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.

A unified framework for unconstrained and constrained ordination of microbiome read count data.

Explorative visualization techniques provide a first summary of microbiome read count datasets through dimension reduction. A plethora of dimension reduction methods exists, but many of them focus primarily on sample ordination, failing to elucidate the role of the bacterial species. Moreover, implicit but often unrealistic assumptions underlying these methods fail to account for overdispersion and differences in sequencing depth, which are two typical characteristics of sequencing data. We combine log-linear models with a dispersion estimation algorithm and flexible response function modelling into a framework for unconstrained and constrained ordination. The method is able to cope with differences in dispersion between taxa and varying sequencing depths, to yield meaningful biological patterns. Moreover, it can correct for observed technical confounders, whereas other methods are adversely affected by these artefacts. Unlike distance-based ordination methods, the assumptions underlying our method are stated explicitly and can be verified using simple diagnostics. The combination of unconstrained and constrained ordination in the same framework is unique in the field and facilitates microbiome data exploration. We illustrate the advantages of our method on simulated and real datasets, while pointing out flaws in existing methods. The algorithms for fitting and plotting are available in the R-package RCM.

A comprehensive overview of genomic imprinting in breast and its deregulation in cancer.

Genomic imprinting plays an important role in growth and development. Loss of imprinting (LOI) has been found in cancer, yet systematic studies are impeded by data-analytical challenges. We developed a methodology to detect monoallelically expressed loci without requiring genotyping data, and applied it on The Cancer Genome Atlas (TCGA, discovery) and Genotype-Tissue expression project (GTEx, validation) breast tissue RNA-seq data. Here, we report the identification of 30 putatively imprinted genes in breast. In breast cancer (TCGA), HM13 is featured by LOI and expression upregulation, which is linked to DNA demethylation. Other imprinted genes typically demonstrate lower expression in cancer, often associated with copy number variation and aberrant DNA methylation. Downregulation in cancer frequently leads to higher relative expression of the (imperfectly) silenced allele, yet this is not considered canonical LOI given the lack of (absolute) re-expression. In summary, our novel methodology highlights the massive deregulation of imprinting in breast cancer.

Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA-sequencing data.

BACKGROUND: Long non-coding RNAs (lncRNAs) are typically expressed at low levels and are inherently highly variable. This is a fundamental challenge for differential expression (DE) analysis. In this study, the performance of 25 pipelines for testing DE in RNA-seq data is comprehensively evaluated, with a particular focus on lncRNAs and low-abundance mRNAs. Fifteen performance metrics are used to evaluate DE tools and normalization methods using simulations and analyses of six diverse RNA-seq datasets. RESULTS: Gene expression data are simulated using non-parametric procedures in such a way that realistic levels of expression and variability are preserved in the simulated data. Throughout the assessment, results for mRNA and lncRNA were tracked separately. All the pipelines exhibit inferior performance for lncRNAs compared to mRNAs across all simulated scenarios and benchmark RNA-seq datasets. The substandard performance of DE tools for lncRNAs applies also to low-abundance mRNAs. No single tool uniformly outperformed the others. Variability, number of samples, and fraction of DE genes markedly influenced DE tool performance. CONCLUSIONS: Overall, linear modeling with empirical Bayes moderation (limma) and a non-parametric approach (SAMSeq) showed good control of the false discovery rate and reasonable sensitivity. Of note, for achieving a sensitivity of at least 50%, more than 80 samples are required when studying expression levels in realistic settings such as in clinical cancer research. About half of the methods showed a substantial excess of false discoveries, making these methods unreliable for DE analysis and jeopardizing reproducible science. The detailed results of our study can be consulted through a user-friendly web application, giving guidance on selection of the optimal DE tool ( http://statapps.ugent.be/tools/AppDGE/ ).

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 new approach for modeling patient overall radiosensitivity and predicting multiple toxicity endpoints for breast cancer patients.

INTRODUCTION: Evaluation of patient characteristics inducing toxicity in breast radiotherapy, using simultaneous modeling of multiple endpoints. METHODS AND MATERIALS: In 269 early-stage breast cancer patients treated with whole-breast irradiation (WBI) after breast-conserving surgery, toxicity was scored, based on five dichotomized endpoints. Five logistic regression models were fitted, one for each endpoint and the effect sizes of all variables were estimated using maximum likelihood (MLE). The MLEs are improved with James-Stein estimates (JSEs). The method combines all the MLEs, obtained for the same variable but from different endpoints. Misclassification errors were computed using MLE- and JSE-based prediction models. For associations, p-values from the sum of squares of MLEs were compared with p-values from the Standardized Total Average Toxicity (STAT) Score. RESULTS: With JSEs, 19 highest ranked variables were predictive of the five different endpoints. Important variables increasing radiation-induced toxicity were chemotherapy, age, SATB2 rs2881208 SNP and nodal irradiation. Treatment position (prone position) was most protective and ranked eighth. Overall, the misclassification errors were 45% and 34% for the MLE- and JSE-based models, respectively. p-Values from the sum of squares of MLEs and p-values from STAT score led to very similar conclusions, except for the variables nodal irradiation and treatment position, for which STAT p-values suggested an association with radiosensitivity, whereas p-values from the sum of squares indicated no association. Breast volume was ranked as the most significant variable in both strategies. DISCUSSION: The James-Stein estimator was used for selecting variables that are predictive for multiple toxicity endpoints. With this estimator, 19 variables were predictive for all toxicities of which four were significantly associated with overall radiosensitivity. JSEs led to almost 25% reduction in the misclassification error rate compared to conventional MLEs. Finally, patient characteristics that are associated with radiosensitivity were identified without explicitly quantifying radiosensitivity.

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.

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.

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.

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.

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.

Pitfalls in Prediction Modeling for Normal Tissue Toxicity in Radiation Therapy: An Illustration With the Individual Radiation Sensitivity and Mammary Carcinoma Risk Factor Investigation Cohorts.

PURPOSE: To identify the main causes underlying the failure of prediction models for radiation therapy toxicity to replicate. METHODS AND MATERIALS: Data were used from two German cohorts, Individual Radiation Sensitivity (ISE) (n=418) and Mammary Carcinoma Risk Factor Investigation (MARIE) (n=409), of breast cancer patients with similar characteristics and radiation therapy treatments. The toxicity endpoint chosen was telangiectasia. The LASSO (least absolute shrinkage and selection operator) logistic regression method was used to build a predictive model for a dichotomized endpoint (Radiation Therapy Oncology Group/European Organization for the Research and Treatment of Cancer score 0, 1, or ≥2). Internal areas under the receiver operating characteristic curve (inAUCs) were calculated by a naïve approach whereby the training data (ISE) were also used for calculating the AUC. Cross-validation was also applied to calculate the AUC within the same cohort, a second type of inAUC. Internal AUCs from cross-validation were calculated within ISE and MARIE separately. Models trained on one dataset (ISE) were applied to a test dataset (MARIE) and AUCs calculated (exAUCs). RESULTS: Internal AUCs from the naïve approach were generally larger than inAUCs from cross-validation owing to overfitting the training data. Internal AUCs from cross-validation were also generally larger than the exAUCs, reflecting heterogeneity in the predictors between cohorts. The best models with largest inAUCs from cross-validation within both cohorts had a number of common predictors: hypertension, normalized total boost, and presence of estrogen receptors. Surprisingly, the effect (coefficient in the prediction model) of hypertension on telangiectasia incidence was positive in ISE and negative in MARIE. Other predictors were also not common between the 2 cohorts, illustrating that overcoming overfitting does not solve the problem of replication failure of prediction models completely. CONCLUSIONS: Overfitting and cohort heterogeneity are the 2 main causes of replication failure of prediction models across cohorts. Cross-validation and similar techniques (eg, bootstrapping) cope with overfitting, but the development of validated predictive models for radiation therapy toxicity requires strategies that deal with cohort heterogeneity.

Mining for viral fragments in methylation enriched sequencing data.

Most next generation sequencing experiments generate more data than is usable for the experimental set up. For example, methyl-CpG binding domain (MBD) affinity purification based sequencing is often used for DNA-methylation profiling, but up to 30% of the sequenced fragments cannot be mapped uniquely to the reference genome. Here we present and evaluate a methodology for the identification of viruses in these otherwise unused paired-end MBD-seq data. Viral detection is accomplished by mapping non-reference alignable reads to a comprehensive set of viral genomes. As viruses play an important role in epigenetics and cancer development, 92 (pre)malignant and benign samples, originating from two different collections of cervical samples and related cell lines, were used in this study. These samples include primary carcinomas (n = 22), low- and high-grade cervical intraepithelial neoplasia (CIN1 and CIN2/3 - n = 2/n = 30) and normal tissue (n = 20), as well as control samples (n = 17). Viruses that were detected include phages, adenoviruses, herpesviridae and HPV. HPV, which causes virtually all cervical cancers, was identified in 95% of the carcinomas, 100% of the CIN2/3 samples, both CIN1 samples and in 55% of the normal samples. Comparing the amount of mapped fragments on HPV for each HPV-infected sample yielded a significant difference between normal samples and carcinomas or CIN2/3 samples (adjusted p-values resp. <10(-5), <10(-5)), reflecting different viral loads and/or methylation degrees in non-normal samples. Fragments originating from different HPV types could be distinguished and were independently validated by PCR-based assays in 71% of the detections. In conclusion, although limited by the a priori knowledge of viral reference genome sequences, the proposed methodology can provide a first confined but substantial insight into the presence, concentration and types of methylated viral sequences in MBD-seq data at low additional cost.

unifiedWMWqPCR: the unified Wilcoxon-Mann-Whitney test for analyzing RT-qPCR data in R.

MOTIVATION: Recently, De Neve et al. proposed a modification of the Wilcoxon-Mann-Whitney (WMW) test for assessing differential expression based on RT-qPCR data. Their test, referred to as the unified WMW (uWMW) test, incorporates a robust and intuitive normalization and quantifies the probability that the expression from one treatment group exceeds the expression from another treatment group. However, no software package for this test was available yet. RESULTS: We have developed a Bioconductor package for analyzing RT-qPCR data with the uWMW test. The package also provides graphical tools for visualizing the effect sizes. AVAILABILITY AND IMPLEMENTATION: The unifiedWMWqPCR package and its user documentation can be obtained through Bioconductor.

Next-generation technologies and data analytical approaches for epigenomics.

Epigenetics refers to the collection of heritable features that modulate the genome-environment interaction without being encoded in the actual DNA sequence. While being mitotically and sometimes even meiotically transmitted, epigenetic traits often demonstrate extensive flexibility. This allows cells to acquire diverse gene expression patterns during differentiation, but also to adapt to a changing environment. However, epigenetic alterations are not always beneficial to the organism, as they are, for example, frequently identified in human diseases such as cancer. Accurate and cost-efficient genome-scale profiling of epigenetic features is thus of major importance to pinpoint these "epimutations," for example, to monitor the epigenetic impact of environmental exposure. Over the last decade, the field of epigenetics has been revolutionized by several innovative "epigenomics" technologies exactly addressing this need. In this review, we discuss and compare widely used next-generation methods to assess DNA methylation and hydroxymethylation, noncoding RNA expression, histone modifications, and nucleosome positioning. Although recent methods are typically based on "second-generation" sequencing, we also pay attention to still commonly used array- and PCR-based methods, and look forward to the additional advantages of single-molecule sequencing. As the current bottleneck in epigenomics research is the analysis rather than generation of data, the basic difficulties and problem-solving strategies regarding data preprocessing and statistical analysis are introduced for the different technologies. Finally, we also consider the complications associated with epigenomic studies of species with yet unsequenced genomes and possible solutions.

An extension of the Wilcoxon-Mann-Whitney test for analyzing RT-qPCR data.

Classical approaches for analyzing reverse transcription quantitative polymerase chain reaction (RT-qPCR) data commonly require normalization before assessing differential expression (DE). Normalization often has a substantial effect on the interpretation and validity of the subsequent analysis steps, but at the same time it causes a reduction in variance and introduces dependence among the normalized outcomes. These effects can be substantial, however, they are typically ignored. Most normalization techniques and methods for DE focus on mean expression and are sensitive to outliers. Moreover, in cancer studies, for example, oncogenes are often only expressed in a subsample of the populations during sampling. This primarily affects the skewness and the tails of the distribution and the mean is therefore not necessarily the best effect size measure within these experimental setups. In our contribution, we propose an extension of the Wilcoxon-Mann-Whitney test which incorporates a robust normalization, and the uncertainty associated with normalization is propagated into the final statistical summaries for DE. Our method relies on semiparametric regression models that focus on the probability P{Y ≤ Y'}, where Y and Y' denote independent responses for different subject groups. This effect size is robust to outliers, while remaining informative and intuitive when DE affects the shape of the distribution instead of only the mean. We also extend our approach for assessing DE for multiple features simultaneously. Simulation studies show that the test has a good performance, and that it is very competitive with standard methods for this platform. The method is illustrated on two neuroblastoma studies.

A predictive model for dysphagia following IMRT for head and neck cancer: introduction of the EMLasso technique.

BACKGROUND AND PURPOSE: Design a model for prediction of acute dysphagia following intensity-modulated radiotherapy (IMRT) for head and neck cancer. Illustrate the use of the EMLasso technique for model selection. MATERIAL AND METHODS: Radiation-induced dysphagia was scored using CTCAE v.3.0 in 189 head and neck cancer patients. Clinical data (gender, age, nicotine and alcohol use, diabetes, tumor location), treatment parameters (chemotherapy, surgery involving the primary tumor, lymph node dissection, overall treatment time), dosimetric parameters (doses delivered to pharyngeal constrictor (PC) muscles and esophagus) and 19 genetic polymorphisms were used in model building. The predicting model was achieved by EMLasso, i.e. an EM algorithm to account for missing values, applied to penalized logistic regression, which allows for variable selection by tuning the penalization parameter through crossvalidation on AUC, thus avoiding overfitting. RESULTS: Fifty-three patients (28%) developed acute ≥ grade 3 dysphagia. The final model has an AUC of 0.71 and contains concurrent chemotherapy, D2 to the superior PC and the rs3213245 (XRCC1) polymorphism. The model's false negative rate and false positive rate in the optimal operation point on the ROC curve are 21% and 49%, respectively. CONCLUSIONS: This study demonstrated the utility of the EMLasso technique for model selection in predictive radiogenetics.

An omnibus consistent adaptive percentile modified Wilcoxon rank sum test with applications in gene expression studies.

We present an adaptive percentile modified Wilcoxon rank sum test for the two-sample problem. The test is basically a Wilcoxon rank sum test applied on a fraction of the sample observations, and the fraction is adaptively determined by the sample observations. Most of the theory is developed under a location-shift model, but we demonstrate that the test is also meaningful for testing against more general alternatives. The test may be particularly useful for the analysis of massive datasets in which quasi-automatic hypothesis testing is required. We investigate the power characteristics of the new test in a simulation study, and we apply the test to a microarray experiment on colorectal cancer. These empirical studies demonstrate that the new test has good overall power and that it succeeds better in finding differentially expressed genes as compared to other popular tests. We conclude that the new nonparametric test is widely applicable and that its power is comparable to the power of the Baumgartner-Weiß-Schindler test.

The effect of clinical covariates on the diagnostic and prognostic value of soluble mesothelin and megakaryocyte potentiating factor.

BACKGROUND: Soluble mesothelin (SM) and megakaryocyte potentiating factor (MPF) are serum biomarkers of mesothelioma. This study examined the effect of clinical covariates on biomarkers levels and their diagnostic and prognostic value. METHODS: Five hundred ninety-four participants were enrolled in a multicenter study, including 106 patients with mesothelioma and 488 control subjects. Multiple linear regression analyses were used to identify which covariates were independently associated with SM and MPF levels. The effect of these covariates on the diagnostic accuracy was evaluated with receiver operating characteristics curve analysis. In patients with mesothelioma, survival analysis was performed with Cox regression. RESULTS: SM and MPF levels were independently associated with age, glomerular filtration rate (GFR), and BMI in control subjects and with GFR and tumor stage in patients with mesothelioma. The diagnostic accuracy of SM and MPF was significantly affected by the distribution of these covariates in the study population. The patients with mesothelioma were best discriminated from the control subjects with either the youngest age, the highest GFR, or the largest BMI. Furthermore, the control subjects were significantly better differentiated from stage II to IV than from stage I mesothelioma. MPF, not SM, was an independent negative prognostic factor, but only if adjusted for the biomarker-associated covariates. CONCLUSIONS: SM and MPF levels were affected by the same clinical covariates, which also had a significant impact on their diagnostic and prognostic value. To improve the interpretation of biomarker results, age, GFR, and BMI should be routinely recorded. Approaches to account for these covariates require further validation, as does the prognostic value of SM and MPF.

Development of a multicomponent prediction model for acute esophagitis in lung cancer patients receiving chemoradiotherapy.

PURPOSE: To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile. PATIENTS AND METHODS: Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes, chronic disease, tumor type, tumor stage, lymph node stage, tumor location, and medical center. Treatment parameters included chemotherapy, surgery, radiotherapy technique, tumor dose, mean fractionation size, mean and maximal esophageal dose, and overall treatment time. A total of 332 genetic polymorphisms were considered in 112 candidate genes. The predicting model was achieved by lasso logistic regression for predictor selection, followed by classic logistic regression for unbiased estimation of the coefficients. Performance of the model was expressed as the area under the curve of the receiver operating characteristic and as the false-negative rate in the optimal point on the receiver operating characteristic curve. RESULTS: A total of 110 patients (40%) developed acute esophagitis Grade ≥2 (Common Terminology Criteria for Adverse Events v3.0). The final model contained chemotherapy treatment, lymph node stage, mean esophageal dose, gender, overall treatment time, radiotherapy technique, rs2302535 (EGFR), rs16930129 (ENG), rs1131877 (TRAF3), and rs2230528 (ITGB2). The area under the curve was 0.87, and the false-negative rate was 16%. CONCLUSION: Prediction of acute esophagitis can be improved by combining clinical, treatment, and genetic factors. A multicomponent prediction model for acute esophagitis with a sensitivity of 84% was constructed with two clinical parameters, four treatment parameters, and four genetic polymorphisms.