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The MIR-15A/-16-1 tumor suppressor microRNAs (miRNAs) are deleted in leukemic cells from more than 50% of patients with chronic lymphocytic leukemia (CLL). As these miRNAs are also less abundant in patients without genomic deletion, their downregulation in CLL is likely to be caused by additional mechanisms. We found the primary transcripts (pri-miRNAs) of MIR-15a/-16/-15b to be elevated and processing intermediates (precursor miRNAs) to be reduced in cells from CLL patients (22/38) compared with non-malignant B-cells (n=14), indicating a block of miRNA maturation at the DROSHA processing step. Using a luciferase reporter assay for pri-miR processing we validated the defect in primary CLL cells. The block of miRNA maturation is restricted to specific miRNAs and can be found in the cell line MEC-2, but not in MEC-1, even though both are derived from the same CLL patient. In these cells, the RNA-specific deaminase ADARB1 leads to reduced pri-miRNA processing, but full processing efficiency is recovered upon deletion of the RNA-binding domains or nuclear localization of ADARB1. Thus, we show that, apart from genomic deletion or transcriptional downregulation, aberrant processing of miRNA leads to specific reduction of miRNAs in leukemic cells. This represents a novel oncogenic mechanism in the pathogenesis of CLL. [ABSTRACT FROM AUTHOR]

Chemoprotection of haematopoietic stem cells (HSCs) by gene therapeutic transfer of drug-resistance genes represents the encouraging approach to prevent myelosuppression, which is one of the most severe side effects in tumor therapy. Thus, we cloned and evaluated six different bicistronic lentiviral SIN vectors encoding two transgenes, MGMTP140K (an O6-benzylguanine-resistant mutant of methylguanine-DNA methyltransferase) and MDR1 (multidrug resistance 1), using various linker sequences (IRESEMCV, IRESFMDV and 2A-element of FMDV (F2A)). Expression of both transgenes in HL-60 and in K562 cells was assayed by quantitative real-time PCR. Combination therapy with ACNU plus paclitaxel in HL-60 cells and with carmustin (BCNU) plus doxorubicin in K562 cells resulted in the most significant survival advantage of cells transduced with the lentiviral vector HR'SIN-MGMTP140K-F2A-MDR1 compared with untransduced cells. In human HSCs, overexpression of both transgenes by this vector also caused significantly increased survival and enrichment of transduced cells after treatment with BCNU plus doxorubicin or temozolomide plus paclitaxel. In summary, we could show significant chemoprotection by overexpression of MDR1 and MGMTP140K with a lentiviral vector using the F2A linker element in two different haematopoietic cell lines and in human primary HSCs with various combination regimens. Consequently, we are convinced that these in vitro investigations will help to improve combination chemotherapy regimens by reducing myelotoxic side effects and increasing the therapeutic efficiency. [ABSTRACT FROM AUTHOR]

WWOX is a bona fide tumour suppressor, with hypomorphic and knockout mouse models exhibiting increased tumour susceptibility. In ovarian cancer cells WWOX transfection abolishes tumourigenicity, suppresses tumour cell adhesion to extracellular matrix and induces apoptosis in non-adherent cells. One-third of ovarian tumours show loss of WWOX expression, and this loss significantly associates with clear cell and mucinous histology, advanced stage, low progesterone receptor expression and poor survival, suggesting that WWOX status affects ovarian cancer progression and prognosis. Genetic variation in other tumour suppressors (e.g. p53 and XPD) is reported to modify cancer progression/outcome, and single nucleotide polymorphisms (SNPs) within the WWOX gene are reported to associate with prostate cancer risk. We previously identified polymorphic variants within WWOX, some of which have potential to affect its expression. We therefore examined a cancer modifier role for these WWOX variants. Eight SNPs, based upon location, frequency and potential to affect WWOX expression, were genotyped in 554 ovarian cancer patients (CGP samples), and associations with pathological and survival data were examined. The CGP samples demonstrated significant associations after Bonferroni correction between Isnp1 and both tumour grade (p(corr)=0.033) and histology (p(corr)=0.046), Isnp8 and tumour grade (p(corr)=0.032) and T1497G and progression-free survival (p(corr)=0.037). None of these positive associations were confirmed in an independent ovarian cancer population (Scotroc1 samples, n=863). While these results may suggest that the associations are false positives, differences between the two populations cannot be excluded, and thus highlight the challenges in validation studies. [ABSTRACT FROM AUTHOR]

Abstract Background Recent advances in antibody microarray technology have made it possible to measure the expression of hundreds of proteins simultaneously in a competitive dual-colour approach similar to dual-colour gene expression microarrays. Thus, the established normalisation methods for gene expression microarrays, e.g. loess regression, can in principle be applied to protein microarrays. However, the typical assumptions of such normalisation methods might be violated due to a bias in the selection of the proteins to be measured. Due to high costs and limited availability of high quality antibodies, the current arrays usually focus on a high proportion of regulated targets. Housekeeping features could be used to circumvent this problem, but they are typically underrepresented on protein arrays. Therefore, it might be beneficial to select invariant features among the features already represented on available arrays for normalisation by a dedicated selection algorithm. Results We compare the performance of several normalisation methods that have been established for dual-colour gene expression microarrays. The focus is on an invariant selection algorithm, for which effective improvements are proposed. In a simulation study the performances of the different normalisation methods are compared with respect to their impact on the ability to correctly detect differentially expressed features. Furthermore, we apply the different normalisation methods to a pancreatic cancer data set to assess the impact on the classification power. Conclusions The simulation study and the data application demonstrate the superior performance of the improved invariant selection algorithms in comparison to other normalisation methods, especially in situations where the assumptions of the usual global loess normalisation are violated.

We aimed to explore novel biomarker candidates and biomarker signatures of late-onset preeclampsia (LOPE) by profiling samples collected in a longitudinal discovery cohort with a high-throughput proteomics platform. Using the Somalogic 5000-plex platform, we analyzed proteins in plasma samples collected at three visits (gestational weeks (GW) 12-19, 20-26 and 28-34 in 35 women with LOPE (birth ≥ 34 GW) and 70 healthy pregnant women). To identify biomarker signatures, we combined Elastic Net with Stability Selection for stable variable selection and validated their predictive performance in a validation cohort. The biomarker signature with the highest predictive performance (AUC 0.88 (95% CI 0.85-0.97)) was identified in the last trimester of pregnancy (GW 28-34) and included the Fatty acid amid hydrolase 2 (FAAH2), HtrA serine peptidase 1 (HTRA1) and Interleukin-17 receptor C (IL17RC) together with sFLT1 and maternal age, BMI and nulliparity. Our biomarker signature showed increased or similar predictive performance to the sFLT1/PGF-ratio within our data set, and we were able to validate the biomarker signature in a validation cohort (AUC ≥ 0.90). Further validation of these candidates should be performed using another protein quantification platform in an independent cohort where the negative and positive predictive values can be validly calculated., (© 2024. The Author(s).)

Background: Pathogenic germline variants in the mismatch repair (MMR) genes are associated with an increased risk of prostate cancer (PCa). Since 2010 we have recommended MMR carriers annual PSA testing from the age of 40. Prospective studies of the outcome of long-term PSA screening are lacking. This study aimed to investigate the incidence and characteristics of PCa in Norwegian MMR carriers attending annual PSA screening (PSA threshold >3.0 ng/mL) to evaluate whether our recommendations should be continued., Methods: This is a prospective observational study of 225 male MMR carriers who were recommended annual PSA screening by the Section of Inherited Cancer, Oslo University Hospital from 2010 and onwards. Incidence and tumor characteristics (age, PSA at diagnosis, Gleason score, TNM score) were described. IHC and MSI-analyses were done on available tumors. Standardized incidence ratio (SIR) was calculated based on data from the Cancer Registry of Norway., Results: Twenty-two of 225 (9.8%) had been diagnosed with PCa, including 10/69 (14.5%) MSH2 carriers and 8/61 (13.1%) MSH6 carriers. Ten of 20 (50%) tumors had Gleason score ≥4 + 3 on biopsy and 6/11 (54.5%) had a pathological T3a/b stage. Eight of 17 (47.1%) tumors showed abnormal staining on IHC and 3/13 (23.1%) were MSI-high. SIR was 9.54 (95% CI 5.98-14.45) for all MMR genes, 13.0 (95% CI 6.23-23.9) for MSH2 and 13.74 for MSH6 (95% CI 5.93-27.08)., Conclusions: Our results indicate that the MMR genes, and especially MSH2 and MSH6, are associated with a significant risk of PCa, and a high number of tumors show aggressive characteristics. While the impact of screening on patient outcomes remains to be more firmly established, the high SIR values we observe provide support for continued PSA screening of MSH2 and MSH6 carriers. Studies are needed to provide optimal recommendations for PSA-threshold and to evaluate whether MLH1 and PMS2 carriers should not be recommended screening., (© 2024 The Authors. The Prostate published by Wiley Periodicals LLC.)

Motivation: Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and treatment approaches. Modern omics technologies have made it possible to investigate the prognostic impact of markers at multiple molecular levels, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, and how these potential risk factors complement clinical characterization of patient outcomes for survival prognosis. However, the massive sizes of the omics datasets, along with their correlation structures, pose challenges for studying relationships between the molecular information and patients' survival outcomes., Results: We present a general workflow for survival analysis that is applicable to high-dimensional omics data as inputs when identifying survival-associated features and validating survival models. In particular, we focus on the commonly used Cox-type penalized regressions and hierarchical Bayesian models for feature selection in survival analysis, which are especially useful for high-dimensional data, but the framework is applicable more generally., Availability and Implementation: A step-by-step R tutorial using The Cancer Genome Atlas survival and omics data for the execution and evaluation of survival models has been made available at https://ocbe-uio.github.io/survomics., (© The Author(s) 2024. Published by Oxford University Press.)

Glioblastoma (GBM) is an aggressive and highly heterogeneous primary brain tumor. Glioma stem cells represent a subpopulation of tumor cells with stem cell traits that are presumed to be the cause of tumor relapse. There exists complex tumor heterogeneity in drug sensitivity patterns between glioma stem cell (GSC) cultures derived from different patients. Here, we describe that heterogeneity also exists between GSC cultures derived from multiple biopsies within a single tumor. From biopsies harvested within spatially distinct regions representing the entire tumor mass, we established seven GSC cultures and compared their stem cell properties, mutations, gene expression profiles, and drug sensitivity patterns against 115 different anticancer drugs. The results were compared to 14 GSC cultures derived from other patients. Between the multiregional-derived GSC cultures, we observed only minor differences in their phenotype, proliferative capacity, and global gene expression. Further, they displayed intratumoral heterogeneity in mutational profiles and sensitivity patterns to anticancer drugs. This heterogeneity, however, did not exceed the extensive heterogeneity found between GSC cultures derived from other GBM patients. Our results suggest that the use of GSC cultures from one single focal biopsy may underestimate the overall complexity of the GSC population and display the importance of including GSC cultures reflecting the entire tumor mass in drug screening strategies.

Objectives: The aim of this study was to provide real-world efficacy and safety data on niraparib maintenance treatment in patients with non-germline (gBRCA)1/2 mutated platinum-sensitive recurrent ovarian cancer., Methods: This retrospective multi-center cohort study included 94 platinum-sensitive recurrent ovarian cancer patients without known gBRCA1/2 mutation treated in an individual patient access program in Norway. The primary outcome was time from start of niraparib treatment to first subsequent treatment. Secondary endpoints included progression-free survival, safety, and tolerability., Results: After median follow-up of 13.4 months (95% confidence interval (CI) 10.0 to 16.8), 68.1% had progressed and 22.3% had died. Of the entire cohort, 61.7% had commenced a new line of treatment, and 24.5% were still receiving niraparib. The median duration of niraparib treatment was 5.0 months (range 0.4 to 27.3), and the median time to first subsequent treatment was 10.7 months (95% CI 8.4 to 13.0). Patients with elevated CA125 prior to start of niraparib had shorter time to first subsequent treatment (7.3 months, 95% CI 4.2 to 10.3) than patients with normalized CA125 (12.2 months, 95% CI 10.9 to 13.7 (p=0.002). Patients who started on individual dose based on weight and platelet counts had fewer dose reductions (p<0.001) and interruptions (p=0.02)., Conclusion: In a real-world setting, niraparib maintenance treatment in patients with non-gBRCA1/2 mutated recurrent platinum-sensitive ovarian cancer showed effectiveness comparable with published phase III studies and acceptable safety. Individualized dosing is essential to minimize adverse events. CA125 levels at start of niraparib treatment may help to estimate the individual prognosis., Competing Interests: Competing interests: No., (© IGCS and ESGO 2023. Re-use permitted under CC BY-NC. No commercial re-use. Published by BMJ.)

Background: Acute myeloid leukemia (AML) is a heterogeneous disease with a poor prognosis. Dysregulation of the epigenetic machinery is a significant contributor to disease development. Some AML patients benefit from treatment with hypomethylating agents (HMAs), but no predictive biomarkers for therapy response exist. Here, we investigated whether unbiased genome-wide assessment of pre-treatment DNA-methylation profiles in AML bone marrow blasts can help to identify patients who will achieve a remission after an azacytidine-containing induction regimen., Results: A total of n = 155 patients with newly diagnosed AML treated in the AMLSG 12-09 trial were randomly assigned to a screening and a refinement and validation cohort. The cohorts were divided according to azacytidine-containing induction regimens and response status. Methylation status was assessed for 664,227 500-bp-regions using methyl-CpG immunoprecipitation-seq, resulting in 1755 differentially methylated regions (DMRs). Top regions were distilled and included genes such as WNT10A and GATA3. 80% of regions identified as a hit were represented on HumanMethlyation 450k Bead Chips. Quantitative methylation analysis confirmed 90% of these regions (36 of 40 DMRs). A classifier was trained using penalized logistic regression and fivefold cross validation containing 17 CpGs. Validation based on mass spectra generated by MALDI-TOF failed (AUC 0.59). However, discriminative ability was maintained by adding neighboring CpGs. A recomposed classifier with 12 CpGs resulted in an AUC of 0.77. When evaluated in the non-azacytidine containing group, the AUC was 0.76., Conclusions: Our analysis evaluated the value of a whole genome methyl-CpG screening assay for the identification of informative methylation changes. We also compared the informative content and discriminatory power of regions and single CpGs for predicting response to therapy. The relevance of the identified DMRs is supported by their association with key regulatory processes of oncogenic transformation and support the idea of relevant DMRs being enriched at distinct loci rather than evenly distribution across the genome. Predictive response to therapy could be established but lacked specificity for treatment with azacytidine. Our results suggest that a predictive epigenotype carries its methylation information at a complex, genome-wide level, that is confined to regions, rather than to single CpGs. With increasing application of combinatorial regimens, response prediction may become even more complicated., (© 2023. The Author(s).)

Motivation: Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes., Results: We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method's potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data., Availability and Implementation: The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL., (© The Author(s) 2023. Published by Oxford University Press.)

Background & Aims: Changes in body composition may affect colorectal cancer (CRC) patient's risk of cancer recurrence, secondary cancer, and other chronic diseases. The suggested interventions for changes in body composition such as low muscle mass or high fat mass, are diet and physical activity. Nevertheless, there is limited evidence of how dietary intervention alone can impact body composition. This study aimed to investigate the effect of a 6 and 12 month dietary intervention with a focus on healthy eating according to Norwegian food-based dietary guidelines on weight and body composition in patients with CRC stage I-III, post-surgery., Methods: This study included participants from the randomized controlled trial CRC-NORDIET study 2-9 months after surgery. The intervention group received an intensive dietary intervention, while the control group underwent similar measurements, but no dietary intervention. Body composition was measured with Lunar iDXA, and the results were analyzed using linear mixed models., Results: A total of 383 participants were included, 192 in the intervention group and 191 in the control group. After 6 months, the intervention group showed a 0.7 kg lower mean weight gain (p = 0.020) and 0.6 kg lower fat mass gain (p = 0.019) than the control group, but no difference at 12 months. Moreover, the fat mass increase was 0.5 percentage points lower at 6 months (p = 0.012), and 0.7 percentage points lower at 12 months (p = 0.011) in the intervention group compared to the controls. At 6 months, the intervention group had 63 g lower gain of visceral adipose tissue compared to the control group (p = 0.031). No differences were seen for fat-free mass or subcutaneous adipose tissue at any time point. The intervention group showed a lower increase in the ratio between fat mass and fat-free mass at both 6 months (p = 0.025) and 12 months (p = 0.021)., Conclusion: The dietary intervention reduced the increases in total weight and fat masses, without changing fat-free mass. Although the individual changes are small, the dietary intervention may have resulted in an overall more favourable body composition profile. These findings suggest that dietary intervention may be part of a treatment strategy for prevention of weight and fat mass gain in CRC survivors., Competing Interests: Declaration of competing interest RB is a shareholder of AS Vitas. DTA have received grants from the Norwegian Research Council and the University of Oslo Growth house for development of BodySegAI, however this is unrelated to this project. HBH, PML, MZ, SKB, CH, IP and SS declare no conflict of interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Long-term antibiotics are prescribed for a variety of medical conditions, recently including low back pain with Modic changes. The molecular impact of such treatment is unknown. We conducted longitudinal transcriptome and epigenome analyses in patients ( n = 100) receiving amoxicillin treatment or placebo for 100 days in the Antibiotics in Modic Changes (AIM) study. Gene expression and DNA methylation were investigated at a genome-wide level at screening, after 100 days of treatment, and at one-year follow-up. We identified intra-individual longitudinal changes in gene expression and DNA methylation in patients receiving amoxicillin, while few changes were observed in patients receiving placebo. After 100 days of amoxicillin treatment, 28 genes were significantly differentially expressed, including the downregulation of 19 immunoglobulin genes. At one-year follow-up, the expression levels were still not completely restored. The significant changes in DNA methylation ( n = 4548 CpGs) were mainly increased methylation levels between 100 days and one-year follow-up. Hence, the effects on gene expression occurred predominantly during treatment, while the effects on DNA methylation occurred after treatment. In conclusion, unrecognized side effects of long-term amoxicillin treatment were revealed, as alterations were observed in both gene expression and DNA methylation that lasted long after the end of treatment.

In this paper we propose PIICM, a probabilistic framework for dose-response prediction in high-throughput drug combination datasets. PIICM utilizes a permutation invariant version of the intrinsic co-regionalization model for multi-output Gaussian process regression, to predict dose-response surfaces in untested drug combination experiments. Coupled with an observation model that incorporates experimental uncertainty, PIICM is able to learn from noisily observed cell-viability measurements in settings where the underlying dose-response experiments are of varying quality, utilize different experimental designs, and the resulting training dataset is sparsely observed. We show that the model can accurately predict dose-response in held out experiments, and the resulting function captures relevant features indicating synergistic interaction between drugs., (© 2023. The Author(s).)

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., (© 2022 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Aim: To describe hand use development in children with unilateral cerebral palsy who did/did not participate in constraint-induced movement therapy (CIMT) before 7 years of age., Method: The study included 334 participants (18 months-12 years) who were assessed with 1,565 Assisting Hand Assessments (AHAs) and categorized into no intensive training (NIT), CIMT (18 months-7 years), and Baby-CIMT (<18 months) groups., Results: AHA performance at 18 months (AHA-18) was positively associated with development regardless of training. The CIMT group had lower AHA-18 performance than the NIT group ( p  = .028), but higher stable limit ( p  = .076). The age when 90% of development was reached was highest in the CIMT group ( p  = .014). Although non-significant, the Baby-CIMT group had higher mean curve than NIT and CIMT combined (AHA-18 p  = .459, limit p  = .477)., Conclusion: The CIMT group improved more over time than the NIT group. Intensive training extended the window of development, and Baby-CIMT might promote early development.

Given that both hearing and touch are 'mechanical senses' that respond to physical pressure or mechanical energy and that individuals appear to have a characteristic internal or spontaneous tempo, individual preferences in musical and touch rhythms might be related. We explored this in two experiments probing individual preferences for tempo in the tactile and auditory modalities. Study 1 collected ratings of received stroking on the forearm and measured the velocity the participants used for stroking a fur. Music tempo preferences were assessed as mean beats per minute of individually selected music pieces and via the adjustment of experimenter-selected music to a preferred tempo. Heart rate was recorded to measure levels of physiological arousal. We found that the preferred tempo of favorite (self-selected) music correlated positively with the velocity with which each individual liked to be touched. In Study 2, participants rated videos of repeated touch on someone else's arm and videos of a drummer playing with brushes on a snare drum, both at a variety of tempos. We found that participants with similar rating patterns for the different stroking speeds did not show similar rating patterns for the different music beats. The results suggest that there may be a correspondence between preferences for favorite music and felt touch, but this is either weak or it cannot be evoked effectively with vicarious touch and/or mere drum beats. Thus, if preferences for touch and music are related, this is likely to be dependent on the specific type of stimulation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sailer, Zucknick and Laeng.)

Motivation: There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data and generate data files that can be analyzed by synergy-finding bioinformatics applications., Results: screenwerk is an open-source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens. The tool allows for a customized build of pipelines through its modularity and provides a flexible approach to quality control and data analysis. screenwerk is adaptable to various experimental requirements with an emphasis on precision medicine. It can be coupled to other R packages, such as bayesynergy, to identify synergistic and antagonistic drug interactions in cell lines or patient samples. screenwerk is scalable and provides a complete solution for setting up drug sensitivity screens, read raw measurements and consolidate different datasets, perform various types of quality control and analyze, report and visualize the results of drug sensitivity screens., Availability and Implementation: The R-package and technical documentation is available at https://github.com/Enserink-lab/screenwerk; the R source code is publicly available at https://github.com/Enserink-lab/screenwerk under GNU General Public License v3.0; bayesynergy is accessible at https://github.com/ocbe-uio/bayesynergy. Selected modules are available through Galaxy, an open-source platform for FAIR data analysis at https://oncotools.elixir.no., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Methotrexate is one of the cornerstones of rheumatoid arthritis (RA) therapy. Genetic factors or single nucleotide polymorphisms (SNPs) are responsible for 15%-30% of the variation in drug response. Identification of clinically effective SNP biomarkers for predicting methotrexate (MTX) sensitivity has been a challenge. The aim of this study was to explore the association between the disease related outcome of MTX treatment and 23 SNPs in 8 genes of the MTX pathway, as well as one pro-inflammatory related gene in RA patients naïve to MTX. Categorical outcomes such as Disease Activity Score (DAS)-based European Alliance of Associations for Rheumatology (EULAR) non-response at 4 months, The American College of Rheumatology and EULAR (ACR/EULAR) non-remission at 6 months, and failure to sustain MTX monotherapy from 12 to 24 months were assessed, together with continuous outcomes of disease activity, joint pain and fatigue. We found that the SNPs rs1801394 in the MTRR gene, rs408626 in DHFR gene, and rs2259571 in AIF-1 gene were significantly associated with disease activity relevant continuous outcomes. Additionally, SNP rs1801133 in the MTHFR gene was identified to be associated with improved fatigue. Moreover, associations with p values at uncorrected significance level were found in SNPs and different categorical outcomes: 1) rs1476413 in the MTHFR gene and rs3784864 in ABCC1 gene are associated with ACR/EULAR non-remission; 2) rs1801133 in the MTHFR gene is associated with EULAR response; 3) rs246240 in the ABCC1 gene, rs2259571 in the AIF-1 gene, rs2274808 in the SLC19A1 gene and rs1476413 in the MTHFR gene are associated with failure to MTX monotherapy after 12-24 months. The results suggest that SNPs in genes associated with MTX activity may be used to predict MTX relevant-clinical outcomes in patients with RA., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Kolan, Li, Grimolizzi, Sexton, Goll, Kvien, Sundlisæter, Zucknick, Lillegraven, Haavardsholm and Skålhegg.)

Summary: Enrichment analysis has been widely used to study whether predefined sets of genes or other molecular features are over-represented in a ranked list associated with a disease or other phenotype. However, computational tools that perform enrichment analysis and visualization are usually limited to predefined sets available from public databases. To make such analyses more flexible, we introduce an R package, EnrichIntersect , which enables enrichment analyses among any ranked features and user-defined custom sets. For interactive visualization of multiple covariates, such as genes or other features, which are associated with multiple phenotypes and multiple sample groups, such as drug responses in various cancer types, EnrichIntersect illustrates all associations at a glance, hence explicitly indicating intersecting covariates between multiple phenotypic variables and between multiple sample groups., Availability and Implementation: The EnrichIntersect R package is available at https://CRAN.R-project.org/package=EnrichIntersect via an open-source MIT license. A package installation process is described on CRAN at https://cran.r-project.org/. A user-manual description of features and function calls can be found from the vignette of our package on CRAN., (© The Author(s) 2022. Published by Oxford University Press.)