Presence of IL-5 protein and IgE antibodies to staphylococcal enterotoxins in nasal polyps is associated with comorbid asthma.

BACKGROUND: Nasal polyps often are associated with asthma. The phenotype of these patients is unknown. OBJECTIVE: To identify the mucosal factors associated with asthma comorbidity, we analyzed the inflammatory patterns of nasal polyps. METHODS: Nasal polyps from 70 Belgian patients, 34% with asthma, were analyzed for type of inflammation, T-cell cytokines, and IgE antibodies to Staphylococcus aureus enterotoxins. The same investigations were repeated in 93 Chinese patients with polyps, a group with a low asthma comorbidity rate (8%). RESULTS: In Belgian patients with polyps, 54% of samples showed eosinophilic inflammation. A classification tree evaluation identified IL-5 as the main positive determinant. Enterotoxin IgE in tissue (37%) was associated with significantly increased total IgE and eosinophil cationic protein concentrations. Expression of enterotoxin IgE, total IgE at greater than 1,442 kU/L, and eosinophil cationic protein at greater than 17,109 µg/L in samples with a total IgE concentration of greater than 246 kU/L significantly predicted asthma (odds ratio, 5.8-13). Only 7.5% of the samples from Chinese patients with polyps showed eosinophilic inflammation. IL-5 was confirmed as a positive determinant of eosinophilic inflammation, and enterotoxin IgE in tissue (17% of patients) was associated with significantly increased total IgE and eosinophil cationic protein concentrations. The expression of IL-5 or total IgE at greater than 790 kU/L in samples with an IL-5 concentration of greater than 194 pg/mL significantly predicted comorbid asthma (odds ratio, 17.2-96). CONCLUSION: Mucosal inflammation in nasal polyps orchestrated by T(H)2 cytokines and amplified by S aureus enterotoxins is characterized by an increased eosinophilic inflammation and formation of IgE antibodies. This phenotype is associated with comorbid asthma in white and Asian patients with nasal polyps.

Combined high dose radiation and pazopanib in metastatic renal cell carcinoma: a phase I dose escalation trial.

BACKGROUND: The primary objective was to determine maximum tolerated radiation dose in patients with metastatic renal cell carcinoma on pazopanib treatment. METHODS: Treatment-naïve patients received pazopanib according to standard of care. Stereotactic body radiotherapy (SBRT) was delivered concurrently to the largest metastatic lesion at day 8, 10 and 12. SBRT doses were escalated in 3 dose levels (24 Gy/3, 30 Gy/3 and 36 Gy/3). Dose level was assigned using Time-to-Event Continual Reassessment Method with the target dose-limiting toxicity rate set to 0.25. RESULTS: Thirteen patients were included. One patient experienced dose limiting toxicity (DLT) at dose level 3 (grade 4 hypoglycemia). Maximum tolerated dose was not reached with a recommended dose of 36 Gy/3 having a probability of DLT of 11%. One-year local control was 83% (95% confidence interval 61-100) and 1-year progression-free survival was 28% (95% confidence interval 1-55). CONCLUSIONS: SBRT in combination with pazopanib is well tolerated with good local control and response rates outside the radiation field. TRIAL REGISTRATION: This trial was retrospectively registered on clinicaltrials.gov( NCT02334709 ) on January 6th, 2015.

Biological 18[F]-FDG-PET image-guided dose painting by numbers for painful uncomplicated bone metastases: A 3-arm randomized phase II trial.

BACKGROUND: Antalgic radiotherapy for bone metastases might be improved by implementing biological information in the radiotherapy planning using (18)F-FDG-PET-CT based dose painting by numbers (DPBN). MATERIALS AND METHODS: Patients with uncomplicated painful bone metastases were randomized (1:1:1) and blinded to receive either 8Gy in a single fraction with conventionally planned radiotherapy (arm A) or 8Gy in a single fraction with DPBN (dose range between 610Gy and 10Gy) (arm B) or 16Gy in a single fraction with DPBN (dose range between 1410Gy and 18Gy) (arm C). The primary endpoint was overall pain response at 1month. The phase II trial was designed to select the experimental arm with sufficient promise of efficacy to continue to a phase III trial. RESULTS: Forty-five patients were randomized. Eight (53%), 12 (80%) and 9 patients (60%) had an overall response to treatment in arm A, B and C, respectively. The estimated odds ratio of overall response for arm B vs. A is 3.5 (95% CI: 0.44-17.71, p=0.12). The estimated odds ratio of arm C vs. A is 1.31 (95% CI: 0.31-5.58, p=0.71). CONCLUSION: A single fraction of 8Gy with DPBN will be further evaluated in a phase III-trial.

A family-based association test to detect gene-gene interactions in the presence of linkage.

For many complex diseases, quantitative traits contain more information than dichotomous traits. One of the approaches used to analyse these traits in family-based association studies is the quantitative transmission disequilibrium test (QTDT). The QTDT is a regression-based approach that models simultaneously linkage and association. It splits up the association effect in a between- and a within-family genetic component to adjust and test for population stratification and includes a variance components method to model linkage. We extend this approach to detect gene-gene interactions between two unlinked QTLs by adjusting the definition of the between- and within-family component and the variance components included in the model. We simulate data to investigate the influence of the epistasis model, linkage disequilibrium patterns between the markers and the QTLs, and allele frequencies on the power and type I error rates of the approach. Results show that for some of the investigated settings, power gains are obtained in comparison with FAM-MDR. We conclude that our approach shows promising results for candidate-gene studies where too few markers are available to correct for population stratification using standard methods (for example EIGENSTRAT). The proposed method is applied to real-life data on hypertension from the FLEMENGHO study.

Comparison of genetic association strategies in the presence of rare alleles.

In the quest for the missing heritability of most complex diseases, rare variants have received increased attention. Advances in large-scale sequencing have led to a shift from the common disease/common variant hypothesis to the common disease/rare variant hypothesis or have at least reopened the debate about the relevance and importance of rare variants for gene discoveries. The investigation of modeling and testing approaches to identify significant disease/rare variant associations is in full motion. New methods to better deal with parameter estimation instabilities, convergence problems, or multiple testing corrections in the presence of rare variants or effect modifiers of rare variants are in their infancy. Using a recently developed semiparametric strategy to detect causal variants, we investigate the performance of the model-based multifactor dimensionality reduction (MB-MDR) technique in terms of power and family-wise error rate (FWER) control in the presence of rare variants, using population-based and family-based data (FAM-MDR). We compare family-based results obtained from MB-MDR analyses to screening findings from a quantitative trait Pedigree-based association test (PBAT). Population-based data were further examined using penalized regression models. We restrict attention to all available single-nucleotide polymorphisms on chromosome 4 and consider Q1 as the outcome of interest. The considered family-based methods identified marker C4S4935 in the VEGFC gene with estimated power not exceeding 0.35 (FAM-MDR), when FWER was kept under control. The considered population-based methods gave rise to highly inflated FWERs (up to 90% for PBAT screening).

A screening methodology based on Random Forests to improve the detection of gene-gene interactions.

The search for susceptibility loci in gene-gene interactions imposes a methodological and computational challenge for statisticians because of the large dimensionality inherent to the modelling of gene-gene interactions or epistasis. In an era in which genome-wide scans have become relatively common, new powerful methods are required to handle the huge amount of feasible gene-gene interactions and to weed out false positives and negatives from these results. One solution to the dimensionality problem is to reduce data by preliminary screening of markers to select the best candidates for further analysis. Ideally, this screening step is statistically independent of the testing phase. Initially developed for small numbers of markers, the Multifactor Dimensionality Reduction (MDR) method is a nonparametric, model-free data reduction technique to associate sets of markers with optimal predictive properties to disease. In this study, we examine the power of MDR in larger data sets and compare it with other approaches that are able to identify gene-gene interactions. Under various interaction models (purely and not purely epistatic), we use a Random Forest (RF)-based prescreening method, before executing MDR, to improve its performance. We find that the power of MDR increases when noisy SNPs are first removed, by creating a collection of candidate markers with RFs. We validate our technique by extensive simulation studies and by application to asthma data from the European Committee of Respiratory Health Study II.

FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals.

We propose a novel multifactor dimensionality reduction method for epistasis detection in small or extended pedigrees, FAM-MDR. It combines features of the Genome-wide Rapid Association using Mixed Model And Regression approach (GRAMMAR) with Model-Based MDR (MB-MDR). We focus on continuous traits, although the method is general and can be used for outcomes of any type, including binary and censored traits. When comparing FAM-MDR with Pedigree-based Generalized MDR (PGMDR), which is a generalization of Multifactor Dimensionality Reduction (MDR) to continuous traits and related individuals, FAM-MDR was found to outperform PGMDR in terms of power, in most of the considered simulated scenarios. Additional simulations revealed that PGMDR does not appropriately deal with multiple testing and consequently gives rise to overly optimistic results. FAM-MDR adequately deals with multiple testing in epistasis screens and is in contrast rather conservative, by construction. Furthermore, simulations show that correcting for lower order (main) effects is of utmost importance when claiming epistasis. As Type 2 Diabetes Mellitus (T2DM) is a complex phenotype likely influenced by gene-gene interactions, we applied FAM-MDR to examine data on glucose area-under-the-curve (GAUC), an endophenotype of T2DM for which multiple independent genetic associations have been observed, in the Amish Family Diabetes Study (AFDS). This application reveals that FAM-MDR makes more efficient use of the available data than PGMDR and can deal with multi-generational pedigrees more easily. In conclusion, we have validated FAM-MDR and compared it to PGMDR, the current state-of-the-art MDR method for family data, using both simulations and a practical dataset. FAM-MDR is found to outperform PGMDR in that it handles the multiple testing issue more correctly, has increased power, and efficiently uses all available information.