Marit Holden

ntroduction: The increasing incidence of endometrial cancer (EC) requires an extensive search for novel preventive tools and early intervention approaches. However, the development of reliable predictive models is impossible without knowledge of genetic alterations prior to diagnosis. In this work, we aimed to establish whether known EC risk factors are associated with peripheral blood gene expression changes in a prospective design and whether such associations differ between women who later developed EC and matched controls. Methods: First, we selected variables (parity status, lifetime number of years of menstruation, coffee consumption, body mass index (BMI), age at menopause, use of oral contraceptives) that were shown to have an impact on EC risk in a large prospective cohort (165,000 women). Next, using BeadChip microarray technology, we tested the association between these variables and gene expression profiles in RNA extracted from mixed circulating immune cells in a nested case-control study (79 case-control pairs) of women from the NOWAC postgenome cohort. Lastly, we undertook a gene set enrichment analysis (GSEA). Results: At overall gene expression level, we found no difference between the EC cases and controls. The introduction of parity status into the statistical model revealed changes in the expression of 1,379 genes in the controls, while we did not observe any expression changes in the cases. Twenty-seven genes were associated with BMI increase in the controls, whereas there was no association observed between changes in BMI and gene expression in women with EC. In GSEA, 2,407 significantly enriched gene sets were attributed to a parity increase among cancer-free women. Conclusion: In this study, we found that an increased number of parities has a life-long effect on the gene expression profile in the peripheral blood of women who never developed cancer. In contrast, in women who were diagnosed with EC later in life, neither multiparity nor elevated BMI showed a significant association with gene expression patterns. However, given the modest sample size and exploratory nature of the study, these findings should be verified in larger cohorts.

Climate sensitivity and aerosol forcing are two of the most central, but uncertain, quantities in climate science that are crucial for assessing historical climate as well as future climate projections. Here, we use a Bayesian approach to estimate inferred climate sensitivity and aerosol forcing using observations of temperature and global ocean heat content as well as prior knowledge of effective radiative forcing (ERF) over the industrial period. Due to limited information on uncertainties related to the time evolution of aerosol forcing, we perform a range of sensitivity analyses with idealized aerosol time evolution. The estimates are sensitive to the aerosol forcing pathway, with the mean estimate of inferred effective climate sensitivity ranging from 2.0 to 2.4 K, present-day (2019 relative to 1750) aerosol ERF ranging from −0.7 to −1.1 W m−2, and anthropogenic ERF ranging from 2.6 to 3.1 W m−2. Using observations and forcing up to and including 2022, the inferred effective climate sensitivity is 2.2 K with a 1.6 to 3.0 K 90 % uncertainty range. Analysis with more freely evolving aerosol forcing between 1950 and 2014 shows that a strong negative aerosol forcing trend in the latter part of the 20th century is not consistent with observations. Although we test our estimation method with strongly idealized aerosol ERF pathways, our posteriori estimates of the climate sensitivity consistently end up in the weaker end of the range assessed in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). As our method only includes climate feedbacks that have occurred over the historical period, it does not include the pattern effect, i.e., where climate feedbacks are dependent on the pattern of warming which will likely change into the future. Adding the best estimate of the pattern effect from IPCC AR6, our climate sensitivity estimate is almost identical to the IPCC AR6 best estimate and very likely range.

This study introduces an enhanced version of a two-stage modelling approach using artificial intelligence (AI) for breast cancer detection in mammography screening. Leveraging a large dataset of 2,863,175 mammograms from the BreastScreen Norway, the approach uses two convolutional neural networks. The first one is trained to classify whole images, and an explainable-AI method is applied to this network to identify a region of interest (ROI). The second neural network subsequently classifies the ROI for malignancy. While a prior method used simple gradient saliency maps to identify ROIs, a key enhancement of the present methodology is the application of Layered GradCam, which identifies cancerous areas more consistently and allows smaller ROIs. Layered GradCam is also used to display identified cancers to the user. By the AUC criterion, our model performs well, 0.974 for screen-detected and 0.931 for all cancers (screen-detected and interval), compared to a commercial program; 0.959 and 0.918, respectively. Comparisons with the radiologist scores indicate that the model has equal performance with two radiologists, and superior performance to one, for the detection of all cancers (screening- and interval type). Our tests indicate that our model generalizes well for different breast centers, but so far only images from a single manufacturer have been tested.

The Cancer Registry of Norway organises a population-based breast cancer screening program, where 250 000 women participate each year. The interpretation of the screening mammograms is a manual process, but deep neural networks are showing potential in mammographic screening. Most methods focus on methods trained from pixel-level annotations, but these require expertise and are time-consuming to produce. Through the screenings, image level annotations are however readily available. In this work we present a few models trained from image level annotations from the Norwegian dataset: a holistic model, an attention model and an ensemble model. We compared their performance with that of pretrained models based on pixel-level annotations, trained on international datasets. From this we found that models trained on our local data with image-level annotation gave considerably better performance than the pretrained models from external data, although based on pixel-level annotations.

Recent studies have indicated that there are functional genomic signals that can be detected in blood years before cancer diagnosis. This study aimed to assess gene expression in prospective blood samples from the Norwegian Women and Cancer cohort focusing on time to lung cancer diagnosis and metastatic cancer using a nested case–control design. We employed several approaches to statistically analyze the data and the methods indicated that the case–control differences were subtle but most distinguishable in metastatic case–control pairs in the period 0–3 years prior to diagnosis. The genes of interest along with estimated blood cell populations could indicate disruption of immunological processes in blood. The genes identified from approaches focusing on alterations with time to diagnosis were distinct from those focusing on the case–control differences. Our results support that explorative analyses of prospective blood samples could indicate circulating signals of disease-related processes.

Recent research published by Li and Abubakar (2020) investigated if the sediment interpretation could be done based on an idea inspired from video analysis, without involving human labelling. The aim of the study was understand how the proposed approach worked and investigate whether we could use it for detecting discontinuities in seismic structures.

Epithelial ovarian cancer (EOC) has a 5-year relative survival of 50%, partly because markers of early-stage disease are not available in current clinical diagnostics. The aim of the present study was to investigate whether EOC is associated with transcriptional profiles in blood collected up to 7 years before diagnosis. For this, we used RNA-stabilized whole blood, which contains circulating immune cells, from a sample of EOC cases from the population-based Norwegian Women and Cancer (NOWAC) postgenome cohort. We explored case-control differences in gene expression in all EOC (66 case-control pairs), as well as associations between gene expression and metastatic EOC (56 pairs), serous EOC (45 pairs, 44 of which were metastatic), and interval from blood sample collection to diagnosis (≤3 or >3 years; 34 and 31 pairs, respectively). Lastly, we assessed differential expression of genes associated with EOC in published functional genomics studies that used blood samples collected from newly diagnosed women. After adjustment for multiple testing, this nested case-control study revealed no significant case-control differences in gene expression in all EOC (false discovery rate q>0.96). With the exception of a few probes, the log2 fold change values obtained in gene-wise linear models were below ±0.2. P-values were lowest in analyses of metastatic EOC (80% of which were serous EOC). No common transcriptional profile was indicated by interval to diagnosis; when comparing the 100 genes with the lowest p-values in gene-wise tests in samples collected ≤3 and >3 years before EOC diagnosis, no overlap in these genes was observed. Among 86 genes linked to ovarian cancer in previous publications, our data contained expression values for 42, and of these, tests of LIME1, GPR162, STAB1, and SKAP1, resulted in unadjusted p<0.05. Although limited by sample size, our findings indicated less variation in blood gene expression between women with similar tumor characteristics.

Objective This explorative study aimed to assess if there are any time-dependent blood gene expression changes during the first one to eight years after breast cancer diagnosis, which can be linked to the clinical outcome of the disease. Material and methods A random distribution of follow-up time from breast cancer diagnosis till blood sampling was obtained by a nested, matched case-control design in the Norwegian Women and Cancer Post-genome Cohort. From 2002–5, women were invited to donate blood samples, regardless of any cancer diagnosis. At end of the study period in 2015, any cancer diagnoses in the 50 000 participants were obtained via linkage to the Norwegian Cancer Registry. For each breast cancer patient (n = 415), an age- and storage time-matched control was drawn. The design gave a uniform, random length of follow-up time, independent of cancer stage. Differences in blood gene expression between breast cancer cases and controls were identified using the Bioconductor R-package limma, using a moving window in time, to handle the varying time elapsed from diagnosis to blood sample. Results The number of differentially expressed genes between cases and controls were close to 2,000 in the first year after diagnosis, but fell sharply the second year. During the next years, a transient second increase was observed, but only in women with metastatic disease who later died, both compared to invasive cases that survived (p<0,001) and to metastatic cases that survived (p = 0.024). Among the differentially expressed genes there was an overrepresentation of heme metabolism and T cell-related processes. Conclusion This explorative analysis identified changing trajectories in the years after diagnosis, depending on clinical stage. Hypothetically, this could represent the escape of the metastatic cancer from the immune system.

Spectral Doppler measurements are an important part of the standard echocardiographic examination. These measurements give insight into myocardial motion and blood flow providing clinicians with parameters for diagnostic decision making. Many of these measurements are performed automatically with high accuracy, increasing the efficiency of the diagnostic pipeline. However, full automation is not yet available because the user must manually select which measurement should be performed on each image. In this work, we develop a pipeline based on convolutional neural networks (CNNs) to automatically classify the measurement type from cardiac Doppler scans. We show how the multi-modal information in each spectral Doppler recording can be combined using a meta parameter post-processing mapping scheme and heatmaps to encode coordinate locations. Additionally, we experiment with several architectures to examine the tradeoff between accuracy, speed, and memory usage for resource-constrained environments. Finally, we propose a confidence metric using the values in the last fully connected layer of the network and show that our confidence metric can prevent many misclassifications. Our algorithm enables a fully automatic pipeline from acquisition to Doppler spectrum measurements. We achieve 96% accuracy on a test set drawn from separate clinical sites, indicating that the proposed method is suitable for clinical adoption.

We investigated mechanisms resulting in low bone mineral density (BMD) and susceptibility to fracture by comparing noncoding RNAs (ncRNAs) in biopsies of non–weight‐bearing (NWB) iliac (n = 84) and weight bearing (WB) femoral (n = 18) postmenopausal bone across BMDs varying from normal (T‐score > −1.0) to osteoporotic (T‐score ≤ −2.5). Global bone ncRNA concentrations were determined by PCR and microchip analyses. Association with BMD or fracture, adjusted by age and body mass index, were calculated using linear and logistic regression and least absolute shrinkage and selection operator (Lasso) analysis. At 10% false discovery rate (FDR), 75 iliac bone ncRNAs and 94 femoral bone ncRNAs were associated with total hip BMD. Eight of the ncRNAs were common for the two sites, but five of them (miR‐484, miR‐328‐3p, miR‐27a‐5p, miR‐28‐3p, and miR‐409‐3p) correlated positively to BMD in femoral bone, but negatively in iliac bone. Of predicted pathways recognized in bone metabolism, ECM‐receptor interaction and proteoglycans in cancer emerged at both sites, whereas fatty acid metabolism and focal adhesion were only identified in iliac bone. Lasso analysis and cross‐validations identified sets of nine bone ncRNAs correlating strongly with adjusted total hip BMD in both femoral and iliac bone. Twenty‐eight iliac ncRNAs were associated with risk of fracture (FDR < 0.1). The small nucleolar RNAs, RNU44 and RNU48, have a function in stabilization of ribosomal RNAs (rRNAs), and their association with fracture and BMD suggest that aberrant processing of rRNAs may be involved in development of osteoporosis. Cis‐eQTL (expressed quantitative trait loci) analysis of the iliac bone biopsies identified two loci associated with microRNAs (miRNAs), one previously identified in a heel‐BMD genomewide association study (GWAS). In this comprehensive investigation of the skeletal genetic background in postmenopausal women, we identified functional bone ncRNAs associated to fracture and BMD, representing distinct subsets in WB and NWB skeletal sites.

We develop new statistical methods for analyzing sparsely sampled curves that vary in time. The typical dataset is differences in log gene expressions from case-control pairs for a large number of genes sampled relative to time of diagnosis. We focus on weak signals in the gene expression in many genes instead of strong signals in a few genes. The methods are based on moving windows in time, hypothesis testing, dimension reductions and randomization of the time to observation.

Using the time-dependent dynamics of gene expression from immune cells in blood, we aimed to explore single gene expression trajectories as biomarkers for death after a diagnosis of breast cancer introducing a new statistical method denoted Difference in Time Development Statistics (DTDS). This shows as proof of principle that the gene expression profiles from immune cells in blood differed in the postdiagnostic period are dependent on later vital status.

BLOBREC is a test for distinguishing breast-cancer patients from population-based controls described by Dumeaux et al. Previously, we performed a quality control of the methods and procedures used for developing this test, and our analyses confirmed the results obtained by Dumeaux et al. The aim of these analyses were to study the properties of the BLOBREC test in the years before or after diagnosis, and compare with time of diagnosis. In addition, we looked at effects of parity in controls, and also used a clinical stress study. We had a case-control design with 539 pairs before diagnosis, 59 at and 429 after diagnosis. In the controls taken from the NOWAC postgenome biobank, we found no difference in percentage false positives (%P) between the pre- and postdiagnostic controls (37% and 34% respectively). The %P were similar to the casecontrol study at time of diagnosis; 37%. The %P for cases were except for one year before diagnosis similar to the controls pre- and postdiagnostic. Additionally, we found a weak, non-significant increase in %P for controls with many children. The stress data originated from the “second look” at one single centre in the national screening program for breast cancer. The %P (the per cent of positive tests) were lower both for cases and controls than for the original case-control series. These data were collected under more stringent conditions. Conclusion: the BLOBREC showed higher %P for cases at diagnosis than either before or after, while the %P for controls remained identical. As previous, the %P was higher for metastatic breast cancer. BLOBREC might be improved through a more stringent sampling of both cases and controls.

Two-dimensional echocardiography (2DE) measurements of left ventricle (LV) dimensions are highly significant markers of several cardiovascular diseases. These measurements are often used in clinical care despite suffering from large variability between observers. This variability is due to the challenging nature of accurately finding the correct temporal and spatial location of measurement endpoints in ultrasound images. These images often contain fuzzy boundaries and varying reflection patterns between frames. In this work, we present a convolutional neural network (CNN) based approach to automate 2DE LV measurements. Treating the problem as a landmark detection problem, we propose a modified U-Net CNN architecture to generate heatmaps of likely coordinate locations. To improve the network performance we use anatomically meaningful heatmaps as labels and train with a multi-component loss function. Our network achieves 13.4%, 6%, and 10.8% mean percent error on intraventricular septum (IVS), LV internal dimension (LVID), and LV posterior wall (LVPW) measurements respectively. The design outperforms other networks and matches or approaches intra-analyser expert error.

We discovered an analytical error after the conference abstracts were published (Norsk Epidemiologi 2018; 28 (Supplement 1)). This error affected the results and conclusion of the study. Corrected results were presented at the conference.

The causes and molecular pathogenesis of postmenopausal osteoporosis (PMO) are far from understood even though osteoporotic fractures affect almost 50% of women above 50 years of age, resulting in e.g. femoral neck fractures imposing high risk to normal life. We addressed this issue by characterizing the global non-coding RNA (ncRNA) transcriptomes in non-weight bearing (unstressed) iliac and weight bearing (stressed) femoral postmenopausal bone across BMDs varying from normal (T-score >-1.0) to osteoporotic (T-score ≤ -2.5). The analyses were performed on transiliacal (84) and femoral trochanter (18) bone biopsies. Nearly 700 iliac bone ncRNAs were determined by a PCR based method and more than 6600 femoral ncRNAs were accessed by Affymetrix microchip analysis. BMD associations were calculated by linear regression and validated by Lasso analysis. DIANA TOOLS – mirPath v.3 software was used to identify overrepresented signaling pathways among the BMD associated transcripts at the two different sites.

Inferred effective climate sensitivity (ECSinf) is estimated using a method combining radiative forcing (RF) time series and several series of observed ocean heat content (OHC) and near-surface temperature change in a Bayesian framework using a simple energy balance model and a stochastic model. The model is updated compared to our previous analysis by using recent forcing estimates from IPCC, including OHC data for the deep ocean, and extending the time series to 2014. In our main analysis, the mean value of the estimated ECSinf is 2.0 ∘C, with a median value of 1.9 ∘C and a 90 % credible interval (CI) of 1.2–3.1 ∘C. The mean estimate has recently been shown to be consistent with the higher values for the equilibrium climate sensitivity estimated by climate models. The transient climate response (TCR) is estimated to have a mean value of 1.4 ∘C (90 % CI 0.9–2.0 ∘C), and in our main analysis the posterior aerosol effective radiative forcing is similar to the range provided by the IPCC. We show a strong sensitivity of the estimated ECSinf to the choice of a priori RF time series, excluding pre-1950 data and the treatment of OHC data. Sensitivity analysis performed by merging the upper (0–700 m) and the deep-ocean OHC or using only one OHC dataset (instead of four in the main analysis) both give an enhancement of the mean ECSinf by about 50 % from our best estimate.

Background: There is a large body of evidence demonstrating long-lasting protective effect of each full-term pregnancy (FTP) on the development of breast cancer (BC) later in life, a phenomenon that could be related to both hormonal and immunological changes during pregnancies. In this work, we studied the pregnancy-associated differences in peripheral blood gene expression profiles between healthy women and women diagnosed with BC in a prospective design. Methods: Using an integrated system epidemiology approach, we modeled BC incidence as a function of parity in the Norwegian Women and Cancer (NOWAC) cohort (165,000 women) and then tested the resulting mathematical model using gene expression profiles in blood in a nested case–control study (460 invasive case–control pairs) of women from the NOWAC postgenome cohort. Lastly, we undertook a gene set enrichment analysis for immunological gene sets. Results: A linear trend fitted the dataset precisely showing an 8% decrease in risk of BC for each FTP, independent of stratification on other risk factors and lasting for decades after a woman’s last FTP. Women with six children demonstrated 48% reduction in the incidence of BC compared to nulliparous. When we looked at gene expression, we found that 756 genes showed linear trends in cancer-free controls (false discovery rate [FDR] 5%), but this was not the case for any of the genes in BC cases. Gene set enrichment analysis of immunologic gene sets (C7 collection in Molecular Signatures Database) revealed 215 significantly enriched human gene sets (FDR 5%). Conclusion: We found marked differences in gene expression and enrichment profiles of immunologic gene sets between BC cases and healthy controls, suggesting an important protective effect of the immune system on BC risk.

A characteristic feature of primary Sjögren’s syndrome (pSS) is the destruction of salivary and lacrimal glands mediated by mononuclear cell infiltration. Adipocytes can also occupy a large portion of the salivary gland (SG) tissue area, although little is known about their significance in pSS. We have previously investigated adipose tissue infiltration in SG biopsies from pSS patients and non-SS sicca controls. Our findings indicated the distinct incidence of adipose tissue replacement in pSS patients, where adipocytes were detected in interleukin (IL) 6 rich regions. We now aimed to examine the development of adipocytes in the SG microenvironment, and delineate their possible involvement in immune reactions. A microarray analysis was performed on SG from 6 pSS patients and 6 non-SS controls, where the expression levels of genes involved in adipose tissue development, inflammatory responses, and lymphoma development were assessed. Real-time PCR was carried out on SG from 14 pSS patients and 15 non-SS controls to account for IL6, IL10, and IL17 mRNA levels. Immunohistochemical staining of frozen SG tissue using IL17 was also conducted. Our results indicate signalling pathways identified in SG of pSS patients displayed genes leading to prominent adipose tissue development and reduced mitochondrial fatty acid beta-oxidation (ARID5B, OXCT1, BDH1, SOX8, HMGCS2, FTO, ECHS1, PCCA, ACADL and ACADVL), inflammatory responses (IL1R1, IL7R, IL10RA, IL15, IL18RAP, CCL2, CCL5, CCL22, CXCR6, CD14, and CD48), and lymphoma development via JAK-STAT signalling (STAT2, TYK2, EBI3, FAS, TNFRSF1B, MAP3K8, HMOX1, LTB, TNF, STAT1, and BAK1). Genes involved in interferon production and signalling were also detected (IRF1, IRF9, and IRF7), in addition to IL6, IL10, and IL17. Higher mRNA levels of IL6, IL17 and IL10 were observed in the SG of pSS patients compared to controls. Moreover, IL17 positive cells were detected mostly interstitially in the SG and around adipocytes, also within the focal infiltrates. In conclusion, adipocyte development seems to be more prominent in the SG of pSS patients, where adipose tissue replacement is also evident. Whether this is due to disease progression, or the repair process, remains to be investigated. Detection of IL17 positive adipocytes in the target organ suggests their involvement in immune reactions.

Background: Recent large-scale undertakings such as ENCODE and Roadmap Epigenomics have generated experimental data mapped to the human reference genome (as genomic tracks) representing a variety of functional elements across a large number of cell types. Despite the high potential value of these publicly available data for a broad variety of investigations, little attention has been given to the analytical methodology necessary for their widespread utilisation. Findings: We here present a first principled treatment of the analysis of collections of genomic tracks. We have developed novel computational and statistical methodology to permit comparative and confirmatory analyses across multiple and disparate data sources. We delineate a set of generic questions that are useful across a broad range of investigations and discuss the implications of choosing different statistical measures and null models. Examples include contrasting analyses across different tissues or diseases. The methodology has been implemented in a comprehensive open-source software system, the GSuite HyperBrowser. To make the functionality accessible to biologists, and to facilitate reproducible analysis, we have also developed a web-based interface providing an expertly guided and customizable way of utilizing the methodology. With this system, many novel biological questions can flexibly be posed and rapidly answered. Conclusions: Through a combination of streamlined data acquisition, interoperable representation of dataset collections and customizable statistical analysis with guided setup and interpretation, the GSuite HyperBrowser represents a first comprehensive solution for integrative analysis of track collections across the genome and epigenome. The software is available at: https://hyperbrowser.uio.no

Background: Functional genomics in a processual analysis cover the time-dependent changes in transcriptomics and epigenetics before diagnosis of a disease, reflecting the changes in both life style and disease processes. The aim of this paper is to explore the dynamic, time-dependent mechanisms of the metastatic processes, using blood transcriptomics and including time in a continuous manner. For achieving this goal, we have developed new statistical methods based on statistics that are local in time. Methods: The new statistical method, Local in Time Statistics (LITS), is based on calculating statistics in moving windows and randomization. The method has been tested for the analysis of a dataset that collectively provides information on the blood transcriptome up to 8 years before breast cancer diagnosis. The dataset from the Norwegian Women and Cancer (NOWAC) Post-genome Cohort consists of 467 case-control pairs matched on birth year and time of blood sampling. The data for a pair are the difference in log2 gene expression between the case and control. The stratified analyses are based on important biological differences like metastatic versus non-metastatic cancer, and the mode of cancer detection, ie, screening-detected cancers versus clinically detected cancers. The dataset was used for examining whether the gene expression profile varies between cases and controls, with time, or between cases with and without metastases. Results: The null hypotheses of no differences between cases and controls, no time-dependent changes, and no differences between different strata were all rejected. For screening-detected cancers, the probability of correct prediction of metastasis status was best in year 1 before diagnosis compared to year 3 and 4 before diagnosis for clinically detected cancers. The predictor was not very sensitive to the number of genes included. Conclusion: Using a new statistical method, LITS, we have demonstrated time-dependent changes of the blood transcriptome up to 8 years before breast cancer diagnosis.

Background The understanding of changes in temporal processes related to human carcinogenesis is limited. One approach for prospective functional genomic studies is to compile trajectories of differential expression of genes, based on measurements from many case-control pairs. We propose a new statistical method that does not assume any parametric shape for the gene trajectories. Methods The trajectory of a gene is defined as the curve representing the changes in gene expression levels in the blood as a function of time to cancer diagnosis. In a nested case–control design it consists of differences in gene expression levels between cases and controls. Genes can be grouped into curve groups, each curve group corresponding to genes with a similar development over time. The proposed new statistical approach is based on a set of hypothesis testing that can determine whether or not there is development in gene expression levels over time, and whether this development varies among different strata. Curve group analysis may reveal significant differences in gene expression levels over time among the different strata considered. This new method was applied as a “proof of concept” to breast cancer in the Norwegian Women and Cancer (NOWAC) postgenome cohort, using blood samples collected prospectively that were specifically preserved for transcriptomic analyses (PAX tube). Cohort members diagnosed with invasive breast cancer through 2009 were identified through linkage to the Cancer Registry of Norway, and for each case a random control from the postgenome cohort was also selected, matched by birth year and time of blood sampling, to create a case-control pair. After exclusions, 441 case-control pairs were available for analyses, in which we considered strata of lymph node status at time of diagnosis and time of diagnosis with respect to breast cancer screening visits. Results The development of gene expression levels in the NOWAC postgenome cohort varied in the last years before breast cancer diagnosis, and this development differed by lymph node status and participation in the Norwegian Breast Cancer Screening Program. The differences among the investigated strata appeared larger in the year before breast cancer diagnosis compared to earlier years. Conclusions This approach shows good properties in term of statistical power and type 1 error under minimal assumptions. When applied to a real data set it was able to discriminate between groups of genes with non-linear similar patterns before diagnosis.

The analyses in this note are based on a dataset with gene expression in blood before diagnosis of ovarian cancer. The dataset consists of case‐control pairs that are matched on birth year and time of blood sampling, and the data for a pair is the log2 difference in gene expression between the case and control. For each case‐ control pair the gene expression is measured once before diagnosis. As the blood samples of the different case‐controls pairs are measured at different points in time before diagnosis, we have used the dataset for examining whether the gene expression profile varies with time. We have also used the dataset for examining whether the gene expression profile varies between cases and controls, or between cases with and without spread (metastases), and for predicting whether a case has ovarian cancer with or without spread. We have used and adapted a method based on hypothesis testing using randomization, that is able to identify small changes that are varying slowly in time and/or among strata, by using a large number of genes in each hypothesis test and predictor. Even though the signals in the data are weak, we concluded that the gene expression profile varies in time, between cases and controls and between cases with and without spread (metastases). The results indicated that there is an increasing variation in the gene expression profiles when approaching the time of diagnosis, while the gene expression profiles far from diagnosis are more stable. The dataset is quite small, with only 59 case‐control pairs with spread and 28 without spread that are distributed over a seven year period before diagnosis. We can therefore not draw any firm conclusion about whether the predictive power of the method used for predicting the metastasis status of the cases is sufficiently good (p‐value 0.28, Fisher’s test). The best predictive power was observed in a two‐year period around year 5 before diagnosis (p‐value 0.12, Fisher’s test).

Background: Marine long-chain polyunsaturated fatty acids are susceptible to oxidation, generating a range of different oxidation products with suggested negative health effects. The aim of the present study was to utilize sensitive high-throughput transcriptome analyses to investigate potential unfavorable effects of oxidized fish oil (PV: 18 meq/kg; AV: 9) compared to high-quality fish oil (PV: 4 meq/kg; AV: 3). Methods: In a double-blinded randomized controlled study for seven weeks, 35 healthy subjects were assigned to 8 g of either oxidized fish oil or high quality fish oil. The daily dose of EPA+DHA was 1.6 g. Peripheral blood mononuclear cells were isolated at baseline and after 7 weeks and transcriptome analyses were performed with the illuminaHT-12 v4 Expression BeadChip. Results: No gene transcripts, biological processes, pathway or network were significantly changed in the oxidized fish oil group compared to the fish oil group. Furthermore, gene sets related to oxidative stress and cardiovascular disease were not differently regulated between the groups. Within group analyses revealed a more prominent effect after intake of high quality fish oil as 11 gene transcripts were significantly (FDR < 0.1) changed from baseline versus three within the oxidized fish oil group. Conclusion: The suggested concern linking lipid oxidation products to short-term unfavorable health effects may therefore not be evident at a molecular level in this explorative study. Trial registration: ClinicalTrials.gov, NCT01034423

The analyses in this note are based on a dataset with gene expression in blood before diagnosis of breast cancer. The dataset consists of case‐control pairs that are matched on birth year and time of blood sampling, and the data for a pair is the log2 difference in gene expression between the case and control. For each case‐ control pair the gene expression is measured once before diagnosis. As the blood samples of the different case‐controls pairs are measured at different points in time before diagnosis, we have used the dataset for examining whether the gene expression profile varies with time. We have also used the dataset for examining whether the gene expression profile varies between cases and controls, or between cases with and without spread (metastases), and for predicting whether a case has breast cancer with or without spread. The dataset consists of two subdatasets, one where the cases participated in the screening program (the screening group) and one where for cases did not participate in the screening program (the clinical group). All analyses have been performed separately for these two subdatasets. We have used and adapted a method based on hypothesis testing using randomization, that is able to identify small changes that are varying slowly in time and/or among strata, by using a large number of genes in each hypothesis test and predictor. Even though the signals in the data are weak, we concluded that the gene expression profile varies in time, between cases and controls and between cases with and without spread (metastases). The dataset is quite small, with only 108 (30) case‐control pairs with spread and 272 (57) without spread in the screening (clinical) group, that are distributed over an eight year period before diagnosis. We can therefore not draw any firm conclusion about whether the predictive power of the method used for predicting the metastasis status of the cases is sufficiently good. In the screening group we obtained p‐value 0.5 for the entire period but 0.03 for the last year before diagnosis. For the clinical group the p‐value for the entire period was 0.05. Here the results indicated best prediction 3‐4 years before diagnosis. The p‐value is equal 0.05 in this time period but this may be due to a small data set).

Purpose: Does lumbar fusion lead to accelerated adjacent segment disc degeneration (ASDD) or is it explained by genetics and aging? The influence of genetics on ASDD remains to be explored. This study assesses whether the disc space height adjacent to a fused segment is associated with candidate gene single nucleotide polymorphisms (SNPs). Methods: Patients with low back pain from four RCTs (N = 208 fusion; 77 non-operative treatment) underwent standing plain radiography and genetic analyses at 13 ± 4 years follow-up. Disc space height was measured using a validated computer-assisted distortion-compensated roentgen analysis technique and reported in standard deviations from normal values. Genetic association analyses included 34 SNPs in 25 structural, inflammatory, matrix degrading, apoptotic, vitamin D receptor and OA-related genes relevant to disc degeneration. These were analysed for their association with disc space height (after adjusting for age, gender, smoking, duration of follow-up and treatment group) first, separately, and then together in a stepwise multivariable model. Results: Two SNPs from the IL18RAP gene (rs1420106 and rs917997) were each associated with a lower disc space height at the adjacent level (B = −0.34, p = 0.04 and B = −0.35, p = 0.04, respectively) and the MMP-9 gene SNP rs20544 was associated with a greater disc space height (B = 0.35, p = 0.04). Age (p < 0.001) and fusion (p < 0.008) were also significant variables in each analysis. The total explained variance in disc space height was for each SNP model 13–14 %, with 11–12 % of this being accounted for by the given SNP, 64–67 % by age and 19–22 % by fusion. In the multivariable regression analysis (with nine SNPs selected for entry, along with the covariates) the total explained variance in disc space height was 23 %, with the nine SNPs, age and fusion accounting for 45, 45 and 7 % of this, respectively. Conclusions: Age was the most significant determinant of adjacent segment disc space height followed by genetic factors, specifically inflammatory genes. Fusion explained a statistically significant but small proportion of the total variance. Much of the variance remained to be explained.

Systemic inflammation like in sepsis is still lacking specific diagnostic markers and effective therapeutics. The first line of defense against intruding pathogens and endogenous damage signals is pattern recognition by e.g., complement and Toll-like receptors (TLR). Combined inhibition of a key complement component (C3 and C5) and TLR-co-receptor CD14 has been shown to attenuate certain systemic inflammatory responses. Using DNA microarray and gene annotation analyses, we aimed to decipher the effect of combined inhibition of C3 and CD14 on the transcriptional response to bacterial challenge in human whole blood. Importantly, combined inhibition reversed the transcriptional changes of 70% of the 2335 genes which significantly responded to heat-inactivated Escherichia coli by on average 80%. Single inhibition was less efficient (p<0.001) but revealed a suppressive effect of C3 on 21% of the responding genes which was partially counteracted by CD14. Furthermore, CD14 dependency of the Escherichia coli-induced response was increased in C5-deficient compared to C5-sufficient blood. The observed crucial distinct and synergistic roles for complement and CD14 on the transcriptional level correspond to their broad impact on the inflammatory response in human blood, and their combined inhibition may become inevitable in the early treatment of acute systemic inflammation.

Document collections resulting from searches in the biomedical literature, for instance, in PubMed, are often so large that some organization of the returned information is necessary. Clustering is an efficient tool for organizing search results. To help the user to decide how to continue the search for relevant documents, the content of each cluster can be characterized by a set of representative keywords or cluster labels. As different users may have different interests, it can be desirable with solutions that make it possible to produce labels from a selection of different topical categories. We therefore introduce the concept of multi-focus cluster labeling to give users the possibility to get an overview of the contents through labels from multiple viewpoints. The concept for multi-focus cluster labeling has been established and has been demonstrated on three different document collections. We illustrate that multi-focus visualizations can give an overview of clusters along axes that general labels are not able to convey. The approach is generic and should be applicable to any biomedical (or other) domain with any selection of foci where appropriate focus vocabularies can be established. A user evaluation also indicates that such a multi-focus concept is useful.

Non-sterile pathogen-induced sepsis and sterile inflammation like in trauma or ischemia–reperfusion injury may both coincide with the life threatening systemic inflammatory response syndrome and multi-organ failure. Consequently, there is an urgent need for specific biomarkers in order to distinguish sepsis from sterile conditions. The overall aim of this study was to uncover putative sepsis biomarkers and biomarker pathways, as well as to test the efficacy of combined inhibition of innate immunity key players complement and Toll-like receptor co-receptor CD14 as a possible therapeutic regimen for sepsis. We performed whole blood gene expression analyses using microarray in order to profile Gram-negative bacteria-induced in- flammatory responses in an ex vivo human whole blood model. The experiments were performed in the presence or absence of inhibitors of complement proteins (C3 and CD88 (C5a receptor 1)) and CD14, alone or in combination. In addition, we used blood from a C5-deficient donor. Anti-coagulated whole blood was challenged with heat-inactivated Escherichia coli for 2 h, total RNA was isolated and microarray analyses were performed on the Affymetrix GeneChip Gene 1.0 ST Array platform. The initial experiments were performed in duplicates using blood from two healthy donors. C5-deficiency is very rare, and only one donor could be recruited. In order to increase statistical power, a technical replicate of the C5- deficient samples was run. Subsequently, log2-transformed intensities were processed by robust multichip analysis and filtered using a threshold of four. In total, 73 microarray chips were run and analyzed. The normalized and filtered raw data have been deposited in NCBI's Gene Expression Omnibus (GEO) and are accessible with GEO Series accession number GSE55537. Linear models for microarray data were applied to estimate fold changes between data sets and the respective multiple testing adjusted p-values (FDR qvalues). The interpretation of the data has been published by Lau et al. in an open access article entitled “CD14 and Complement Crosstalk and Largely Mediate the Transcriptional Response to Escherichia coli in Human Whole Blood as revealed by DNA Microarray” (Lau et al., 2015).

BLOBREC is a test for distinguishing breast‐cancer patients from population‐based controls described by Dumeaux et al. We have performed a quality control of the methods and procedures used for developing this test. Besides reproducing results obtained using exactly the same datasets as Dumeaux et al., we examined how sensitive the test results are to the approach selected when preprocessing the data, and whether the test results are influenced by drug use, smoking, or stress due to a potential cancer diagnosis. We also examined if the test results for the breast‐cancer patients depend on whether the patient participated in the screening program. A data et intended for examining the effect of stress was used as a validation dataset for the test. Our analyses confirm the results obtained by Dumeaux et al. We obtain comparable results when using different approaches for preprocessing the data. Prediction performance for the datasets used when developing the test is clearly better than for the validation dataset. Batch effects and other differences between the datasets are the most likely explanations for this difference. However, the validation dataset consists of many different subgroups of individuals with a limited number of individuals in each subgroup, making the interpretation uncertain. We were not able to show that the test is influenced by stress, drug use or smoking, but again, the datasets are too small to draw any firm conclusions.

Purpose: To examine the association between COMT and OPRM1 gene polymorphisms and pain and disability at baseline and long-term follow-up in patients treated for chronic low back pain (LBP). Methods: 371 of 767 unrelated European patients recruited from four randomised trials underwent genetic analyses at mean 11.4 years follow-up. 274 patients had fusion and 97 had non-operative treatment. Association analyses included disability, pain, five single nucleotide polymorphisms (SNPs) in the COMT gene, and one SNP in the OPRM1 gene. Analyses were adjusted for age, gender, smoking, analgesics and treatment. Results: Disability at baseline was significantly associated with COMT SNPs rs4818 (p = 0.02), rs6269 (p = 0.007), rs4633 (p = 0.04) rs2075507 (p = 0.009), two haplotypes (p < 0.002), age, gender and smoking (p ≤ 0.002). No significant associations with clinical variables were observed for OPRM1, or for COMT at long-term follow-up. Conclusions: Results suggest that genetic factors are partly responsible for the variation in disability levels in patients presenting with chronic LBP being considered for surgery; in contrast, genetics has no influence on the long-term outcome of treatment.

In this study we evaluate the potential of local binary descriptors for automatic sorting in an industrial context. This problem is different from that of retrieval for human handling as we need to identify the one correct class, rather than finding all the similar classes. We have looked at classes of objects that need to be identified by their cover or label, rather than their shape. Challenges for this application are that the process needs to be very fast and the approach must be able to distinguish between a large number of classes, where the classes can be quite similar and have identical elements. We have studied various combinations of detectors and binary descriptors in combination with approximate nearest neighbor (ANN) searches in such contexts. Our conclusion is that these approaches are well suited for this type of automatic sorting, and our experiments show that for the best performing combinations we are able to obtain a 99% recognition rate on a database of 80,000 images using an average of less than 0.5 seconds per image.

Background Fish oil supplementation has been shown to alter gene expression of mononuclear cells both in vitro and in vivo. However, little is known about the total transcriptome profile in healthy subjects after intake of fish oil. We therefore investigated the gene expression profile in peripheral blood mononuclear cells (PBMCs) after intake of fish oil for 7 weeks using transcriptome analyses. Design In a 7-week, double-blinded, randomized, controlled, parallel-group study, healthy subjects received 8 g day−1 fish oil (1.6 g day−1 eicosapentaenoic acid + docosahexaenoic acid) (n = 17) or 8 g day−1 high oleic sunflower oil (n = 19). Microarray analyses of RNA isolated from PBMCs were performed at baseline and after 7 weeks of intervention. Results Cell cycle, DNA packaging and chromosome organization are biological processes found to be upregulated after intake of fish oil compared to high oleic sunflower oil using a moderated t-test. In addition, gene set enrichment analysis identified several enriched gene sets after intake of fish oil. The genes contributing to the significantly different gene sets in the subjects given fish oil compared with the control group are involved in cell cycle, endoplasmic reticulum (ER) stress and apoptosis. Gene transcripts with common motifs for 35 known transcription factors including E2F, TP53 and ATF4 were upregulated after intake of fish oil. Conclusion We have shown that intake of fish oil for 7 weeks modulates gene expression in PBMCs of healthy subjects. The increased expression of genes related to cell cycle, ER stress and apoptosis suggests that intake of fish oil may modulate basic cellular processes involved in normal cellular function.

This note is a working document and is based on work in progress. The note describes methods for and results of analyzing gene expression data related to breast cancer diagnosis. The hypothesis is that the genes related to the stages of cancer development could be differentially expressed over time, perhaps in a small but consistent manner. We have started developing methods for testing whether there is such a development in time, and for identifying groups of genes with similar behavior, or functional form, the last years before diagnosis. Hence, we are looking for weak signals from a large number of genes in contrast to stronger signals from a few genes. We have also proposed a method for using information from such groups of genes for predicting whether a case has breast cancer with or without spread. From the preliminary results described in this note we conclude that it is important to normalize the data before further analysis. However, normalizing the data may also remove trends we are looking for, and we have observed that the results presented are sensitive to choice of normalization method. Therefore different normalization methods should be tested and evaluated to decide which method is best suited for our dataset. This is outside the scope of this note and should be further examined in later work. The dataset consists of log2-transformed gene expression values in blood cells related to breast cancer. The developed methods have been tested on several version of this dataset as the data are continuously updated when new information becomes available (for example when new individuals are diagnosed with cancer or the quality of the data is improved) and because different subsets of the dataset have been selected dependent of what information we wanted to include in the analyses. This, and slightly different choices in the preprocessing steps, resulted in different subsets of genes selected for the different versions of the dataset. We have observed that the results are sensitive to the subset of genes selected. Later this will be further examined to find the procedure for selecting genes to be included in the statistical analyses that is best suited for our dataset. For some of the preliminary analyses we conclude that there is a significantly high number of genes that increase or decrease monotonically in gene expression the years before diagnosis in the stratum where we a priori expect it is most likely to observe a signal. We expect a more homogeneous dataset for persons participating in a screening program and expect a stronger signal from patients with spread. However, the signal is still weak. Using information from the identified groups of genes when predicting spread or not spread, we were able to identify about 1/3 of the cases without spread and no or few false negatives. The preliminary methods will be further developed later, and they will also be tested on a dataset with improved quality where more optimal preprocessing procedures and normalization methods are used.

Equilibrium climate sensitivity (ECS) is constrained based on observed near-surface temperature change, changes in ocean heat content (OHC) and detailed radiative forcing (RF) time series from pre-industrial times to 2010 for all main anthropogenic and natural forcing mechanism. The RF time series are linked to the observations of OHC and temperature change through an energy balance model (EBM) and a stochastic model, using a Bayesian approach to estimate the ECS and other unknown parameters from the data. For the net anthropogenic RF the posterior mean in 2010 is 2.0 Wm−2, with a 90% credible interval (C.I.) of 1.3 to 2.8 Wm−2, excluding present-day total aerosol effects (direct + indirect) stronger than −1.7 Wm−2. The posterior mean of the ECS is 1.8 °C, with 90% C.I. ranging from 0.9 to 3.2 °C, which is tighter than most previously published estimates. We find that using three OHC data sets simultaneously and data for global mean temperature and OHC up to 2010 substantially narrows the range in ECS compared to using less updated data and only one OHC data set. Using only one OHC set and data up to 2000 can produce comparable results as previously published estimates using observations in the 20th century, including the heavy tail in the probability function. The analyses show a significant contribution of internal variability on a multi-decadal scale to the global mean temperature change. If we do not explicitly account for long-term internal variability, the 90% C.I. is 40% narrower than in the main analysis and the mean ECS becomes slightly lower, which demonstrates that the uncertainty in ECS may be severely underestimated if the method is too simple. In addition to the uncertainties represented through the estimated probability density functions, there may be uncertainties due to limitations in the treatment of the temporal development in RF and structural uncertainties in the EBM.

Identification of three-dimensional (3D) interactions between regulatory elements across the genome is crucial to unravel the complex regulatory machinery that orchestrates proliferation and differentiation of cells. ChIA-PET is a novel method to identify such interactions, where physical contacts between regions bound by a specific protein are quantified using next-generation sequencing. However, determining the significance of the observed interaction frequencies in such datasets is challenging, and few methods have been proposed. Despite the fact that regions that are close in linear genomic distance have a much higher tendency to interact by chance, no methods to date are capable of taking such dependency into account. Here, we propose a statistical model taking into account the genomic distance relationship, as well as the general propensity of anchors to be involved in contacts overall. Using both real and simulated data, we show that the previously proposed statistical test, based on Fisher's exact test, leads to invalid results when data are dependent on genomic distance. We also evaluate our method on previously validated cell-line specific and constitutive 3D interactions, and show that relevant interactions are significant, while avoiding over-estimating the significance of short nearby interactions.

The work is performed in close cooperation with the University of Tromsø and professor Eiliv Lund and is financed by the ERC TICE project. This note describes the preprocessing steps of gene expression data and focuses particularly on the filtering and normalization steps as the choices made here greatly affects the set of probes used in later analyses. In the filtering step, two parameters are set. Firstly, a cut-off for the detection p-value for each probe is set, and a probe is present in a given sample if its detection p-value is smaller than this cut-off. Secondly, the present limit is set. It is used to decide in how many samples a probe has to be present in order to be included in the dataset. The results show that a p-value cut-off at 0.01 and a present limit at 0.01 are reasonable choices. After filtering, the data can be normalized. Four different approaches are evaluated, and for the available dataset, quantile normalization of the data on original scale gives the most stable results.

The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.

Colorectal cancer (CRC) is one of the leading causes of cancer related deaths and the search for prognostic biomarkers that might improve treatment decisions is warranted. MicroRNAs (miRNAs) are short non-coding RNA molecules involved in regulating gene expression and have been proposed as possible biomarkers in CRC. In order to characterize the miRNA transcriptome, a large cohort including 88 CRC tumors with long-term follow-up was deep sequenced. 523 mature miRNAs were expressed in our cohort, and they exhibited largely uniform expression patterns across tumor samples. Few associations were found between clinical parameters and miRNA expression, among them, low expression of miR-592 and high expression of miR-10b-5p and miR-615-3p were associated with tumors located in the right colon relative to the left colon and rectum. High expression of miR-615-3p was also associated with poorly differentiated tumors. No prognostic biomarker candidates for overall and metastasis-free survival were identified by applying the LASSO method in a Cox proportional hazards model or univariate Cox. Examination of the five most abundantly expressed miRNAs in the cohort (miR-10a-5p, miR-21-5p, miR-22-3p, miR-143-3p and miR-192-5p) revealed that their collective expression represented 54% of the detected miRNA sequences. Pathway analysis of the target genes regulated by the five most highly expressed miRNAs uncovered a significant number of genes involved in the CRC pathway, including APC, TGFβ and PI3K, thus suggesting that these miRNAs are relevant in CRC.

Background: Inflammatory and matrix degrading gene variants have been reported to be associated with disc degeneration. Some of these variants also modulate peripheral pain. This study examines the association of these genetic variants with radiographic lumbar disc degeneration and changes in pain and disability at long-term after surgical and cognitive behavioural management. Methods: 93 unrelated patients with chronic low back pain (CLBP) for duration of >1 year and lumbar disc degeneration were treated with lumbar fusion or cognitive intervention and exercises. Standardised questionnaires included the Oswestry Disability Index (ODI) and Visual Analog Score (VAS) for CLBP, were filled in by patients both at baseline and at 9 years follow-up. Degenerative changes at baseline Magnetic Resonance Imaging and Computed Tomography scans, were graded as moderate and severe (N=79). Yield and quality of blood and saliva DNA was assessed by nano drop spectrophotometry. Eight SNPs in 5 inflammatory and matrix degrading genes were successfully genotyped. Single marker and haplotype association with severity of degeneration, number of discs involved, changes in ODI and VAS CLBP, was done using Haploview, linear regression and R-package Haplostats. Results: Association analysis of individual SNPs revealed association of IL18RAP polymorphism rs1420100 with severe degeneration (p = 0.05) and more than one degenerated disc (p = 0.02). From the same gene two SNPs, rs917997 and rs1420106, were found to be in strong linkage disequilibrium (LD) and were associated with post treatment improvement in disability (p = 0.02). Haplotype association analysis of 5 SNPs spanning across IL18RAP, IL18R1 and IL1A genes revealed significant associations with improvement in disability (p=0.02) and reduction in pain (p=0.04). An association was found between MMP3 polymorphism rs72520913 and improvement in pain (p = 0.03) and with severe degeneration (p = 0.006). Conclusions: The findings of the current study suggest a role of variation at inflammatory and matrix degrading genes with severity of lumbar disc degeneration, pain and disability.

The pathogenetic role, including its target genes, of the recurrent 3p12–p14 loss in cervical cancer has remained unclear. To determine the onset of the event during carcinogenesis, we used microarray techniques and found that the loss was the most frequent 3p event, occurring in 61% of 92 invasive carcinomas, in only 2% of 43 high-grade intraepithelial lesions (CIN2/3), and in 33% of 6 CIN3 lesions adjacent to invasive carcinomas, suggesting a role in acquisition of invasiveness or early during the invasive phase. We performed an integrative DNA copy number and expression analysis of 77 invasive carcinomas, where all genes within the recurrent region were included. We selected eight genes, THOC7, PSMD6, SLC25A26, TMF1, RYBP, SHQ1, EBLN2, and GBE1, which were highly down-regulated in cases with loss, as confirmed at the protein level for RYBP and TMF1 by immunohistochemistry. The eight genes were subjected to network analysis based on the expression profiles, revealing interaction partners of proteins encoded by the genes that were coordinately regulated in tumours with loss. Several partners were shared among the eight genes, indicating crosstalk in their signalling. Gene ontology analysis showed enrichment of biological processes such as apoptosis, proliferation, and stress response in the network and suggested a relationship between down-regulation of the eight genes and activation of tumourigenic pathways. Survival analysis showed prognostic impact of the eight-gene signature that was confirmed in a validation cohort of 74 patients and was independent of clinical parameters. These results support the role of the eight candidate genes as targets of the 3p12–p14 loss in cervical cancer and suggest that the strong selection advantage of the loss during carcinogenesis might be caused by a synergetic effect of several tumourigenic processes controlled by these targets.

Predictions of climate change are uncertain mainly because of uncertainties in the emissions of greenhouse gases and how sensitive the climate is to changes in the abundance of the atmospheric constituents. The equilibrium climate sensitivity is defined as the temperature increase because of a doubling of the CO2 concentration in the atmosphere when the climate reaches a new steady state. CO2 is only one out of the several external factors that affect the global temperature, called radiative forcing mechanisms as a collective term. In this paper, we present a model framework for estimating the climate sensitivity. The core of the model is a simple, deterministic climate model based on elementary physical laws such as energy balance. It models yearly hemispheric surface temperature and global ocean heat content as a function of historical radiative forcing. This deterministic model is combined with an empirical, stochastic model and fitted to observations on global temperature and ocean heat content, conditioned on estimates of historical radiative forcing. We use a Bayesian framework, with informative priors on a subset of the parameters and flat priors on the climate sensitivity and the remaining parameters. The model is estimated by Markov Chain Monte Carlo techniques

Background Treatment outcome of low back pain (LBP) is associated with inter-individual variations in pain relief and functional disability. Genetic variants of catechol-O-methyltransferase (COMT) gene have previously been shown to be associated with pain sensitivity and pain medication. This study examines the association between COMT polymorphisms and 7–11 year change in Oswestry Disability Index (ODI) and Visual Analog Score (VAS) for LBP as clinical outcome variables in patients treated with surgical instrumented lumbar fusion or cognitive intervention and exercise. Methods 93 unrelated patients with chronic LBP for duration of >1 year and lumbar disc degeneration (LDD) were treated with lumbar fusion (N = 60) or cognitive therapy and exercises (N = 33). Standardised questionnaires assessing the ODI, VAS LBP, psychological factors and use of analgesics, were answered by patients both at baseline and at 7–11 years follow-up. Four SNPs in the COMT gene were successfully genotyped. Single marker as well as haplotype association with change in ODI and VAS LBP, were analyzed using Haploview, linear regression and R-package Haplostats. P-values were not formally corrected for multiple testing as this was an explorative study. Results Association analysis of individual SNPs adjusted for covariates revealed association of rs4633 and rs4680 with post treatment improvement in VAS LBP (p = 0.02, mean difference (β) = 13.5 and p = 0.02, β = 14.2 respectively). SNPs, rs4633 and rs4680 were found to be genotypically similar and in strong linkage disequilibrium (LD). A significant association was found with covariates, analgesics (p = 0.001, β = 18.6); anxiety and depression (p = 0.008, β = 15.4) and age (p = 0.03, mean difference per year (β) = 0.7) at follow-up. There was a tendency for better improvement among heterozygous patients compared to the homozygous. No association was observed for the analysis of the common haplotypes, these SNPs were situated on. Conclusions Results suggest an influence of genetic variants of COMT gene in describing the variation in pain after treatment for low back pain. Replication in large samples with testing for other pain related genes is warranted.

Background We previously observed that a radiotherapy-induced biochemical response in plasma was associated with favourable outcome in head and neck squamous carcinoma cancer (HNSCC) patients. The aim of the present study was to compare stress associated blood cell gene expression between two sub-groups of HNSCC patients with different biochemical responses to radiotherapy. Methods Out of 87 patients (histologically verified), 10 biochemical ‘responders’ having a high relative increase in plasma oxidative damage and a concomitant decrease in plasma antioxidants during radiotherapy and 10 ‘poor-responders’ were selected for gene-expression analysis and compared using gene set enrichment analysis. Results There was a significant induction of stress-relevant gene-sets in the responders following radiotherapy compared to the poor-responders. The relevance of the involvement of similar stress associated gene expression for HNSCC cancer and radioresistance was verified using two publicly available data sets of 42 HNSCC cases and 14 controls (GEO GSE6791), and radiation resistant and radiation sensitive HNSCC xenografts (E-GEOD-9716). Conclusions Radiotherapy induces a systemic stress response, as revealed by induction of stress relevant gene expression in blood cells, which is associated to favourable outcome in a cohort of 87 HNSCC patients. Whether these changes in gene expression reflects a systemic effect or are biomarkers of the tumour micro-environmental status needs further study.

Knowledge of the molecular background of functional magnetic resonance (MR) images is required to fully exploit their potential in cancer management. We explored the prognostic impact of dynamic contrast-enhanced MR imaging (DCE-MRI) parameters in cervical cancer combined with global gene expression data to reveal their underlying molecular phenotype and construct a representative gene signature for the relevant parameter. On the basis of 78 patients with cervical cancer subjected to curative chemoradiotherapy, we identified the prognostic DCE-MRI parameter ABrix by pharmacokinetic analysis of pretreatment images based on the Brix model, in which tumors with low ABrix appeared to be most aggressive. Gene set analysis of 46 tumors with pairwise DCE-MRI and gene expression data showed a significant correlation between ABrix and the hypoxia gene sets, whereas gene sets related to other tumor phenotypes were not significant. Hypoxia gene sets specific for cervical cancer created in cell culture experiments, including both targets of the hypoxia inducible factor (HIF1α) and the unfolded protein response, were the most significant. In the remaining 32 tumors, low ABrix was associated with upregulation of HIF1α protein expression, as assessed by immunohistochemistry, consistent with increased hypoxia. On the basis of the hypoxia gene sets, a signature of 31 genes that were upregulated in tumors with low ABrix was constructed. This DCE-MRI hypoxia gene signature showed prognostic impact in an independent validation cohort of 109 patients. Our findings reveal the molecular basis of an aggressive hypoxic phenotype and suggest the use of DCE-MRI to noninvasively identify patients with hypoxia-related chemoradioresistance.

Hensikten med denne pilotstudien har vært å vurdere hvordan og i hvilken grad verktøy basert på automatisk bildeanalyse kan være til hjelp ved aldersvurderinger med tanke på å oppnå mer repeterbare, objektive og kvantitative målinger. Hovedfokus har vært på tannrøntgen. Gjennom studien har vi vist at det bør være mulig å utvikle bildeanalysealgoritmer som gir en beskrivelse av tannen som gjør det mulig å trekke ut mål som kan ha en sammenheng med tannens utvikling. Dagens manuelle tilnærming for aldersvurdering er basert på tannlegenes visuelle analyse og sammenligning med beskrivelser og maler for utviklingsstadier. En slik direkte bruk av maler tror vi er vanskelig å kombinere med bildeanalyse. Gitt at en kan finne gode nok mål, har imidlertid en framgangsmåte basert på mål flere fordeler ved at den gir kvantitative og kontinuerlige observasjoner med bedre mulighet for å kvantifisere usikkerheten og mindre tap av informasjon på veien. Vi har sett på noen ulike klasser av bildebaserte mål som kan la seg bestemme automatisk ved hjelp av bildeanalyse; lengdemål, breddemål og arealmål. De arealbaserte målene representerer her noe nytt i forhold til tidligere mål som i hovedsak har vært basert på avstander. Bildeanalysealgoritmene er testet ut og målene beregnet for et lite datasett. På det samme datasettet er det også gjort en manuell gradering i stadier av to uavhengige observatører, der den manuelle graderingen viser stor interobservatørvariasjon. Resultatene av bildeanalysen ser lovende ut. Av målene ser de arealbaserte spesielt interessante ut, og resultatene kan indikere et bra sammenfall med alder. For å verifisere disse sammenhengene er det likevel nødvendig med studier av mye større datasett for å avgjøre at sammenfall mellom alder og mål ikke er tilfeldig. Det er også nødvendig med videreutvikling av de foreslåtte algoritmene og uttesting på et større datasett for å avgjøre om de vil være robuste nok for bruk i praktisk aldersvurdering.