Large-scale meta-genome-wide association study reveals common genetic factors linked to radiation-induced acute toxicities across cancer types.

BACKGROUND: This study was designed to identify common genetic susceptibility and shared genetic variants associated with acute radiation-induced toxicity across 4 cancer types (prostate, head and neck, breast, and lung). METHODS: A genome-wide association study meta-analysis was performed using 19 cohorts totaling 12 042 patients. Acute standardized total average toxicity (STATacute) was modelled using a generalized linear regression model for additive effect of genetic variants, adjusted for demographic and clinical covariates (rSTATacute). Linkage disequilibrium score regression estimated shared single-nucleotide variation (SNV-formerly SNP)-based heritability of rSTATacute in all patients and for each cancer type. RESULTS: Shared SNV-based heritability of STATacute among all cancer types was estimated at 10% (SE = 0.02) and was higher for prostate (17%, SE = 0.07), head and neck (27%, SE = 0.09), and breast (16%, SE = 0.09) cancers. We identified 130 suggestive associated SNVs with rSTATacute (5.0 × 10‒8 < P < 1.0 × 10‒5) across 25 genomic regions. rs142667902 showed the strongest association (effect allele A; effect size ‒0.17; P = 1.7 × 10‒7), which is located near DPPA4, encoding a protein involved in pluripotency in stem cells, which are essential for repair of radiation-induced tissue injury. Gene-set enrichment analysis identified 'RNA splicing via endonucleolytic cleavage and ligation' (P = 5.1 × 10‒6, P = .079 corrected) as the top gene set associated with rSTATacute among all patients. In silico gene expression analysis showed that the genes associated with rSTATacute were statistically significantly up-regulated in skin (not sun exposed P = .004 corrected; sun exposed P = .026 corrected). CONCLUSIONS: There is shared SNV-based heritability for acute radiation-induced toxicity across and within individual cancer sites. Future meta-genome-wide association studies among large radiation therapy patient cohorts are worthwhile to identify the common causal variants for acute radiotoxicity across cancer types.

Genome-wide association study of treatment-related toxicity two years following radiotherapy for breast cancer.

BACKGROUND AND PURPOSE: Up to a quarter of breast cancer patients treated by surgery and radiotherapy experience clinically significant toxicity. If patients at high risk of adverse effects could be identified at diagnosis, their treatment could be tailored accordingly. This study was designed to identify common single nucleotide polymorphisms (SNPs) associated with toxicity two years following whole breast radiotherapy. MATERIALS AND METHODS: A genome-wide association study (GWAS) was performed in 1,640 breast cancer patients with complete SNP, clinical, treatment and toxicity data, recruited across 18 European and US centres into the prospective REQUITE cohort study. Toxicity data (CTCAE v4.0) were collected at baseline, end of radiotherapy, and annual follow-up. A total of 7,097,340 SNPs were tested for association with the residuals of toxicity endpoints, adjusted for clinical, treatment co-variates and population substructure. RESULTS: Quantile-quantile plots showed more associations with toxicity above the p < 5 × 10-5 level than expected by chance. Eight SNPs reached genome-wide significance. Nipple retraction grade ≥ 2 was associated with the rs188287402 variant (p = 2.80 × 10-8), breast oedema grade ≥ 2 with rs12657177 (p = 1.12 × 10-10), rs75912034 (p = 1.12 × 10-10), rs145328458 (p = 1.06 × 10-9) and rs61966612 (p = 1.23 × 10-9), induration grade ≥ 2 with rs77311050 (p = 2.54 × 10-8) and rs34063419 (p = 1.21 × 10-8), and arm lymphoedema grade ≥ 1 with rs643644 (p = 3.54 × 10-8). Heritability estimates across significant endpoints ranged from 25% to 39%. Our study did not replicate previously reported SNPs associated with breast radiation toxicity at the pre-specified significance level. CONCLUSIONS: This GWAS for long-term breast radiation toxicity provides further evidence for significant association of common SNPs with distinct toxicity endpoints.

Penalized least-squares for imaging with hypertelescopes.

Practical considerations such as cost constrain the aperture size of conventional telescopes, which, combined with atmospheric turbulence effects, even in the presence of adaptive optics, limit achievable angular resolution. Sparse aperture telescopes represent a viable alternative for achieving improved angular resolution by combining light collected from small apertures distributed over a wide spatial area either using amplitude interferometry or a direct imaging approach to beam-combining. The so-called densified hypertelescope imaging concept in particular provides a methodology for direct image formation from large sparse aperture arrays. The densification system suppresses wide-angle side lobes and concentrates that energy in the center of the focal plane, significantly improving the signal-to-noise ratio of the measurement. Even with densification, an inevitable consequence of sparse aperture sampling is that the point-spread function associated with the direct image contains an additional structure not present in full aperture imaging systems. Postdetection image reconstruction is performed here to compute a high-fidelity estimate of the measured object in the presence of noise. In this paper, we describe a penalized least-squares object-estimation approach and compare the results with the classical Richardson-Lucy deconvolution algorithm as it is applied to hypertelescope image formation. The parameters of the algorithm are selected based on a comprehensive simulation study using the structure similarity metric to assess reconstruction performance. We find that the penalized least-squares formulation with optimized parameters provides significantly improved reconstructions compared with the conventional Richardson-Lucy algorithm.

Treatment time and circadian genotype interact to influence radiotherapy side-effects. A prospective European validation study using the REQUITE cohort.

BACKGROUND: Circadian rhythm impacts broad biological processes, including response to cancer treatment. Evidence conflicts on whether treatment time affects risk of radiotherapy side-effects, likely because of differing time analyses and target tissues. We previously showed interactive effects of time and genotypes of circadian genes on late toxicity after breast radiotherapy and aimed to validate those results in a multi-centre cohort. METHODS: Clinical and genotype data from 1690 REQUITE breast cancer patients were used with erythema (acute; n=340) and breast atrophy (two years post-radiotherapy; n=514) as primary endpoints. Local datetimes per fraction were converted into solar times as predictors. Genetic chronotype markers were included in logistic regressions to identify primary endpoint predictors. FINDINGS: Significant predictors for erythema included BMI, radiation dose and PER3 genotype (OR 1.27(95%CI 1.03-1.56); P < 0.03). Effect of treatment time effect on acute toxicity was inconclusive, with no interaction between time and genotype. For late toxicity (breast atrophy), predictors included BMI, radiation dose, surgery type, treatment time and SNPs in CLOCK (OR 0.62 (95%CI 0.4-0.9); P < 0.01), PER3 (OR 0.65 (95%CI 0.44-0.97); P < 0.04) and RASD1 (OR 0.56 (95%CI 0.35-0.89); P < 0.02). There was a statistically significant interaction between time and genotypes of circadian rhythm genes (CLOCK OR 1.13 (95%CI 1.03-1.23), P < 0.01; PER3 OR 1.1 (95%CI 1.01-1.2), P < 0.04; RASD1 OR 1.15 (95%CI 1.04-1.28), P < 0.008), with peak time for toxicity determined by genotype. INTERPRETATION: Late atrophy can be mitigated by selecting optimal treatment time according to circadian genotypes (e.g. treat PER3 rs2087947C/C genotypes in mornings; T/T in afternoons). We predict triple-homozygous patients (14%) reduce chance of atrophy from 70% to 33% by treating in mornings as opposed to mid-afternoon. Future clinical trials could stratify patients treated at optimal times compared to those scheduled normally. FUNDING: EU-FP7.

Atmospheric turbulence characterization through multiframe blind deconvolution.

Characterization of turbulence in the atmosphere and mitigation of its effects in optical systems are important capabilities in both commercial and military applications. We present an image processing approach that jointly characterizes the magnitude of turbulence in the atmosphere and mitigates the adverse effects imposed on optical imaging systems. The magnitude of turbulence is measured indirectly through a series of image frames in terms of the atmospheric coherence length. We believe the results demonstrate the utility of the approach on both simulated and experimental data.

External Validation of a Predictive Model for Acute Skin Radiation Toxicity in the REQUITE Breast Cohort.

Background: Acute skin toxicity is a common and usually transient side-effect of breast radiotherapy although, if sufficiently severe, it can affect breast cosmesis, aftercare costs and the patient's quality-of-life. The aim of this study was to develop predictive models for acute skin toxicity using published risk factors and externally validate the models in patients recruited into the prospective multi-center REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side-effects and improve QUalITy of lifE in cancer survivors) study. Methods: Patient and treatment-related risk factors significantly associated with acute breast radiation toxicity on multivariate analysis were identified in the literature. These predictors were used to develop risk models for acute erythema and acute desquamation (skin loss) in three Radiogenomics Consortium cohorts of patients treated by breast-conserving surgery and whole breast external beam radiotherapy (n = 2,031). The models were externally validated in the REQUITE breast cancer cohort (n = 2,057). Results: The final risk model for acute erythema included BMI, breast size, hypo-fractionation, boost, tamoxifen use and smoking status. This model was validated in REQUITE with moderate discrimination (AUC 0.65), calibration and agreement between predicted and observed toxicity (Brier score 0.17). The risk model for acute desquamation, excluding the predictor tamoxifen use, failed to validate in the REQUITE cohort. Conclusions: While most published prediction research in the field has focused on model development, this study reports successful external validation of a predictive model using clinical risk factors for acute erythema following radiotherapy after breast-conserving surgery. This model retained discriminatory power but will benefit from further re-calibration. A similar model to predict acute desquamation failed to validate in the REQUITE cohort. Future improvements and more accurate predictions are expected through the addition of genetic markers and application of other modeling and machine learning techniques.

The sputum microbiome is distinct between COPD and health, independent of smoking history.

BACKGROUND: Airway bacterial dysbiosis is a feature of chronic obstructive pulmonary disease (COPD). However, there is limited comparative data of the lung microbiome between healthy smokers, non-smokers and COPD. METHODS: We compared the 16S rRNA gene-based sputum microbiome generated from pair-ended Illumina sequencing of 124 healthy subjects (28 smokers and 96 non-smokers with normal lung function), with single stable samples from 218 COPD subjects collected from three UK clinical centres as part of the COPDMAP consortium. RESULTS: In healthy subjects Firmicutes, Bacteroidetes and Actinobacteria were the major phyla constituting 88% of the total reads, and Streptococcus, Veillonella, Prevotella, Actinomyces and Rothia were the dominant genera. Haemophilus formed only 3% of the healthy microbiome. In contrast, Proteobacteria was the most dominant phylum accounting for 50% of the microbiome in COPD subjects, with Haemophilus and Moraxella at genus level contributing 25 and 3% respectively. There were no differences in the microbiome profile within healthy and COPD subgroups when stratified based on smoking history. Principal coordinate analysis on operational taxonomic units showed two distinct clusters, representative of healthy and COPD subjects (PERMANOVA, p = 0·001). CONCLUSION: The healthy and COPD sputum microbiomes are distinct and independent of smoking history. Our results underline the important role for Gammaproteobacteria in COPD.

Target Localization and Tracking by Fusing Doppler Differentials from Cellular Emanations with a Multi-Spectral Video Tracker.

We present an algorithm for fusing data from a constellation of RF sensors detecting cellular emanations with the output of a multi-spectral video tracker to localize and track a target with a specific cell phone. The RF sensors measure the Doppler shift caused by the moving cellular emanation and then Doppler differentials between all sensor pairs are calculated. The multi-spectral video tracker uses a Gaussian mixture model to detect foreground targets and SIFT features to track targets through the video sequence. The data is fused by associating the Doppler differential from the RF sensors with the theoretical Doppler differential computed from the multi-spectral tracker output. The absolute difference and the root-mean-square difference are computed to associate the Doppler differentials from the two sensor systems. Performance of the algorithm was evaluated using synthetically generated datasets of an urban scene with multiple moving vehicles. The presented fusion algorithm correctly associates the cellular emanation with the corresponding video target for low measurement uncertainty and in the presence of favorable motion patterns. For nearly all objects the fusion algorithm has high confidence in associating the emanation with the correct multi-spectral target from the most probable background target.

Sputum microbiome temporal variability and dysbiosis in chronic obstructive pulmonary disease exacerbations: an analysis of the COPDMAP study.

BACKGROUND: Recent studies suggest that lung microbiome dysbiosis, the disease associated disruption of the lung microbial community, might play a key role in chronic obstructive pulmonary disease (COPD) exacerbations. However, characterising temporal variability of the microbiome from large longitudinal COPD cohorts is needed to better understand this phenomenon. METHODS: We performed a 16S ribosomal RNA survey of microbiome on 716 sputum samples collected longitudinally at baseline and exacerbations from 281 subjects with COPD at three UK clinical centres as part of the COPDMAP consortium. RESULTS: The microbiome composition was similar among centres and between stable and exacerbations except for a small significant decrease of Veillonella at exacerbations. The abundance of Moraxella was negatively associated with bacterial alpha diversity. Microbiomes were distinct between exacerbations associated with bacteria versus eosinophilic airway inflammation. Dysbiosis at exacerbations, measured as significant within subject deviation of microbial composition relative to baseline, was present in 41% of exacerbations. Dysbiosis was associated with increased exacerbation severity indicated by a greater fall in forced expiratory volume in one second, forced vital capacity and a greater increase in CAT score, particularly in exacerbations with concurrent eosinophilic inflammation. There was a significant difference of temporal variability of microbial alpha and beta diversity among centres. The variation of beta diversity significantly decreased in those subjects with frequent historical exacerbations. CONCLUSIONS: Microbial dysbiosis is a feature of some exacerbations and its presence, especially in concert with eosinophilic inflammation, is associated with more severe exacerbations indicated by a greater fall in lung function. TRIAL REGISTRATION NUMBER: Results, NCT01620645.

Blood and sputum eosinophils in COPD; relationship with bacterial load.

BACKGROUND: Sputum and blood eosinophil counts predict corticosteroid effects in COPD patients. Bacterial infection causes increased airway neutrophilic inflammation. The relationship of eosinophil counts with airway bacterial load in COPD patients is uncertain. We tested the hypothesis that bacterial load and eosinophil counts are inversely related. METHODS: COPD patients were seen at stable state and exacerbation onset. Sputum was processed for quantitative polymerase chain reaction detection of the potentially pathogenic microorganisms (PPM) H. influenzae, M. catarrhalis and S. pneumoniae. PPM positive was defined as total load ≥1 × 104copies/ml. Sputum and whole blood were analysed for differential cell counts. RESULTS: At baseline, bacterial counts were not related to blood eosinophils, but sputum eosinophil % was significantly lower in patients with PPM positive compared to PPM negative samples (medians: 0.5% vs. 1.25% respectively, p = 0.01). Patients with PPM positive samples during an exacerbation had significantly lower blood eosinophil counts at exacerbation compared to baseline (medians: 0.17 × 109/L vs. 0.23 × 109/L respectively, p = 0.008), while no blood eosinophil change was observed with PPM negative samples. CONCLUSIONS: These findings indicate an inverse relationship between bacterial infection and eosinophil counts. Bacterial infection may influence corticosteroid responsiveness by altering the profile of neutrophilic and eosinophilic inflammation.

An informatics project and online "Knowledge Centre" supporting modern genotype-to-phenotype research.

Explosive growth in the generation of genotype-to-phenotype (G2P) data necessitates a concerted effort to tackle the logistical and informatics challenges this presents. The GEN2PHEN Project represents one such effort, with a broad strategy of uniting disparate G2P resources into a hybrid centralized-federated network. This is achieved through a holistic strategy focussed on three overlapping areas: data input standards and pipelines through which to submit and collect data (data in); federated, independent, extendable, yet interoperable database platforms on which to store and curate widely diverse datasets (data storage); and data formats and mechanisms with which to exchange, combine, and extract data (data exchange and output). To fully leverage this data network, we have constructed the "G2P Knowledge Centre" (http://www.gen2phen.org). This central platform provides holistic searching of the G2P data domain allied with facilities for data annotation and user feedback, access to extensive G2P and informatics resources, and tools for constructing online working communities centered on the G2P domain. Through the efforts of GEN2PHEN, and through combining data with broader community-derived knowledge, the Knowledge Centre opens up exciting possibilities for organizing, integrating, sharing, and interpreting new waves of G2P data in a collaborative fashion.

Locus-specific database domain and data content analysis: evolution and content maturation toward clinical use.

Genetic variation databases have become indispensable in many areas of health care. In addition, more and more experts are depositing published and unpublished disease-causing variants of particular genes into locus-specific databases (LSDBs). Some of these databases contain such extensive information that they have become known as knowledge bases. Here, we analyzed 1,188 LSDBs and their content for the presence or absence of 44 content criteria related to database features (general presentation, locus-specific information, database structure) and data content (data collection, summary table of variants, database querying). Our analyses revealed that several elements have helped to advance the field and reduce data heterogeneity, such as the development of specialized database management systems and the creation of data querying tools. We also identified a number of deficiencies, namely, the lack of detailed disease and phenotypic descriptions for each genetic variant and links to relevant patient organizations, which, if addressed, would allow LSDBs to better serve the clinical genetics community. We propose a structure, based on LSDBs and closely related repositories (namely, clinical genetics databases), which would contribute to a federated genetic variation browser and also allow the maintenance of variation data.

Sperm cross-over activity in regions of the human genome showing extreme breakdown of marker association.

Population diversity data have recently provided profound, albeit inferential, insights into meiotic recombination across the human genome, revealing a landscape dominated by thousands of cross-over hotspots. However, very few of these putative hotspots have been directly analyzed for cross-over activity. We now describe a search for very active hotspots, by using extreme breakdown of marker association as a guide for high-resolution sperm cross-over analysis. This strategy has led to the isolation of the most active cross-over hotspots yet described. Their morphology, sequence attributes, and cross-over processes are very similar to those seen at less active hotspots, but their activity in sperm is poorly predicted from population diversity information. Several of these hotspots showed evidence for biased gene conversion accompanying cross-over, in some cases associated with variation between men in cross-over activity and with two hotspots showing complete presence/absence polymorphism in different men. Hotspot polymorphism is very common at less active hotspots but curiously was not seen at any of the most active hotspots. This contrasts with the prediction that extreme hotspots should be the most vulnerable to attenuation by meiotic drive in favor of mutations that suppress recombination and should therefore show rapid rate evolution and thus variation in activity between men. Finally, these very intense hotspots provide a valuable resource for dissecting meiotic recombination processes and pathways in humans.

Meiotic recombination hot spots and human DNA diversity.

Meiotic recombination plays a key role in the maintenance of sequence diversity in the human genome. However, little is known about the fine-scale distribution and processes of recombination in human chromosomes, or how these impact on patterns of human diversity. We have therefore developed sperm typing systems that allow human recombination to be analysed at very high resolution. The emerging picture is that human crossovers are far from randomly distributed but instead are targeted into very narrow hot spots that can profoundly influence patterns of haplotype diversity in the human genome. These hot spots provide fundamental information on processes of human crossover and gene conversion, as well as evidence that they can violate basic rules of Mendelian inheritance.