum biomarker signature might represent a tenable approach to detecting early-stage, localized PDAC if these findings are supported by a prospective validation study.
| S-EPMC6161836 | biostudies-literature