The impact of hysterectomy on subsequent colonoscopy in women with Lynch Syndrome.

BACKGROUND AND AIMS: Women with Lynch Syndrome (LS) have a high risk of colorectal and endometrial cancer. They are recommended regular colonoscopies, and some choose prophylactic hysterectomy. The aim of this study was to determine the impact of hysterectomy on subsequent colonoscopy in these women. MATERIALS AND METHODS: A total of 219 LS women >30 years of age registered in the clinical registry at Section for Hereditary Cancer, Oslo University Hospital, were included. Data included hysterectomy status, other abdominal surgeries, and time of surgery. For colonoscopies, data were collected on cecal intubation rate, challenges, and level of pain. Observations in women with and without hysterectomy, and pre- and post-hysterectomy were compared. RESULTS: Cecal intubation rate was lower in women with hysterectomy than in those without (119/126 = 94.4% vs 88/88 = 100%, p = 0.025). Multivariate regression analysis showed an increased risk of challenging colonoscopies (OR,3.58; CI: 1.52-8.43; p = 0.003), and indicated a higher risk of painful colonoscopy (OR, 3.00; 95%CI: 0.99-17.44, p = 0.052), in women with hysterectomy compared with no hysterectomy. Comparing colonoscopy before and after hysterectomy, we also found higher rates of reported challenging colonoscopies post-hysterectomy (6/69 = 8.7% vs 23/69 = 33.3%, p < 0.001). CONCLUSIONS: Women with hysterectomy had a lower cecal intubation rate and a higher number of reported challenging colonoscopy than women with no hysterectomy. However, completion rate in the hysterectomy group was still as high as 94.4%. Thus, LS women who consider hysterectomy should not be advised against it.

Transcriptomic pan-cancer analysis using rank-based Bayesian inference.

The analysis of whole genomes of pan-cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank-based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top-ranked genomic features. We applied our method to RNA-seq data from cancer samples from 12 tumor types from the Cancer Genome Atlas. We identified a robust clustering that mostly reflects tissue of origin but also includes pan-cancer clusters. Importantly, we identified three pan-squamous clusters composed of a mix of lung squamous cell carcinoma, head and neck squamous carcinoma, and bladder cancer, with different biological functions over-represented in the top genes that characterize the three clusters. We also found two novel subtypes of kidney cancer that show different prognosis, and we reproduced known subtypes of breast cancer. Taken together, our method allows the identification of robust and biologically meaningful clusters of pan-cancer samples.

Endometrial Carcinoma: Molecular Cytogenetics and Transcriptomic Profile.

Endometrial carcinomas (ECs) are histologically classified as endometrioid and nonendometrioid tumors, with each subgroup displaying different molecular profiles and clinical outcomes. Considerable biological and clinical heterogeneity exists within this scheme, however, reflecting its imperfection. We aimed to gather additional data that might help clarify the tumors' pathogenesis and contribute toward a more meaningful classification scheme. In total, 33 ECs were examined for the presence of chromosomal aberrations, genomic imbalances, pathogenic variants, microsatellite instability, and expression profiles at both gene and miRNA levels. Chromosome 1 was the most frequently rearranged chromosome, showing a gain of all or part of the long arm. Pathogenic variants were found for PTEN (53%), PDGFRA (37%), PIK3CA (34%), and KIT (31%). High microsatellite instability was identified in 15 ECs. Comparing tumors and controls, we identified 23 differentially expressed genes of known importance in carcinogenesis, 15 genes involved in innate and adaptative immune responses, and altered expression of 7 miRNAs. miR-32-5p was the most upregulated. Our series showed a high degree of heterogeneity. Tumors were well-separated from controls, but there was no clear-cut separation between endometrioid and nonendometrioid ECs. Whether this means that the current phenotypic classification is of little relevance or if one still has not detected which genomic parameters to enter into correlation analyses remains unknown.

Quantitative proteomics reveals protein dysregulation during T cell activation in multiple sclerosis patients compared to healthy controls.

BACKGROUND: Multiple sclerosis (MS) is an autoimmune, neurodegenerative disorder with a strong genetic component that acts in a complex interaction with environmental factors for disease development. CD4+ T cells are pivotal players in MS pathogenesis, where peripherally activated T cells migrate to the central nervous system leading to demyelination and axonal degeneration. Through a proteomic approach, we aim at identifying dysregulated pathways in activated T cells from MS patients as compared to healthy controls. METHODS: CD4+ T cells were purified from peripheral blood from MS patients and healthy controls by magnetic separation. Cells were left unstimulated or stimulated in vitro through the TCR and costimulatory CD28 receptor for 24 h prior to sampling. Electrospray liquid chromatography-tandem mass spectrometry was used to measure protein abundances. RESULTS: Upon T cell activation the abundance of 1801 proteins was changed. Among these proteins, we observed an enrichment of proteins expressed by MS-susceptibility genes. When comparing protein abundances in T cell samples from healthy controls and MS patients, 18 and 33 proteins were differentially expressed in unstimulated and stimulated CD4+ T cells, respectively. Moreover, 353 and 304 proteins were identified as proteins exclusively induced upon T cell activation in healthy controls and MS patients, respectively and dysregulation of the Nur77 pathway was observed only in samples from MS patients. CONCLUSIONS: Our study highlights the importance of CD4+ T cell activation for MS, as proteins that change in abundance upon T cell activation are enriched for proteins encoded by MS susceptibility genes. The results provide evidence for proteomic disturbances in T cell activation in MS, and pinpoint to dysregulation of the Nur77 pathway, a biological pathway known to limit aberrant effector T cell responses.

Rank-based Bayesian variable selection for genome-wide transcriptomic analyses.

Variable selection is crucial in high-dimensional omics-based analyses, since it is biologically reasonable to assume only a subset of non-noisy features contributes to the data structures. However, the task is particularly hard in an unsupervised setting, and a priori ad hoc variable selection is still a very frequent approach, despite the evident drawbacks and lack of reproducibility. We propose a Bayesian variable selection approach for rank-based unsupervised transcriptomic analysis. Making use of data rankings instead of the actual continuous measurements increases the robustness of conclusions when compared to classical statistical methods, and embedding variable selection into the inferential tasks allows complete reproducibility. Specifically, we develop a novel extension of the Bayesian Mallows model for variable selection that allows for a full probabilistic analysis, leading to coherent quantification of uncertainties. Simulation studies demonstrate the versatility and robustness of the proposed method in a variety of scenarios, as well as its superiority with respect to several competitors when varying the data dimension or data generating process. We use the novel approach to analyze genome-wide RNAseq gene expression data from ovarian cancer patients: several genes that affect cancer development are correctly detected in a completely unsupervised fashion, showing the usefulness of the method in the context of signature discovery for cancer genomics. Moreover, the possibility to also perform uncertainty quantification plays a key role in the subsequent biological investigation.

Epigenetic alterations at distal enhancers are linked to proliferation in human breast cancer.

Aberrant DNA methylation is an early event in breast carcinogenesis and plays a critical role in regulating gene expression. Here, we perform genome-wide expression-methylation Quantitative Trait Loci (emQTL) analysis through the integration of DNA methylation and gene expression to identify disease-driving pathways under epigenetic control. By grouping the emQTLs using biclustering we identify associations representing important biological processes associated with breast cancer pathogenesis including regulation of proliferation and tumor-infiltrating fibroblasts. We report genome-wide loss of enhancer methylation at binding sites of proliferation-driving transcription factors including CEBP-ß, FOSL1, and FOSL2 with concomitant high expression of proliferation-related genes in aggressive breast tumors as we confirm with scRNA-seq. The identified emQTL-CpGs and genes were found connected through chromatin loops, indicating that proliferation in breast tumors is under epigenetic regulation by DNA methylation. Interestingly, the associations between enhancer methylation and proliferation-related gene expression were also observed within known subtypes of breast cancer, suggesting a common role of epigenetic regulation of proliferation. Taken together, we show that proliferation in breast cancer is linked to loss of methylation at specific enhancers and transcription factor binding and gene activation through chromatin looping.

Autofluorescence Imaging in the Long-Term Follow-Up of Scleral Buckling Surgery for Retinal Detachment.

Purpose: To analyse fundus autofluorescence (AF) changes in retinal reattachment following primary scleral buckling (SB) surgery for rhegmatogenous retinal detachment (RRD). Methods: Prospective noninterventional chart review study. AF images were reviewed for peripheral and central changes and compared to clinical and OCT findings. Results: A total of 73 eyes from 69 patients were included, four presenting with bilateral RRD. Mean age was 55 ± 12 years, male/female ratio 40/29, fovea-on/-off RRD 43/30, and mean follow-up time 376 ± 270 days, with a mean of 5 ± 3 postoperative visits. Preoperatively, RRD was seen as a hypofluorescent area with a hyperfluorescent leading edge. Immediately postoperatively, three types of cryopexy could be differentiated, gradually transforming to scleral hyperfluorescence. Buckle tightening produced alternating hyper-/hypofluorescent streaks, and demarcation lines showed a persistent rugged hyperfluorescent signal. Choroidal detachment led to transient hypofluorescence, whereas vortex vein compression induced persistent hypofluorescence. Peripheral retinal folds were hyperfluorescent and the drainage site was hypofluorescent. AF was highly sensitive in detecting even small amounts of hyperfluorescent persistent subretinal fluid (SRF) that showed a slow resolution during follow-up. A granular "salt-and-pepper-" like pattern in the central macula was seen in 80% of eyes with fovea-off RRD and alternating streaks in 10%. Findings from OCT imaging correlated well with AF regarding SRF, macular oedema, retinal pigment epithelial detachment, and presence of a subretinal scar, but only moderately in epiretinal membrane formation and choroidal folds. Conclusions: AF is a useful, noninvasive, adjuvant tool in the long-term follow-up after SB surgery.

Regional performance variation in external validation of four prediction models for severity of COVID-19 at hospital admission: An observational multi-centre cohort study.

BACKGROUND: Prediction models should be externally validated to assess their performance before implementation. Several prediction models for coronavirus disease-19 (COVID-19) have been published. This observational cohort study aimed to validate published models of severity for hospitalized patients with COVID-19 using clinical and laboratory predictors. METHODS: Prediction models fitting relevant inclusion criteria were chosen for validation. The outcome was either mortality or a composite outcome of mortality and ICU admission (severe disease). 1295 patients admitted with symptoms of COVID-19 at Kings Cross Hospital (KCH) in London, United Kingdom, and 307 patients at Oslo University Hospital (OUH) in Oslo, Norway were included. The performance of the models was assessed in terms of discrimination and calibration. RESULTS: We identified two models for prediction of mortality (referred to as Xie and Zhang1) and two models for prediction of severe disease (Allenbach and Zhang2). The performance of the models was variable. For prediction of mortality Xie had good discrimination at OUH with an area under the receiver-operating characteristic (AUROC) 0.87 [95% confidence interval (CI) 0.79-0.95] and acceptable discrimination at KCH, AUROC 0.79 [0.76-0.82]. In prediction of severe disease, Allenbach had acceptable discrimination (OUH AUROC 0.81 [0.74-0.88] and KCH AUROC 0.72 [0.68-0.75]). The Zhang models had moderate to poor discrimination. Initial calibration was poor for all models but improved with recalibration. CONCLUSIONS: The performance of the four prediction models was variable. The Xie model had the best discrimination for mortality, while the Allenbach model had acceptable results for prediction of severe disease.

Characterization of Lipids in Saliva, Tears and Minor Salivary Glands of Sjögren's Syndrome Patients Using an HPLC/MS-Based Approach.

The diagnostic work-up of primary Sjögren's syndrome (pSS) includes quantifying saliva and tear production, evaluation of autoantibodies in serum and histopathological analysis of minor salivary glands. Thus, the potential for further utilizing these fluids and tissues in the quest to find better diagnostic and therapeutic tools should be fully explored. Ten samples of saliva and tears from female patients diagnosed with pSS and ten samples of saliva and tears from healthy females were included for lipidomic analysis of tears and whole saliva using high-performance liquid chromatography coupled to time-of-flight mass spectrometry. In addition, lipidomic analysis was performed on minor salivary gland biopsies from three pSS and three non-SS females. We found significant differences in the lipidomic profiles of saliva and tears in pSS patients compared to healthy controls. Moreover, there were differences in individual lipid species in stimulated saliva that were comparable to those of glandular biopsies, representing an intriguing avenue for further research. We believe a comprehensive elucidation of the changes in lipid composition in saliva, tears and minor salivary glands in pSS patients may be the key to detecting pSS-related dry mouth and dry eyes at an early stage. The identified differences may illuminate the path towards future innovative diagnostic methodologies and treatment modalities for alleviating pSS-related sicca symptoms.

Tear Film Break-Up Time and Dry Eye Disease Severity in a Large Norwegian Cohort.

This study evaluated to what extent tear film break-up time (TFBUT) could discriminate pathological scores for other clinical tests and explore the associations between them. Dry eye patients (n = 2094) were examined for ocular surface disease index (OSDI), tear film osmolarity (Osm), TFBUT, blink interval, ocular protection index (OPI), ocular surface staining (OSS), Schirmer I test, meibomian expressibility, meibomian quality, and meibomian gland dysfunction. The results were grouped into eight levels of break-up time (≤2, ≥3, ≤5, ≥6, ≤10, ≥11, ≤15, and ≥16) with or without sex stratification. Receiver-operating characteristic curve (ROC) analysis and Pearson's correlation coefficients were used to study TFBUT's discriminative power and the associations among the tests, respectively. Above and below each TFBUT's cut-off, all of the parameters indicated significant difference between groups, except OSDI (cut-off 15 s) and Osm (cut-offs 5 s-15 s). At TFBUT cut-off of 2 s, sex difference could be detected for OSDI, Osm, and OSS. OPI presented the strongest discriminative power and association with TFBUT in sharp contrast to Osm, holding the poorest discriminative power with no significant correlation. The remaining parameters were within the poor to very poor categories, both with regard to discrimination and correlation. In conclusion, patients with lower TFBUT presented with more severe DED parameters at all four defined cut-off values.

DNA copy number motifs are strong and independent predictors of survival in breast cancer.

Somatic copy number alterations are a frequent sign of genome instability in cancer. A precise characterization of the genome architecture would reveal underlying instability mechanisms and provide an instrument for outcome prediction and treatment guidance. Here we show that the local spatial behavior of copy number profiles conveys important information about this architecture. Six filters were defined to characterize regional traits in copy number profiles, and the resulting Copy Aberration Regional Mapping Analysis (CARMA) algorithm was applied to tumors in four breast cancer cohorts (n = 2919). The derived motifs represent a layer of information that complements established molecular classifications of breast cancer. A score reflecting presence or absence of motifs provided a highly significant independent prognostic predictor. Results were consistent between cohorts. The nonsite-specific occurrence of the detected patterns suggests that CARMA captures underlying replication and repair defects and could have a future potential in treatment stratification.

Utility of Tear Osmolarity Measurement in Diagnosis of Dry Eye Disease.

The prevalence of dry eye disease is high worldwide and poses a great burden on patients' daily lives. Accurate diagnosis of the disease is important, and it requires application of various methods. Hyperosmolarity is believed to be the disease marker and thus measuring it provides useful information. In this study we investigated utility of tear osmolarity measured with TearLab osmometer, along with other diagnostic tests (Ocular Surface Disease Index questionnaire, Tear film break-up time, Ocular Protection Index, Ocular Surface Staining, Schirmer I test, Meibomian gland functionality in 757 patients (1514 eyes) with dry eye disease and 29 healthy controls (58 eyes). Statistical differences between the patient group and the control group were observed for all the tests apart from tear osmolarity, regardless of cut-off value (>308 mOsm/L, >316 mOsm/L, and inter-eye difference >8 mOsm/L). Moreover, in the receiver operating characteristics curve analyses tear osmolarity measurement could not discriminate dry eye disease pathological scores. Therefore, our study suggests that tear osmolarity measured with TearLab osmometer cannot be used as a key indicator of DED.

NEWS2 versus a single-parameter system to identify critically ill medical patients in the emergency department.

AIM: To test National Early Warning Score 2 (NEWS2) versus a single-parameter system to identify critically ill general medical patients in the emergency department (ED), by 1) testing NEWS2s prediction of and association with primary outcome 'mortality' (hospital or 30 day) and secondary outcomes 'intensive care unit (ICU) admission' and 'critical care in ED' and 2) comparing this for different NEWS2 cut-offs and the single-parameter system in use. METHODS: Register-data on adult triage 1 and 2 patients with complete NEWS2 from 2015 and 2016 were retrieved. Prediction was assessed using area under the receiver-operating characteristic curve. Associations were analyzed using multiple logistic regression. RESULTS: 1586 patients were included. NEWS2 showed poor prediction of 'mortality' (AUC 0.686, CI 0.633-0.739) and adequate prediction of 'ICU admission' (AUC 0.716, CI 0.690-0.742) and 'critical care in ED' (AUC 0.756, CI 0.732-0.780). It was strongly associated with all outcomes (all p<0.001). All NEWS2 cut-offs and the single-parameter system showed poor prediction of all outcomes (all AUCs <0.7). The single-parameter system had the strongest association with 'mortality' (OR 1.688, CI 1.052-2.708, p<0.05) and 'critical care in ED' (OR 3.267, CI 2.490-4.286, p<0.001). NEWS2 > 4 had the strongest association with 'ICU admission' (OR 2.339, CI 1.742-3.141, p<0.001). CONCLUSION: For identification in order to trigger a response in the ED, outcomes closest in time seem most clinically relevant. As such, the single-parameter system had acceptable performance. NEWS2 > 4 should be considered as an additional trigger due to its association with ICU admission.

The Evans' Index revisited: New cut-off levels for use in radiological assessment of ventricular enlargement in the elderly.

BACKGROUND AND PURPOSE: Assessment of ventricular enlargement is subjective and based on the radiologist's experience. Linear indices, such as the Evans Index (EI), have been proposed as markers of ventricular volume with an EI≥0.3 indicating pathologic ventricular enlargement in any subject. However, normal range for EI measured on magnetic resonance imaging (MRI) scans are lacking in healthy elderly according to age and sex. We propose new age and sex specific cut-off values for ventricular enlargement in the elderly population. MATERIALS AND METHODS: 534 participants (53% women) aged 65-84 years; 226 patients with Alzheimer's disease (AD), and 308 healthy elderly controls (CTR) from the AddNeuroMed and ADNI studies were included. The cut-off for pathological ventricular enlargement was estimated from healthy elderly categorized into age groups of 5 years range and defined as EI 97,5 percentile (mean+2SD). Cut-off values were tested on patients with Alzheimer's disease and a small sample of patients with probable idiopathic normal pressure hydrocephalus (iNPH) to assess the sensitivity. RESULTS: The range of the EI in healthy elderly is wide and 29% of the CTR had an EI of 0.3 or greater. The EI increases with age in both CTR and AD, and the overall EI for women were lower than for men (p<0.001). New EI cut off values for male/female: 65-69 years 0.34/0.32, 70-74 years 0.36/0.33, 75-79 years 0.37/0.34 and 80-84 years 0.37/0.36. When applying the proposed cut-offs for EI in men and women aged 65-84, they differentiated between iNPH and CTR with a sensitivity of 80% and for different age and sex categories of AD and CTR with a sensitivity and specificity of 0-27% and 91-98%, respectively. CONCLUSION: The range of the EI measurements in healthy elderly is wide, and a cut-off value of 0.3 cannot be used to differentiate between normal and enlarged ventricles in individual cases. The proposed EI thresholds from the present study show good sensitivity for the iNPH diagnosis.

Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome.

BACKGROUND: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. METHODS: Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering. RESULTS: Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. CONCLUSIONS: The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.

Molecular Features of Subtype-Specific Progression from Ductal Carcinoma In Situ to Invasive Breast Cancer.

Breast cancer consists of at least five main molecular "intrinsic" subtypes that are reflected in both pre-invasive and invasive disease. Although previous studies have suggested that many of the molecular features of invasive breast cancer are established early, it is unclear what mechanisms drive progression and whether the mechanisms of progression are dependent or independent of subtype. We have generated mRNA, miRNA, and DNA copy-number profiles from a total of 59 in situ lesions and 85 invasive tumors in order to comprehensively identify those genes, signaling pathways, processes, and cell types that are involved in breast cancer progression. Our work provides evidence that there are molecular features associated with disease progression that are unique to the intrinsic subtypes. We additionally establish subtype-specific signatures that are able to identify a small proportion of pre-invasive tumors with expression profiles that resemble invasive carcinoma, indicating a higher likelihood of future disease progression.

A cumulative genetic risk score predicts progression in Parkinson's disease.

BACKGROUND: The contribution of genetic variability to clinical heterogeneity in Parkinson's disease is insufficiently understood. We aimed to investigate the effect of cumulative genetic risk on clinical outcomes. METHODS: In a single-center study of 336 patients we genotyped 19 independent susceptibility variants identified in genome-wide association studies of Parkinson's disease. We tested for association between a cumulative genetic risk score and 3 outcome measures: survival, time until progression to Hoehn and Yahr stage 3, and Unified Parkinson's Disease Rating Scale motor score severity. RESULTS: Genetic risk score was significantly associated with time from diagnosis to Hoehn and Yahr stage 3 in a Cox regression model (P = 0.010). We observed no clear association for the other outcomes. CONCLUSIONS: We present results linking cumulative genetic risk to a motor outcome in Parkinson's disease. Our findings provide a valuable starting point for future large-scale efforts to map the genetic determinants of phenotypic variability.

An Interval-Valued Neural Network Approach for Uncertainty Quantification in Short-Term Wind Speed Prediction.

We consider the task of performing prediction with neural networks (NNs) on the basis of uncertain input data expressed in the form of intervals. We aim at quantifying the uncertainty in the prediction arising from both the input data and the prediction model. A multilayer perceptron NN is trained to map interval-valued input data onto interval outputs, representing the prediction intervals (PIs) of the real target values. The NN training is performed by nondominated sorting genetic algorithm-II, so that the PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). Demonstration of the proposed method is given in two case studies: 1) a synthetic case study, in which the data have been generated with a 5-min time frequency from an autoregressive moving average model with either Gaussian or Chi-squared innovation distribution and 2) a real case study, in which experimental data consist of wind speed measurements with a time step of 1 h. Comparisons are given with a crisp (single-valued) approach. The results show that the crisp approach is less reliable than the interval-valued input approach in terms of capturing the variability in input.