Prognostic factors in anti-neutrophil cytoplasmic antibody-associated glomerulonephritis with severe glomerular sclerosis: a national registry-based cohort study

Background. Classification of patients with anti-neutrophil cytoplasmic antibody-associated glomerulonephritis (ANCA-GN) into histological classes is useful for predicting a patient’s risk of progression to end-stage renal disease (ESRD). However, even in the worst prognostic group, the 5-year end-stage renal disease-free survival rate is as high as 50%. Objectives. To investigate those prognostic factors indicative of progression to ESRD in patients with ANCA-GN and sclerosing histology. Methods. Patients from the Norwegian Kidney Biopsy Registry between 1991 and 2012 who had biopsy verified pauci-immune glomerulonephritis, positive ANCA serology, and sclerosing histology were included. Cases with ESRD during follow-up were identified via linkage with the Norwegian Renal Registry. Potential prognostic factors with relevant cut-offs were compared in patients with and without progression to ESRD during follow-up. Results. Of 23 included patients, 10 progressed to ESRD. ESRD patients had a lower initial estimated glomerular filtration rate (eGFR; 21 versus 52 ml/min/1.73 m2) and a lower percentage of normal glomeruli (4% versus 15%). Five-year risks of ESRD with eGFR >15 versus ≤15 ml/min/1.73 m2 were 77% and 15%, with percentage normal glomeruli >10% versus ≤10%, 83% and 39%. Conclusions. eGFR and percentage of normal glomeruli are strong risk factors for ESRD in ANCA-GN with sclerosing histology.publishedVersio

Robust Reasoning for Autonomous Cyber-Physical Systems in Dynamic Environments

Autonomous cyber-physical systems, CPS, in dynamic environments must work impeccably. The cyber-physical systems must handle tasks consistently and trustworthily, i.e., with a robust behavior. Robust systems, in general, require making valid and solid decisions using one or a combination of robust reasoning strategies, algorithms, and robustness analysis. However, in dynamic environments, data can be incomplete, skewed, contradictory, and redundant impacting the reasoning. Basing decisions on these data can lead to inconsistent, irrational, and unreasonable cyber-physical systems' movements, adversely impacting the system’s reliability and integrity. This paper presents the assessment of robust reasoning for autonomous cyber-physical systems in dynamic environments. In this work, robust reasoning is considered as 1) the capability of drawing conclusions with available data by applying classical and non-classical reasoning strategies and algorithms and 2) act and react robustly and safely in dynamic environments by employing robustness analysis to provide options on possible actions and evaluate alternative decisions. The result of the research shows that different common existing strategies, algorithms and analyses can be provided together with a comparison of their applicabilities, benefits, and drawbacks in the context of cyber-physical systems operating in dynamically changing environments. The conclusion is that robust reasoning in cyber-physical systems can handle dynamic environments. Moreover, combining these strategies and algorithms with robustness analysis can support achieving robust behavior in autonomous cyber-physical systems while operating in dynamically changing environments

PathTracer: High-sensitivity detection of differential pathway activity in tumours

Abstract: Gene expression profiling of tumours is an important source of information for cancer patient stratification. Detecting subtle alterations of gene expression remains a challenge, however. Here, we propose a novel tool for high-sensitivity detection of differential pathway activity in tumours. For a pathway defined by a collection of genes, the samples are projected onto a low-dimensional manifold in the subspace spanned by those genes. For each sample, a score is next found by calculating the distance between each projected sample and the projection of a subgroup of reference samples. Depending on the aim of the analysis and the available data, the reference samples may represent e.g. normal tissue or tumour samples with a particular genotype or phenotype. The proposed tool, PathTracer, is demonstrated on gene expression data from 1952 invasive breast cancer samples, 10 DCIS, 9 benign samples and 144 tumour adjacent normal breast tissue samples. PathTracer scores are shown to predict survival, clinical subtypes, cellular proliferation and genomic instability. Furthermore, predictions are shown to outperform those obtained with other comparable methods

Sortsforsøk med erter og åkerbønner i 2021

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Subtype-specific micro-RNA expression signatures in breast cancer progression.

Robust markers of invasiveness may help reduce the overtreatment of in situ carcinomas. Breast cancer is a heterogeneous disease and biological mechanisms for carcinogenesis vary between subtypes. Stratification by subtype is therefore necessary to identify relevant and robust signatures of invasive disease. We have identified microRNA (miRNA) alterations during breast cancer progression in two separate datasets and used stratification and external validation to strengthen the findings. We analyzed two separate datasets (METABRIC and AHUS) consisting of a total of 186 normal breast tissue samples, 18 ductal carcinoma in situ (DCIS) and 1,338 invasive breast carcinomas. Validation in a separate dataset and stratification by molecular subtypes based on immunohistochemistry, PAM50 and integrated cluster classifications were performed. We propose subtype-specific miRNA signatures of invasive carcinoma and a validated signature of DCIS. miRNAs included in the invasive signatures include downregulation of miR-139-5p in aggressive subtypes and upregulation of miR-29c-5p expression in the luminal subtypes. No miRNAs were differentially expressed in the transition from DCIS to invasive carcinomas on the whole, indicating the need for subtype stratification. A total of 27 miRNAs were included in our proposed DCIS signature. Significant alterations of expression included upregulation of miR-21-5p and the miR-200 family and downregulation of let-7 family members in DCIS samples. The signatures proposed here can form the basis for studies exploring DCIS samples with increased invasive potential and serum biomarkers for in situ and invasive breast cancer.This work was performed as part of the EurocanPlatform which has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 260791. Portions of this research (Venn diagram creator) were supported by the W.R. Wiley Environmental Molecular Science Laboratory, a national scientific user facility sponsored by the U.S. Department of Energy's Office of Biological and Environmental Research and located at PNNL. PNNL is operated by Battelle Memorial Institute for the U.S. Department of Energy under contract DE-AC05-76RL0 1830.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/ijc.3014

Serum estradiol levels associated with specific gene expression patterns in normal breast tissue and in breast carcinomas

Abstract Background High serum levels of estradiol are associated with increased risk of postmenopausal breast cancer. Little is known about the gene expression in normal breast tissue in relation to levels of circulating serum estradiol. Methods We compared whole genome expression data of breast tissue samples with serum hormone levels using data from 79 healthy women and 64 breast cancer patients. Significance analysis of microarrays (SAM) was used to identify differentially expressed genes and multivariate linear regression was used to identify independent associations. Results Six genes (SCGB3A1, RSPO1, TLN2, SLITRK4, DCLK1, PTGS1) were found differentially expressed according to serum estradiol levels (FDR = 0). Three of these independently predicted estradiol levels in a multivariate model, as SCGB3A1 (HIN1) and TLN2 were up-regulated and PTGS1 (COX1) was down-regulated in breast samples from women with high serum estradiol. Serum estradiol, but none of the differentially expressed genes were significantly associated with mammographic density, another strong breast cancer risk factor. In breast carcinomas, expression of GREB1 and AREG was associated with serum estradiol in all cancers and in the subgroup of estrogen receptor positive cases. Conclusions We have identified genes associated with serum estradiol levels in normal breast tissue and in breast carcinomas. SCGB3A1 is a suggested tumor suppressor gene that inhibits cell growth and invasion and is methylated and down-regulated in many epithelial cancers. Our findings indicate this gene as an important inhibitor of breast cell proliferation in healthy women with high estradiol levels. In the breast, this gene is expressed in luminal cells only and is methylated in non-BRCA-related breast cancers. The possibility of a carcinogenic contribution of silencing of this gene for luminal, but not basal-like cancers should be further explored. PTGS1 induces prostaglandin E2 (PGE2) production which in turn stimulates aromatase expression and hence increases the local production of estradiol. This is the first report studying such associations in normal breast tissue in humans

Expression levels of uridine 5'-diphospho-glucuronosyltransferase genes in breast tissue from healthy women are associated with mammographic density

Introduction Mammographic density (MD), as assessed from film screen mammograms, is determined by the relative content of adipose, connective and epithelial tissue in the female breast. In epidemiological studies, a high percentage of MD confers a four to six fold risk elevation of developing breast cancer, even after adjustment for other known breast cancer risk factors. However, the biologic correlates of density are little known. Methods Gene expression analysis using whole genome arrays was performed on breast biopsies from 143 women; 79 women with no malignancy (healthy women) and 64 newly diagnosed breast cancer patients, both included from mammographic centres. Percent MD was determined using a previously validated, computerized method on scanned mammograms. Significance analysis of microarrays (SAM) was performed to identify genes influencing MD and a linear regression model was used to assess the independent contribution from different variables to MD. Results SAM-analysis identified 24 genes differentially expressed between samples from breasts with high and low MD. These genes included three uridine 5'-diphospho-glucuronosyltransferase (UGT) genes and the oestrogen receptor gene (ESR1). These genes were down-regulated in samples with high MD compared to those with low MD. The UGT gene products, which are known to inactivate oestrogen metabolites, were also down-regulated in tumour samples compared to samples from healthy individuals. Several single nucleotide polymorphisms (SNPs) in the UGT genes associated with the expression of UGT and other genes in their vicinity were identified. Conclusions Three UGT enzymes were lower expressed both in breast tissue biopsies from healthy women with high MD and in biopsies from newly diagnosed breast cancers. The association was strongest amongst young women and women using hormonal therapy. UGT2B10 predicts MD independently of age, hormone therapy and parity. Our results indicate that down-regulation of UGT genes in women exposed to female sex hormones is associated with high MD and might increase the risk of breast cancer

Gene expression profiles of breast biopsies from healthy women identify a group with claudin-low features

Background Increased understanding of the variability in normal breast biology will enable us to identify mechanisms of breast cancer initiation and the origin of different subtypes, and to better predict breast cancer risk. Methods Gene expression patterns in breast biopsies from 79 healthy women referred to breast diagnostic centers in Norway were explored by unsupervised hierarchical clustering and supervised analyses, such as gene set enrichment analysis and gene ontology analysis and comparison with previously published genelists and independent datasets. Results Unsupervised hierarchical clustering identified two separate clusters of normal breast tissue based on gene-expression profiling, regardless of clustering algorithm and gene filtering used. Comparison of the expression profile of the two clusters with several published gene lists describing breast cells revealed that the samples in cluster 1 share characteristics with stromal cells and stem cells, and to a certain degree with mesenchymal cells and myoepithelial cells. The samples in cluster 1 also share many features with the newly identified claudin-low breast cancer intrinsic subtype, which also shows characteristics of stromal and stem cells. More women belonging to cluster 1 have a family history of breast cancer and there is a slight overrepresentation of nulliparous women in cluster 1. Similar findings were seen in a separate dataset consisting of histologically normal tissue from both breasts harboring breast cancer and from mammoplasty reductions. Conclusion This is the first study to explore the variability of gene expression patterns in whole biopsies from normal breasts and identified distinct subtypes of normal breast tissue. Further studies are needed to determine the specific cell contribution to the variation in the biology of normal breasts, how the clusters identified relate to breast cancer risk and their possible link to the origin of the different molecular subtypes of breast cancer

Treatment Characteristics and Real-World Progression-Free Survival in Patients With Unresectable Stage III NSCLC Who Received Durvalumab After Chemoradiotherapy: Findings From the PACIFIC-R Study.

IntroductionThe phase 3 PACIFIC trial established consolidation therapy with durvalumab as standard of care for patients with unresectable, stage III NSCLC and no disease progression after definitive chemoradiotherapy (CRT). The observational PACIFIC-R study assesses the real-world effectiveness of durvalumab in patients from an early access program. Here, we report treatment characteristics and a preplanned analysis of real-world progression-free survival (rwPFS).MethodsPACIFIC-R (NCT03798535) is an ongoing, international, retrospective study of patients who started durvalumab (intravenously; 10 mg/kg every 2 wk) within an early access program between September 2017 and December 2018. The primary end points are investigator-assessed rwPFS and overall survival (analyzed by Kaplan-Meier method).ResultsAs of November 30, 2020, the full analysis set comprised 1399 patients from 11 countries (median follow-up duration, 23.5 mo). Patients received durvalumab for a median of 11.0 months. Median rwPFS was 21.7 months (95% confidence interval: 19.1-24.5). RwPFS was numerically longer among patients who received concurrent versus sequential CRT (median, 23.7 versus 19.3 mo) and among patients with programmed cell death-ligand 1 expression greater than or equal to 1% versus less than 1% (22.4 versus 15.6 mo). Overall, 16.5% of the patients had adverse events leading to treatment discontinuation; 9.5% of all patients discontinued because of pneumonitis or interstitial lung disease.ConclusionsConsolidation durvalumab after definitive CRT was well tolerated and effective in this large, real-world cohort study of patients with unresectable, stage III NSCLC. As expected, rwPFS was longer among patients who received concurrent versus sequential CRT and patients with higher programmed cell death-ligand 1 expression. Nevertheless, favorable rwPFS outcomes were observed regardless of these factors