Droplet Digital PCR for MYD88L265P Mutation Detection in Waldenström Macroglobulinemia: Minimal Residual Disease Monitoring and Characterization on Circulating Free DNA
1Department of Molecular Biotechnologies and health sciences, Hematology Division, University of Torino, Italy; 2Servicio de Hematología, Hospital Universitario de Salamanca, Salamanca, Espania; 3Clinical and Experimental Medicine, University of Pisa, Pisa, Italy; 4Division of Hematology, ASO S.Giovanni Battista, Torino, Italy; 5Division of Hematology, Az Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
Background: MYD88L265P mutation might represent an ideal marker for minimal residual disease (MRD) monitoring in Waldenström Macroglobulinemia (WM). However, the conventional allele-specific quantitative PCR (ASqPCR) is not sensitive enough for MRD monitoring on peripheral blood (PB), harboring low concentrations of tumor cells. Besides, cell-free DNA (cfDNA) is increasingly used for mutational studies. We set up a new, highly sensitive, droplet digital PCR (ddPCR) assay for MYD88L265P detection and described: 1) its feasibility for mutation screening and MRD monitoring in bone marrow (BM) and PB; 2) its application for mutational studies on cfDNA.
Methods: BM, PB and plasma from local series of WM, IgG-lymphoplasmacytic lymphoma (LPL) and IgM-MGUS patients (pts) were collected at baseline and during follow-up (FU). 40 healthy subjects were used as negative controls. Genomic DNA (gDNA) and plasmatic cfDNA were extracted by Maxwell RSC system (Promega). MYD88L265P was assessed on gDNA (100ng) and cfDNA (from 1ml of plasma) by a custom ddPCR assay on a QX100 System (Bio-Rad). For comparison ASqPCR was assessed on gDNA (100ng), as described [Xu L, 2013]. MYD88L265P cut-off was settled based on the healthy samples background level. IGH-based MRD analysis was performed as described [Drandi D, 2015].
Results: Sensitivity of ddPCR versus ASqPCR was assessed on a ten-fold serial dilution standard curve. Whereas ASqPCR confirmed the sensitivity of 1.00E−03, ddPCR reached a sensitivity up to 5.00E−05. Overall, 291 samples from 148 pts, 194 baseline (128 BM, 66 PB) and 97 follow-up (43 BM and 54 PB), were analyzed. 123/128 (96.1%) diagnostic BM and 47/66 (71.2%) PB samples scored positive for MYD88L265P (BM median 3.6%, range: 0.02-72.6%: PB median 0.3%, range: 0.01-27.8%). 11/46 (24%) pts with both BM/PB collected at diagnosis showed a positive/negative match. Concordances between ddPCR and qPCR methods were investigate on 100 samples (60 BM, 40 PB) and overall a good concordance was observed (p=0.0005). Of note the majority of discordances were observed in the follow-up samples. Moreover, to investigate whether MYD88L265P ddPCR could be used for MRD monitoring we compared it to the gold standard IGH-based MRD assay in baseline and FU samples (23 BM, 15 PB) from 10 pts. The comparison showed highly superimposable results between methods. Finally, ddPCR performed on cfDNA from 60 plasma samples showed 1 log higher median levels of MYD88L265P mutation by mutation in plasma (1.4%, range 0-72.2%) compared to PB (0.1%, range: 0.01-27.8%).
Conclusion: ddPCR is a feasible and highly sensitive assay for mutational screening and MRD monitoring in WM, particularly in samples harboring low concentrations of circulating tumor cells. Moreover, plasmatic cfDNA represents a promising tissue source and might be an attractive, less invasive alternative to PB or BM for MYD88L265P detection.
Partition Volume in dPCR -- Monodispersity Not Really Important
Vanderbilt University, United States of America
The role of partition volume variability, or polydispersity, in digital polymerase chain reaction methods is examined through formal considerations and Monte Carlo simulations. Contrary to intuition, polydispersity causes little precision loss for low average copy number per partition μ and can actually improve precision when μ exceeds ~4. It does this by negatively biasing the estimates of μ, thus increasing the number of negative (null) partitions N0. In keeping with binomial statistics, this increases the relative precision of N0 and hence of the biased estimate m of μ. Below μ = 1, the precision loss and the bias are both small enough to be negligible for many applications. For higher μ the bias becomes more important than the imprecision, making accuracy dependent on knowledge of the partition volume distribution function. This information can be gained with optical microscopy or through calibration with reference materials.
Automated cell picking and single cell digital PCR focusing on mitochondrial transfer
1Institute of Biotechnology AS CR, Prague; 2TATAA Biocenter, Czech Republic
Mitochondria are unique organelles comprising their own genetic information in the form of mitochondrial DNA (mtDNA). Cell type mtDNA heteroplasmy was reported before as a common event observed not only in patients with mitochondrial diseases but also in healthy individuals. In parallel reports show that mitochondria move between mammalian cells. We performed single cell expression profiling focusing on horizontal mitochondrial transfer. Using automated cell picking and single cell digital PCR, we showed that generation of tumors in syngeneic mice by cells devoid of mitochondrial (mt) DNA (ρ0 cells) is linked to acquisition of the host mtDNA, leading to the normalization of mitochondrial respiration.
Digital PCR Inhibition Mechanisms Using Standardized Inhibitors Representing Soil And Blood Matrices
1Applied Microbiology, Department of Chemistry, Lund University, PO Box 124, 221 00 Lund, Sweden; 2Swedish National Forensic Centre, 581 94 Linköping, Sweden; 3Materials Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8314, United States; 4Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, 41296 Gothenburg, Sweden
Digital PCR (dPCR) enables absolute quantification of nucleic acids by partitioning the sample into hundreds or thousands of minute reactions (1). By assuming a Poisson distribution for the number of DNA fragments present in each chamber, the DNA concentration is determined without the need for a standard curve. However, when analyzing nucleic acids from complex matrices such as soil and blood, the dPCR quantification can be biased due to the presence of inhibitory compounds (2,3). In this study, we present how certain inhibitors disturb dPCR quantification and suggest solutions to these problems. Furthermore, we use real-time PCR, dPCR and isothermal titration calorimetry as tools to elucidate the mechanisms underlying the PCR inhibition. The impact of impurities on dPCR quantification was studied using humic acid as a model inhibitor. We show that the inhibitor-tolerance differs greatly for three different DNA polymerases, illustrating the importance of choosing a DNA polymerase-buffer system that is compatible with the samples to be analysed. Various inhibitory-substances from blood were found to disturb the system in different ways. For example, hemoglobin was found to cause quenching of fluorescence and a dramatic decrease of the number of positive reactions, leading to an underestimation of DNA quantity. IgG caused an increased number of late-starters. The system was more susceptible to inhibition by IgG when single-stranded DNA was used as template, compared with double-stranded DNA. By understanding more about the mechanisms of PCR inhibitors it will be possible to design more optimal PCR chemistries, improving dPCR detection and quantification.
1. Baker, M. (2012) Digital PCR hits its stride. Nat. Methods, 9, 541-544.
2. Coudray-Meunier, C., Fraisse, A., Martin-Latil, S., Guillier, L., Delannoy, S., Fach, P. and Perelle, S. (2015) A comparative study of digital RT-PCR and RT-qPCR for quantification of Hepatitis A virus and Norovirus in lettuce and water samples. Int. J. Food Microbiol., 201, 17-26.
3. Hoshino, T. and Inagaki, F. (2012) Molecular quantification of environmental DNA using microfluidics and digital PCR. Syst. Appl. Microbiol., 35, 390-395.
dpcReport: web server and software suite for unified analysis of digital PCRs and digital assays
1Department of Genomics, University of Wrocław, Poland; 2Molecular and Cell Biology Team, LGC, Teddington, United Kingdom; 3Research Dept. Cell and Gene Therapy, Department of SCT, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; 4Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology, Wrocław, Poland; 5University Medical Center Hamburg-Eppendorf, Hamburg, Germany; 6Institute of Biotechnology, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany
Digital PCR (dPCR) is a variant of PCR, where the PCR amplification is conducted in multiple small volume reactions (termed partitions) instead of a bulk. The dichotomous status of each partition (positive or negative amplification) is used for absolute quantification of the template molecules by Poisson transformation of the proportion of positive partitions. The vast expansion of dPCR technology and its applications has been followed by the development of statistical data analysis methods. Yet, the software landscape is scattered, consisting of scripts in various programming languages, web servers with narrow scopes or closed source vendor software packages, that are usually tightly tied to their platform. This leads to unfavourable environments, as results from different platforms, or even from different laboratories using the same platform, cannot be easily compared with one another.
To address these problems, we developed the dpcReport web server that provides an open-source tool for the analysis of dPCR data. dpcReport provides a streamlined analysis framework to the dPCR community that is compatible with the data output (e.g., CSV, XLSX) from different dPCR platforms (e.g., Bio-Rad QX100/200, Biomark). This goes beyond the basic dPCR data analysis with vendor-supplied softwares, which is often limited to the computation of the mean template copy number per partition and its uncertainty. dpcReport gives users more control over their data analysis and they benefit from standardization and reproducible analysis.
Our web server analyses data regardless of the platform vendor or type (droplet or chamber dPCR). It is not limited to the commercially available platforms and can also be used with experimental systems by importing data through the universal REDF format, which follows the IETF® RFC 4180 standard. dpcReport provides users with advanced tools for data quality control and it incorporates statistical tests for comparing multiple reactions in an experiment, currently absent in many dPCR-related software tools. dpcReport provides users with advanced tools for data quality control. The conducted analyses are fully integrated within extensive and customizable interactive HTML reports including figures, tables and calculations. To improve reproducibility and transparency, a report may include snippets in the programming language R enabling an exact reproduction of the analysis performed by dpcReport through functions from the dpcR package. Our software follows the standardized dPCR nomenclature of the dMIQE guidelines. Since the vast functionality offered by dpcReport may be overwhelming at first, our web server is extensively documented.
The server is freely accessible at: http://www.smorfland.uni.wroc.pl/shiny/dpcReport/.
A Statistical Contribution to the Uncertainty of Concentration Measurements Using Digital PCR
Physikalisch-Technische Bundesanstalt, Germany
Measurement of the concentration of biological entities using quantitative PCR (qPCR) usually results in a large spread of results obtained in interlaboratory comparisons. As such, deviations in the range of ±0.6 on a logarithmic scale (base 10) are currently considered acceptable in the external quality assurance of quantification of viruses like HBV, HCV, and HIV-1 in Germany. Notably, a considerable contribution to this variance may originate from pre-analytical steps and variable efficacy of the assays utilized. Technical limitations of the instrument remain as a source of uncertainty of measurement, even if a perfect extraction and amplification is assumed. Digital PCR (dPCR) does neither require reference genes nor calibration material to quantify the concentration in biological samples and thus might substantially improve the measurement uncertainty in concentration measurements compared to qPCR.
An apparent physical limitation for concentration measurements using dPCR is the uncertainty of the reaction volume. Recent studies quote an (unexpanded) uncertainty of around 2% using microscopic measurements of the average size of the respective reaction volume. In this study, we discuss the effect of the limited number of repeat reactions in dPCR on the uncertainty of concentration.
It is generally accepted that a Poisson correction must be applied to the concentration determined by dPCR, i.e. for an average of λ reactive DNA molecules in each reaction chamber, the probability of a positive reaction is p=(1-exp(-λ)).Thus, for N reactions one expects A=pN positive reactions and B=(1-p)N negative reactions.
The statistical uncertainty for positive (and negative) reactions does not follow a normal distribution. We show, that the uncertainty of finding positive reactions has to be calculated based on the standard deviation of the binomial distribution, which yields u=(AB/N)1/2. This result is used to calculate the minimum number of reactions required for a given DNA concentration λ and targeted statistical uncertainty u of the concentration. Additionally, the implications of this result on setting up dPCR experiments is discussed. We calculated the sweet spot for the fewest number of reactions around λ=1.5, which falls to λ=1 for large statistical uncertainties (>10%). Even in this case, at least 4000 reactions are required to reach a statistical uncertainty of 2%. To maintain this uncertainty for a dynamic range of 3 orders of magnitude requires using more than 300000 reactions.
The research receives funding from the EMPIR project HLT-07 AntiMicroResist. The EMPIR programme is co-financed by the Participating States and the European Union’s Horizon 2020 research and innovation programme.