48 research outputs found
Urine Metabolic Signature of Pancreatic Ductal Adenocarcinoma by <sup>1</sup>H Nuclear Magnetic Resonance: Identification, Mapping, and Evolution
Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis and is highly chemoresistant. Early detection is the only means to impact long-term survival, but screening methods are lacking. Given the complex and heterogeneous nature of pancreatic cancer, unbiased analytical methods such as metabolomics by nuclear magnetic resonance (NMR) spectroscopy show promise to identify disease-specific molecular fingerprints. NMR profiles constitute a fingerprint of the biofluid, reporting quantitatively on all detectable small biomolecules. NMR spectroscopy was applied to investigate the urine metabolome of PDAC patients (<i>n</i> = 33) and to detect altered metabolic profiles in comparison with healthy matched controls (<i>n</i> = 54). The spectral data were analyzed using multivariate statistical techniques. Statistically significant differences were found between urine metabolomic profiles of PDAC and control individuals (<i>p</i> < 10<sup>–5</sup>). Group discrimination was possible due to average concentration differences of several metabolite signals, pointing to a multimolecular signature of the disease. The robustness of the determined statistical model is confirmed by its predictive performance (sensitivity = 75.8%, specificity = 90.7%). Additionally, the method allowed for a neat separation between spectral profiles of individuals with intermediate and advanced pathologic staging, as well as for the discrimination of samples based on tumor localization. NMR spectroscopy analysis of urinary metabolic profiles proved successful in identifying a complex molecular signature of PDAC. Furthermore, results of a descriptive-level analysis show the possibility to follow disease evolution and to carry out tumor site mapping. Given the high reproducibility and the noninvasive nature of the analytical procedure, the described method bears potential to impact large-scale screening programs
DNA Qualification Workflow for Next Generation Sequencing of Histopathological Samples
<div><p>Histopathological samples are a treasure-trove of DNA for clinical research. However, the quality of DNA can vary depending on the source or extraction method applied. Thus a standardized and cost-effective workflow for the qualification of DNA preparations is essential to guarantee interlaboratory reproducible results. The qualification process consists of the quantification of double strand DNA (dsDNA) and the assessment of its suitability for downstream applications, such as high-throughput next-generation sequencing. We tested the two most frequently used instrumentations to define their role in this process: NanoDrop, based on UV spectroscopy, and Qubit 2.0, which uses fluorochromes specifically binding dsDNA. Quantitative PCR (qPCR) was used as the reference technique as it simultaneously assesses DNA concentration and suitability for PCR amplification. We used 17 genomic DNAs from 6 fresh-frozen (FF) tissues, 6 formalin-fixed paraffin-embedded (FFPE) tissues, 3 cell lines, and 2 commercial preparations. Intra- and inter-operator variability was negligible, and intra-methodology variability was minimal, while consistent inter-methodology divergences were observed. In fact, NanoDrop measured DNA concentrations higher than Qubit and its consistency with dsDNA quantification by qPCR was limited to high molecular weight DNA from FF samples and cell lines, where total DNA and dsDNA quantity virtually coincide. In partially degraded DNA from FFPE samples, only Qubit proved highly reproducible and consistent with qPCR measurements. Multiplex PCR amplifying 191 regions of 46 cancer-related genes was designated the downstream application, using 40 ng dsDNA from FFPE samples calculated by Qubit. All but one sample produced amplicon libraries suitable for next-generation sequencing. NanoDrop UV-spectrum verified contamination of the unsuccessful sample. In conclusion, as qPCR has high costs and is labor intensive, an alternative effective standard workflow for qualification of DNA preparations should include the sequential combination of NanoDrop and Qubit to assess the purity and quantity of dsDNA, respectively.</p></div
DNA qualification for next-generation sequencing applications.
<p>Effect of low-quality DNA on next-generation sequencing (NGS) workflow. Three FFPE samples were tested for construction of NGS amplicon libraries (Ion Torrent Ampliseq Cancer Panel). Qubit: 40 ng of DNA according to Qubit measurement were processed using the Ampliseq library construction kit (multiplex PCR amplification of 191 DNA regions from 46 cancer-related genes). NanoDrop: absorption spectra of samples showed different degrees of organic contamination (230 nm spike, A260/A230 ratio). Agilent: quality and quantity of the obtained libraries were evaluated by Agilent high sensitivity assay on-chip electrophoresis, where the library is represented by the large band between 150 and 200 bp. Fragments test: histogram showing length and abundance of produced sequences. Sample FFPE 5 did not produce a good library due to high organic contamination; this is revealed by the remarkable spike at 230 nm that concurs to the low 260/230 ratio, and explains the faint electrophoretic band and the almost flat fragments test histogram.</p
Significant discrepancies in DNA quantification by NanoDrop and Qubit.
<p>A total of 100 ng of DNA based on NanoDrop (N, black bars) or Qubit (Q, grey bars) measurements was analyzed by electrophoresis on 0.8% agarose gel. Sample ID is indicated at the bottom. Lane L contains 200 ng of DNA as the reference for normalization. Densitometric analysis (bar chart) was performed by ImageJ software <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062692#pone.0062692-Abramoff1" target="_blank">[20]</a>. It is clear from the electrophoretic bands and their densitometric charts that NanoDrop overestimates DNA concentration.</p
Cross-validation of DNA samples quantification by qPCR.
<p>Bland-Altman plots for inter-technology (NanoDrop or Qubit vs. qPCR) comparison of all samples (A), and according to the different sample sources, as indicated (B, C). A) Qubit measurements show high correlation (mean measured/expected ratio  = 0.92; SD  = 0.69; Wilcoxon signed rank test p = 0.07) with the measurements obtained by qPCR (x-axis), whereas NanoDrop measurements tend to overestimate samples concentration (mean measured/expected ratio  = 3.8; SD  = 6.4; Wilcoxon signed rank test p<0.0001). B) Fresh frozen sample quantification by NanoDrop overestimates (mean measured/expected ratio  = 1.48; SD  = 0.57; Wilcoxon signed rank test p<0.01) the DNA concentration detected by quantitative PCR, while Qubit underestimates (mean measured/expected ratio  = 0.78; SD  = 0.32; Wilcoxon signed rank test p<0.001) the value. C) In formalin-fixed paraffin-embedded samples a better concentration estimation is obtained by Qubit (mean measured/expected ratio  = 1.23; SD  = 1.15; Wilcoxon signed rank test p = 0.91) than by NanoDrop (mean measured/expected ratio  = 9.21; SD  = 9.95; Wilcoxon signed rank test p<0.004).</p
Intra- and inter-method accuracy and precision.
<p>Distribution of DNA sample concentration (dispersion chart) was estimated by both NanoDrop (black) and Qubit (gray) on repeated (n = 20) measurements of two commercial human genomic DNA preparations (Sample L 200 ng/µl; Sample G 5 ng/µl). For both samples, NanoDrop overestimated the DNA concentration (+8.8% for L and +24.0% for G, p<0.0003), while Qubit underestimated it (−5.0% for L and −7.3% for G, p<0.005).</p
Comparison of estimated costs and time requirements per sample for DNA quantification using NanoDrop, Qubit, or qPCR.
*<p>indicative cost for a standard qPCR instrument.</p
Human genomic DNA preparations used in the study.
a<p>FF, fresh frozen; FFPE, formalin-fixed paraffin embedded;</p>b<p>HMW, high molecular weight DNA; n.a., not applicable, as DNA from formalin fixed tissue is always partially degraded and no information is obtained by gel electrophoresis.</p
frozen_dilutioncurve_1_percent_tumor
These files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2A of the manuscript
SPT5_FFPE Sequences file
These files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in tables and figures of the manuscript