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• Clinical and genetic factors associated with tumor response to neoadjuvant (chemo)radiotherapy, survival and recurrence risk in rectal cancer

  Rectal cancer poses challenges in preoperative treatment response, with up to 30% achieving a complete response (CR). Personalized treatment relies on accurate identification of responders at diagnosis. This study aimed to unravel CR determinants, overall survival (OS), and time to recurrence (TTR) using clinical and targeted sequencing data. Analyzing 402 patients undergoing preoperative treatment, tumor stage, size, and treatment emerged as robust response predictors. CR rates were higher in smaller, early-stage, and intensively treated tumors. Targeted sequencing analyzed 216 cases, while 120 patients provided hotspot mutation data. KRAS mutation dramatically reduced CR odds by over 50% (odds ratio [OR]=0.3 in the targeted sequencing and OR=0.4 hotspot cohorts, respectively). In contrast, SMAD4 and SYNE1 mutations were associated with higher CR rates (OR=6.0 and 6.8, respectively). Favorable OS was linked to younger age, CR, and low baseline carcinoembryonic antigen levels. Notably, CR and an APC mutation increased TTR, while a BRAF mutation negatively affected TTR. Beyond tumor burden, SMAD4 and SYNE1 mutations significantly influenced CR. KRAS mutations independently correlated with radiotherapy resistance, and BRAF mutations heightened recurrence risk. Intriguingly, nonresponding tumors with initially small sizes carried a higher risk of recurrence. These findings offer insights into rectal cancer treatment response, guiding personalized therapeutic strategies. By uncovering factors impacting CR, OS, and TTR, this study underscores the importance of tailored approaches for rectal cancer patients. These findings, based on extensive analysis and mutation data, pave the way for personalized interventions, optimizing outcomes in the challenges of rectal cancer preoperative treatment.

  Study EGAS00001007501

• Peripheral blood DNA methylome in adalimumab-treated patients with rheumatoid arthritis

  Background and aims Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade such as anti-TNFα therapy. Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood Methods DNA methylation profiling on whole peripheral blood from 92 RA patients before the start of adalimumab (ADA) was determined using Illumina HumanMethylationEPIC BeadChip Array. After 6 months, treatment response was assessed according to the Alliance of Associations for Rheumatology (EULAR) criteria for disease response. Patients were classified as responders (DAS28<3.2 or decrease of 1.2 points) or as non-responders (DAS28>5.1 or decrease of less than 0.6 points). Machine learning models were built with gradient boosting and stability selection models to predict response prior to ADA treatment with predictor DNA methylation markers. Results We demonstrated a 27-feature panel of DNA methylation markers or CpG classifiers that predict response in RA patients treated with ADA. Forty-nine patients were assigned as responder and 43 patients assigned as non-responders. We differentiate responders from non-responders with a high sensitivity (AUC 0.76). The predictor CpGs annotated to genes involved in immunological- and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology and angiogenesis. Conclusion Our findings indicate that DNA methylation signatures discriminate responders and non-responders to ADA treatment and can serve as a tool for therapy prediction.

  Study EGAS00001007578

• CINECA_synthetic_cohort_EUROPE_UK1 referencing fake samples

  Please note: This synthetic data set (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about cohort data or results. The purpose of this dataset is to aid development of technical implementations for cohort data discovery, harmonization, access, and federated analysis. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY). Please ensure this preamble is included with this dataset and that the CINECA project (funding: EC H2020 grant 825775) is acknowledged. For any questions please contact isuru@ebi.ac.uk or cthomas@ebi.ac.uk This dataset (CINECA_synthetic_cohort_EUROPE_UK1) consists of 2521 samples which have genetic data based on 1000 Genomes data (https://www.nature.com/articles/nature15393), and synthetic subject attributes and phenotypic data derived from UKBiobank (https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001779). These data were initially derived using the TOFU tool (https://github.com/spiros/tofu), which generates randomly generated values based on the UKBiobank data dictionary. Categorical values were randomly generated based on the data dictionary, continuous variables generated based on the distribution of values reported by the UK Biobank showcase, and date / time values were random. Additionally we split the phenotypes and attributes into 4 main classes - general, cancer, diabetes mellitus, and cardiac. We assigned the general attributes to all the samples, and the cardiac / diabetes mellitus / cancer attributes to a proportion of the total samples. Once the initial set of phenotypes and attributes were generated, the data data was checked for consistency and where possible dependent attributes were calculated from the independent variables generated by TOFU. For example, BMI was calculated from height and weight data, and age at death generated by date of death and date of birth. These data were then loaded to the development instance of Biosamples (https://www.ebi.ac.uk/biosamples/) which accessioned each of the samples. The genetic data are derived from the 1000 Genomes Phase 3 release (https://www.internationalgenome.org/category/phase-3/). The genotype data consists of a single joint call vcf files with call genotypes for all 2504 samples, plus bed, bim, fam, and nosex files generated via plink for these samples and genotypes. The genotype data has had a variety of errors introduced to mimic real data and as a test for quality control pipelines. These include gender mismatches, ethnic background mislabelling and low call rates for a randomly chosen subset of sample data as well as deviations from Hardy Weinberg equilibrium and low call rates for a random selection of variants. Additionally 40 samples have raw genetic data available in the form of both bam and cram files, including unmapped data. The gender of the samples in the 1000 genomes data has been matched to the synthetic phenotypic data generated for these samples. The genetic data was then linked to the synthetic data in BioSamples, and submitted to EGA.

  Dataset EGAD00001006673

• DAC "Genotyping based on SNPs covered by targeted NGS"

  DAC for the dataset from the article "Genotyping from targeted NGS data based on a small set of SNPs correctly matches patient samples"

  Dac EGAC50000000358

• Reference epigenome DB31_N_Alpha_WGBS data generated from KEP study

  A DB31_N_Alpha_WGBS paired end data for alpha cells(PSA-NCAM(-), pancreas)

  Dataset EGAD00001003472

• Reference epigenome ADMSC01_ChIP-Seq(H3K27me3) data generated from KEP study

  A ADMSC01_ChIP-Seq(H3K27me3) paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003854

• Reference epigenome ADMSC02_ChIP-Seq(H3K27me3) data generated from KEP study

  A ADMSC02_ChIP-Seq(H3K27me3) paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003855

• Reference epigenome ADMSC03_ChIP-Seq(H3K27me3) data generated from KEP study

  A ADMSC03_ChIP-Seq(H3K27me3) paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003856

• Reference epigenome ADMSC04_ChIP-Seq(H3K27me3) data generated from KEP study

  A ADMSC04_ChIP-Seq(H3K27me3) paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003857

• Reference epigenome SMC01_smRNA-Seq data generated from KEP study

  A SMC01_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003858

• Reference epigenome SMC02_smRNA-Seq data generated from KEP study

  A SMC02_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003859

• Reference epigenome SMC03_smRNA-Seq data generated from KEP study

  A SMC03_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003860

• Reference epigenome SMC04_smRNA-Seq data generated from KEP study

  A SMC04_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003861

• Reference epigenome SMC05_smRNA-Seq data generated from KEP study

  A SMC05_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003862

• Reference epigenome SMC06_smRNA-Seq data generated from KEP study

  A SMC06_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003863

• Reference epigenome SMC07_smRNA-Seq data generated from KEP study

  A SMC07_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003864

• Reference epigenome SMC08_smRNA-Seq data generated from KEP study

  A SMC08_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003865

• Reference epigenome SMC09_smRNA-Seq data generated from KEP study

  A SMC09_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003866

• Reference epigenome ADMSC01_WGBS data generated from KEP study

  A ADMSC01_WGBS paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003878

• Reference epigenome ADMSC02_WGBS data generated from KEP study

  A ADMSC02_WGBS paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003879

• Reference epigenome ADMSC03_WGBS data generated from KEP study

  A ADMSC03_WGBS paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003880

• Reference epigenome ADMSC04_WGBS data generated from KEP study

  A ADMSC04_WGBS paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003881

• Reference epigenome KNIH007 mRNA-seq data generated from KEP study

  A KNIH007 mRNA-seq paired end data for adipocytes

  Dataset EGAD00001002173

• Reference epigenome KNIH008 mRNA-seq data generated from KEP study

  A KNIH008 mRNA-seq paired end data for adipocytes

  Dataset EGAD00001002174

• Reference epigenome KNIH009 mRNA-seq data generated from KEP study

  A KNIH009 mRNA-seq paired end data for preadipocytes

  Dataset EGAD00001002175