Benchmarking digital PCR partition classification methods with empirical and simulated duplex data.

Digital PCR (dPCR) is a highly accurate technique for the quantification of target nucleic acid(s). It has shown great potential in clinical applications, like tumor liquid biopsy and validation of biomarkers. Accurate classification of partitions based on end-point fluorescence intensities is crucial to avoid biased estimators of the concentration of the target molecules. We have evaluated many clustering methods, from general-purpose methods to specific methods for dPCR and flowcytometry, on both simulated and real-life data. Clustering method performance was evaluated by simulating various scenarios. Based on our extensive comparison of clustering methods, we describe the limits of these methods, and formulate guidelines for choosing an appropriate method. In addition, we have developed a novel method for simulating realistic dPCR data. The method is based on a mixture distribution of a Poisson point process and a skew-$t$ distribution, which enables the generation of irregularities of cluster shapes and randomness of partitions between clusters ('rain') as commonly observed in dPCR data. Users can fine-tune the model parameters and generate labeled datasets, using their own data as a template. Besides, the database of experimental dPCR data augmented with the labeled simulated data can serve as training and testing data for new clustering methods. The simulation method is available as an R Shiny app.

Digital PCR Partition Classification.

BACKGROUND: Partition classification is a critical step in the digital PCR data analysis pipeline. A range of partition classification methods have been developed, many motivated by specific experimental setups. An overview of these partition classification methods is lacking and their comparative properties are often unclear, likely impacting the proper application of these methods. CONTENT: This review provides a summary of all available digital PCR partition classification approaches and the challenges they aim to overcome, serving as a guide for the digital PCR practitioner wishing to apply them. We additionally discuss strengths and weaknesses of these methods, which can further guide practitioners in vigilant application of these existing methods. This review provides method developers with ideas for improving methods or designing new ones. The latter is further stimulated by our identification and discussion of application gaps in the literature, for which there are currently no or few methods available. SUMMARY: This review provides an overview of digital PCR partition classification methods, their properties, and potential applications. Ideas for further advances are presented and may bolster method development.

Dynamics of the Cellular and Humoral Immune Response After BNT162b2 Messenger Ribonucleic Acid Coronavirus Disease 2019 (COVID-19) Vaccination in COVID-19-Naive Nursing Home Residents.

Short-term humoral and cellular immune responses are diminished after BNT162b2 messenger ribonucleic acid coronavirus disease 2019 (COVID-19) vaccination in COVID-19-naive nursing home residents, a population particularly vulnerable to the disease. We found both responses to decline after 4 weeks and remain lower than those of healthcare workers after 24 weeks, with an estimated half-life of the antibody response of 47 days. At 4 weeks, older age was significantly associated with a decreased humoral response, and diabetes mellitus and active malignancy were associated with a decreased cellular response. Our results imply that COVID-19-naive nursing home residents are a target group for booster vaccination trials.

Reducing Bias in Digital PCR Quantification Experiments: The Importance of Appropriately Modeling Volume Variability.

Multiple simulation studies have shown that volume variability of partition sizes in digital PCR (dPCR) causes bias in the resulting concentration estimates and their associated standard errors. These biases are especially apparent when the volume variability is large, and the targeted nucleic acid concentration is high. Currently, only a single method for the elimination or reduction of these biases is available, and it assumes a fixed class of models for the volume variability. We show that the form in which volumetric variability occurs in empirical data is variable and cannot be modeled by a single distribution. We propose a new volume-modeling method, NPVolMod, which takes volume variability of an arbitrary form into account and is applicable to both absolute and relative quantification. The method is nonparametric in the sense that no distributional assumption is needed. Moreover, the volumes of each of the individual partitions are not needed. We empirically demonstrate by simulation that NPVolMod nearly eliminates the biases caused by volumetric variability and that it often outperforms the existing method. The possibility of the proper modeling of volume variability may have implications for platform design and may increase the performance of existing dPCR platforms in terms of, for example, their trueness and linear dynamic range.

On determining the power of digital PCR experiments.

The experimental design that will be carried out to evaluate a nucleic acid quantification hypothesis determines the cost and feasibility of digital polymerase chain reaction (digital PCR) studies. Experiment design involves the calculation of the number of technical measurement replicates and the determination of the characteristics of those replicates, and this in accordance with the capabilities of the available digital PCR platform. Available digital PCR power analyses suffer from one or more of the following limitations: narrow scope, unrealistic assumptions, no sufficient detail for replication, lack of source code and user-friendly software. Here, we discuss the nature of six parameters that affect the statistical power, i.e., desired effect size, total number of partitions, fraction of positive partitions, number of replicate measurements, between-replicate variance, and significance level. We also show to what extent these parameters affect power, and argue that careful design of experiments is needed to achieve the desired power. A web tool, dPowerCalcR, that allows interactive calculation of statistical power and optimization of the experimental design is available.

Exhaled breath to screen for malignant pleural mesothelioma: a validation study.

Malignant pleural mesothelioma (MPM) is predominantly caused by asbestos exposure and has a poor prognosis. Breath contains volatile organic compounds (VOCs) and can be explored as an early detection tool. Previously, we used multicapillary column/ion mobility spectrometry (MCC/IMS) to discriminate between patients with MPM and asymptomatic high-risk persons with a high rate of accuracy. Here, we aim to validate these findings in different control groups.Breath and background samples were obtained from 52 patients with MPM, 52 healthy controls without asbestos exposure (HC), 59 asymptomatic former asbestos workers (AEx), 41 patients with benign asbestos-related diseases (ARD), 70 patients with benign non-asbestos-related lung diseases (BLD) and 56 patients with lung cancer (LC).After background correction, logistic lasso regression and receiver operating characteristic (ROC) analysis, the MPM group was discriminated from the HC, AEx, ARD, BLD and LC groups with 65%, 88%, 82%, 80% and 72% accuracy, respectively. Combining AEx and ARD patients resulted in 94% sensitivity and 96% negative predictive value (NPV). The most important VOCs selected were P1, P3, P7, P9, P21 and P26.We discriminated MPM patients from at-risk subjects with great accuracy. The high sensitivity and NPV allow breath analysis to be used as a screening tool for ruling out MPM.

Quality control of digital PCR assays and platforms.

Digital polymerase chain reaction (digital PCR, dPCR) is a direct nucleic acid quantification method, thus requiring no standard curves unlike quantitative real-time PCR (qPCR). Nevertheless, evaluation of the linear dynamic range, accuracy, and precision of an assay or platform is recommended, as there are several potential causes of important non-linearity, bias, and imprecision. Ignoring these quality issues may lead to erroneous quantification. This necessitates an approach akin to the construction of standard curves. We study the pitfalls associated with the evaluation of such an experiment, and provide guidelines for the assessment of linearity, accuracy, and precision in dPCR experiments. We present simulation results and a case study supporting the importance of a thorough evaluation. Further, typically presented plots and statistics may not reveal problems with linearity, accuracy, or precision. We find that a robust weighted least-squares approach is highly advisable, yet may also suffer from an inflated false-positive rate. The proposed assessments are also applicable to other analyses, such as the comparison of results obtained from qPCR and dPCR. A web tool for quality evaluation, dPCalibRate, is available.

ddpcRquant: threshold determination for single channel droplet digital PCR experiments.

Digital PCR is rapidly gaining interest in the field of molecular biology for absolute quantification of nucleic acids. However, the first generation of platforms still needs careful validation and requires a specific methodology for data analysis to distinguish negative from positive signals by defining a threshold value. The currently described methods to assess droplet digital PCR (ddPCR) are based on an underlying assumption that the fluorescent signal of droplets is normally distributed. We show that this normality assumption does not likely hold true for most ddPCR runs, resulting in an erroneous threshold. We suggest a methodology that does not make any assumptions about the distribution of the fluorescence readouts. A threshold is estimated by modelling the extreme values in the negative droplet population using extreme value theory. Furthermore, the method takes shifts in baseline fluorescence between samples into account. An R implementation of our method is available, allowing automated threshold determination for absolute ddPCR quantification using a single fluorescent reporter.

Bias reduction improves accuracy and informativity of high-throughput sequencing chimerism assays.

BACKGROUND AND AIMS: Chimerism monitoring by means of high-throughput sequencing of biallelic polymorphisms has shown promising advantages for patient follow-up after hematopoietic stem cell transplantation. Yet, the presence of method bias precludes achievement of an assay's theoretically attainable informativity rate, as method bias necessitates the exclusion of some markers. This method bias arises because of preferential observation of one allele over the other, and for some allelic constellations because of stochasticity. RESULTS: This paper suggests how preferential allelic observation may lead to method bias, and when and why such bias necessitates the exclusion of markers. It is shown that also markers that remain informative suffer a reduction in trueness and precision due to method bias. A bias reduction approach in the data analysis phase is introduced and shown to improve trueness and precision under all circumstances, meriting its universal adoption. This bias reduction furthermore allows to achieve an assay's theoretically achievable informativity rate, though at the cost of reduced sensitivity. Several strategies to consider in the assay design phase that may lower biases are proposed. CONCLUSION: Improved design and data analysis of chimerism assays increase the accuracy, applicability, and cost-effectiveness of high-throughput sequencing chimerism assays.

Comparative Assessment of Survival and Clinical Outcome Between Two Commercial Vitrification Kits with Different Warming Protocols After Blastocyst Culture: Potential Perspectives Toward Simplified Warming Procedures.

This study investigates whether there is an effect on laboratory results and clinical outcome using commercial kits with similar vitrification but different warming procedures for blastocysts vitrified on day 5 or day 6. A single-center retrospective cohort study was performed between 2011 and 2020. A change from a stage-specific kit (Kit 1) to a universal kit (Kit 2) was undertaken in 2017. A total of 1845 untested blastocysts were warmed for single vitrified-warmed blastocyst transfers (SVBT). Eight hundred and twenty-five blastocysts were vitrified with Kit 1 and 1020 with Kit 2. Blastocyst survival was not different (96.1% versus 97.3%). Seven hundred seventy-seven SVBT were performed from Kit 1 and 981 from Kit 2. Overall clinical pregnancy and live birth rates were not different (35.4% versus 34.1% and 30.9% versus 30.5% for Kit 1 and 2, respectively). Subgroup analysis for live birth rates in relation to the day of blastocyst vitrification showed no differences (36.1% and 36.1% for day 5 and 25.4% and 23.5% for day 6 blastocysts, respectively). For both kits, the mean gestational age was not different (38.8 ± 2.5 weeks versus 38.8 ± 2.0 weeks) with a singleton birth weight of 3413 ± 571 g and 3410 ± 528 g for Kit 1 and Kit 2, respectively. Differences in warming procedures do not affect laboratory performance or clinical outcome after blastocyst vitrification. The plasticity of a human blastocyst may allow for further investigation on simplification of blastocyst warming procedures.

Chimerism monitoring using biallelic single nucleotide or insertion/deletion polymorphisms: How many markers to screen?

BACKGROUND/AIMS: Chimerism monitoring by means of high-throughput sequencing or quantitative PCR of biallelic single nucleotide and insertion/deletion polymorphisms has shown potential for improved patient care when compared to the gold standard capillary electrophoresis assays. When designing chimerism assays the number of markers to screen needs consideration: it determines the informativity rate and accuracy of the assay, but screening too many markers increases the assay's cost and complexity. The minimal number of biallelic markers to screen is currently unstudied. MATERIALS/METHODS: A simulation framework accounting for marker minor allele frequencies, the number of markers screened, marker allelic constellations and donor-recipient relatedness was constructed. The framework was validated through analysis of 324 clinical samples. RESULTS: Empirical clinical data confirm the validity of the simulation framework. With guidelines suggesting to monitor at least three informative markers, we demonstrate that, for optimized assays, at least 40 biallelic markers need to be screened to achieve enough informative markers in over 99% of cases. We propose and discuss several assay optimization strategies. CONCLUSION: Currently used chimerism assays often screen too little or too many markers, leaving room for optimization. Through support of the simulation framework here introduced and validated, more informative, cost-effective chimerism assays can be designed.

Performance Assessment of the Devyser High-Throughput Sequencing-Based Assay for Chimerism Monitoring in Patients after Allogeneic Hematopoietic Stem Cell Transplantation.

Chimerism analysis is widely used to aid in the clinical management of patients after allogeneic hematopoietic stem cell transplantation. Many laboratories currently use assays based on PCR followed by capillary electrophoresis, with a limit of quantification of 1% to 5%. Assays with a lower limit of quantification could allow for earlier relapse detection, resulting in improved patient care. This study investigated the analytical, clinical, technical, and practical performance of the Devyser next-generation sequencing chimerism assay, a commercial high-throughput sequencing-based assay for chimerism analysis. Performance of this assay was compared with that of the Promega PowerPlex 16 HS assay, a commercial capillary electrophoresis-based assay. A limit of quantification of 0.1% was achievable with the Devyser assay. The repeatability, reproducibility, trueness, and linearity of the Devyser assay were acceptable. The Devyser assay showed potential for earlier relapse detection compared with the Promega assay. Conclusive analysis was not possible for 3% of donor-recipient pairs with the Devyser assay due to an insufficient number of informative markers; this factor was not an issue for the Promega assay. Further improvements in assay design or data analysis may allow the assay's applicability to be extended to all donor-recipient pairs studied. Technical performance criteria for chimerism analysis by high-throughput sequencing were suggested and evaluated. Both assays were found to be practical for use in a clinical diagnostics laboratory.

Power Doppler ultrasound and contrast-enhanced ultrasound demonstrate non-invasive tumour vascular response to anti-vascular therapy in canine cancer patients.

Combretastatin A4-phosphate (CA4P) is an anti-vascular agent which selectively shuts down blood supply in tumours, resulting in extensive tumour necrosis. The aim of this study was to assess in vivo, non-invasive ultrasound techniques for the early evaluation of tumour perfusion following CA4P treatment of spontaneous tumours. Eight dogs that bore spontaneous tumours were enrolled and were subsequently treated with a single dose of intravenous CA4P. Perfusion of tumours was evaluated by power Doppler ultrasound (PDUS) pre-treatment (0 h), during the injection (10 min, 20 min, 30 min) and after CA4P infusion (24 and 72 h). Vascularity index (VI) of the tumour tissue was quantitatively analysed and accuracy was verified by correlation analysis with the results of immunohistochemical evaluation of microvessel density (MVD). Central and peripheral perfusion was evaluated by contrast-enhanced ultrasound (CEUS) pre-treatment and at 72 h post-treatment. Post-treatment, PDUS demonstrated a significant decrease in VI within 10 min of CA4P infusion. CEUS parameters demonstrated a significant decrease in blood velocity and volume in the central aspect of the tumour. Histology revealed a 4.4-fold reduction (p < 0.001, 95% CI [2.2,9.4]) in MVD and a 4.1-fold increase (p = 0.003, 95% CI [1.4,11.8]) in necrotic tumour tissue. A strong correlation between PDUS results and immunohistochemical results was found (Pearson R2 = 0.957, p < 0.001). Furthermore, the findings of PDUS were supported by the objective results of the CEUS analyses. These data suggest a role for ultrasound in real-time, non-invasive monitoring of tumour vascular response as an early indicator of CA4P treatment efficacy.

Prognostic and Therapeutic Implications of Circulating Androgen Receptor Gene Copy Number in Prostate Cancer Patients Using Droplet Digital Polymerase Chain Reaction.

BACKGROUND: Resistance mechanisms in the androgen receptor (AR) signaling pathway remain key drivers in the progression to castration-resistant prostate cancer (CRPC) and relapse under antihormonal therapy. MATERIALS AND METHODS: We evaluated the circulating AR gene copy number (CN) gain using droplet digital polymerase chain reaction in 21 control and 91 prostate cancer serum samples and its prognostic and therapeutic implications in prostate cancer. RESULTS: In CRPC, AR CN gain was associated with faster progression to CRPC (P = .026), a greater number of previous treatments (P = .045), and previous chemotherapy (P = .016). Comparing patients with and without CN gain, the median progression-free survival (PFS) in the abiraterone subgroup was 1.7 months versus not reached (P = .004), and the median overall survival (OS) was 7 months versus 20.9 months (P = .020). In the enzalutamide subgroup, PFS was 1.7 versus 10.8 months (P = .006), and OS was 6.1 versus 16.5 months (P = .042). In the taxane subgroup, PFS was 3.2 versus 6.5 months (P = .093), and OS was 3.9 months versus not reached (P = .026). The presence of more AR copies correlated with shorter androgen deprivation (P = .002), abiraterone (P = .022), enzalutamide (P = .008), and taxane (P = .039) therapy. CONCLUSION: Circulating AR CN gain predicts for a poor prognosis in CRPC. It is a promising biomarker predetermining rapid CRPC progression and predicting worse abiraterone and enzalutamide outcomes. Furthermore, it is associated with multiple previous treatments and previous chemotherapy.

A unified censored normal regression model for qPCR differential gene expression analysis.

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is considered as the gold standard for accurate, sensitive, and fast measurement of gene expression. Prior to downstream statistical analysis, RT-qPCR fluorescence amplification curves are summarized into one single value, the quantification cycle (Cq). When RT-qPCR does not reach the limit of detection, the Cq is labeled as "undetermined". Current state of the art qPCR data analysis pipelines acknowledge the importance of normalization for removing non-biological sample to sample variation in the Cq values. However, their strategies for handling undetermined Cq values are very ad hoc. We show that popular methods for handling undetermined values can have a severe impact on the downstream differential expression analysis. They introduce a considerable bias and suffer from a lower precision. We propose a novel method that unites preprocessing and differential expression analysis in a single statistical model that provides a rigorous way for handling undetermined Cq values. We compare our method with existing approaches in a simulation study and on published microRNA and mRNA gene expression datasets. We show that our method outperforms traditional RT-qPCR differential expression analysis pipelines in the presence of undetermined values, both in terms of accuracy and precision.

Breath analysis by gas chromatography-mass spectrometry and electronic nose to screen for pleural mesothelioma: a cross-sectional case-control study.

RATIONALE: Malignant pleural mesothelioma (MPM) is mainly caused by previous exposure to asbestos fibers and has a poor prognosis. Due to a long latency period between exposure and diagnosis, MPM incidence is expected to peak between 2020-2025. Screening of asbestos-exposed individuals is believed to improve early detection and hence, MPM management. Recent developments focus on breath analysis for screening since breath contains volatile organic compounds (VOCs) which reflect the cell's metabolism. OBJECTIVES: The goal of this cross-sectional, case-control study is to identify VOCs in exhaled breath of MPM patients with gas chromatography-mass spectrometry (GC-MS) and to assess breath analysis to screen for MPM using an electronic nose (eNose). METHODS: Breath and background samples were taken from 64 subjects: 16 healthy controls (HC), 19 asymptomatic former asbestos-exposed (AEx) individuals, 15 patients with benign asbestos-related diseases (ARD) and 14 MPM patients. Samples were analyzed with both GC-MS and eNose. RESULTS: Using GC-MS, AEx individuals were discriminated from MPM patients with 97% accuracy, with diethyl ether, limonene, nonanal, methylcyclopentane and cyclohexane as important VOCs. This was validated by eNose analysis. MPM patients were discriminated from AEx+ARD participants by GC-MS and eNose with 94% and 74% accuracy, respectively. The sensitivity, specificity, positive and negative predictive values were 100%, 91%, 82%, 100% for GC-MS and 82%, 55%, 82%, 55% for eNose, respectively. CONCLUSION: This study shows accurate discrimination of patients with MPM from asymptomatic asbestos-exposed persons at risk by GC-MS and eNose analysis of exhaled VOCs and provides proof-of-principle of breath analysis for MPM screening.

The Future of Digital Polymerase Chain Reaction in Virology.

Driven by its potential benefits over currently available methods, and the recent development of commercial platforms, digital polymerase chain reaction (dPCR) has received increasing attention in virology research and diagnostics as a tool for the quantification of nucleic acids. The current technologies are more precise and accurate, but may not be much more sensitive, compared with quantitative PCR (qPCR) applications. The most promising applications with the current technology are the analysis of mutated sequences, such as emerging drug-resistant mutations. Guided by the recent literature, this review focuses on three aspects that demonstrate the potential of dPCR for virology researchers and clinicians: the applications of dPCR within both virology research and clinical virology, the benefits of the technique over the currently used real-time qPCR, and the importance and availability of specific data analysis approaches for dPCR. Comments are provided on current drawbacks and often overlooked pitfalls that need further attention to allow widespread implementation of dPCR as an accurate and precise tool within the field of virology.

Detection of malignant pleural mesothelioma in exhaled breath by multicapillary column/ion mobility spectrometry (MCC/IMS).

Malignant pleural mesothelioma (MPM) is predominantly caused by previous asbestos exposure. Diagnosis often happens in advanced stages restricting any therapeutic perspectives. Early stage detection via breath analysis was explored using multicapillary column/ion mobility spectrometry (MCC/IMS) to detect volatile organic compounds (VOCs) in the exhaled breath of MPM patients in comparison to former occupational asbestos-exposed and non-exposed controls. Breath and background samples of 23 MPM patients, 22 asymptomatic former asbestos (AEx) workers and 21 healthy non-asbestos exposed persons were taken for analysis. After background correction, we performed a logistic least absolute shrinkage and selection operator (lasso) regression to select the most important VOCs, followed by receiver operating characteristic (ROC) analysis. MPM patients were discriminated from both controls with 87% sensitivity, 70% specificity and respective positive and negative predictive values of 61% and 91%. The overall accuracy was 76% and the area under the ROC-curve was 0.81. AEx individuals could be discriminated from MPM patients with 87% sensitivity, 86% specificity and respective positive and negative predictive values of 87% and 86%. The overall accuracy was 87% with an area under the ROC-curve of 0.86. Breath analysis by MCC/IMS allows MPM patients to be discriminated from controls and holds promise for further investigation as a screening tool for former asbestos-exposed persons at risk of developing MPM.

Flexible analysis of digital PCR experiments using generalized linear mixed models.

The use of digital PCR for quantification of nucleic acids is rapidly growing. A major drawback remains the lack of flexible data analysis tools. Published analysis approaches are either tailored to specific problem settings or fail to take into account sources of variability. We propose the generalized linear mixed models framework as a flexible tool for analyzing a wide range of experiments. We also introduce a method for estimating reference gene stability to improve accuracy and precision of copy number and relative expression estimates. We demonstrate the usefulness of the methodology on a complex experimental setup.