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• Finnish_population_cohort_genotyping_B

  The FINRISK cohorts comprise the respondents of representative, cross-sectional populationsurveys that are carried out every 5 years since 1972, to assess the risk factors of chronicdiseases (e.g. CVD, diabetes, obesity, cancer) and health behavior in the working agepopulation, in 3-5 large study areas of Finland. DNA samples were collected in the followingsurvey years: 1987, 1992, 1997, 2002, 2007, and 2012. The MONICA and EHES (EU)procedures were applied in phenotype collection (cf. MORGAM) and a wide spectrum oflaboratory tests was carried out from serum and plasma samples. Background information onsocioeconomic status, medical history, diet, exercise, measured anthropometric measures,etc. was collected by questionnaires and during a clinical visit. Plasma/serum samples arestill available for the 2002-2012 cohorts. The cohort sizes are 6000-8800 per survey. Thecohorts have been followed up by linking them to the national hospital discharge register,causes-of-death register and cancer register.This project is an extension to previous efforts to build a catalogue of Finnish genome widedata on population-based Finsrisk samples with rich phenotypic characterisations and healthregistry link-up. These samples will extend the current Sequencing Initiative Suomi (SISu)samples with a combination of genotyping using Illumina HumanCoreExome array and SISu-based imputation. This will lead to high confidence common and low frequency variantcatalogue. The project will be funded by Aarno Palotie’s remaining faculty fundscomplemented by Finnish funding from FIMM.

  Study EGAS00001001047

• PacBio data: Rapid brain tumor classification from sparse epigenomic data

  This dataset contains 16 samples sequenced in pools on SMRT Cells 25M on a PacBio Revio instrument. One unaligned BAM file per sample is provided.

  Dataset EGAD50000000798

• TARGET-seq+ single-cell genotyping

  Single-cell genotyping data for bone marrow samples from 9 cases with clonal hematopoiesis and 1 control sample. The TARGET-seq+ protocol was used to generate plate-based 3' transcriptome data. For details on cell sorting and the TARGET-seq+ protocol see the methods section of the manuscript. One FASTQ file is provided per cell. Cells are named with their plate and well IDs and the subject ID. Empty wells (no-cell controls) are named "blank". Corresponding transcriptome files use the same naming with the "_transcriptome" suffix.

  Dataset EGAD00001011150

• Neuroblastoma WGS samples used for detection of seismic amplification

  Paired-end WGS data of 27 neuroblastoma patient samples (10 obtained at diagnosis, 6 at relapse and 11 matched blood samples as controls) used for detection of complex "seismic" amplification. Mean coverage is 24-55x per sample. The remaining patient samples of the dataset can be found under accession number EGAS00001001308.

  Dataset EGAD00001007807

• Biobank Japan genotype and phenotype data

  The Biobank Japan (BBJ) Project started at the Institute of Medical Science, the University of Tokyo in 2003. To date, the BBJ Project has collected around 200,000 individuals with disease cases consisting of 47 diseases. The purpose of the BBJ is to realize personalized medicine.

  Study JGAS000114

• Loss of IFN-gamma Signaling in Tumor Cells Associates with Primary Resistance to Anti-CTLA-4 Therapy

  Antibody blockade of the inhibitory CTLA-4 pathway has led to clinical benefit in a subset of patients with metastatic melanoma. Anti-CTLA-4 enhances T cell responses, including production of IFN-γ, which is a critical cytokine for host immune responses. However, the role of IFN-γ signaling in tumor cells in the setting of anti-CTLA-4 therapy remains unknown. Here we demonstrate that, based upon exome sequencing data, patients identified as non-responders to anti-CTLA-4 (ipilimumab) harbor a much higher genomic defects in the IFN-γ pathway genes than melanoma patients who had clinical response to ipilimumab therapy.

  Study phs001257

• Cancer Genomics of the Kidney

  The aim of CAGEKID is to carry out comprehensive detection of DNA markers for conventional (clear cell) renal carcinoma. The project includes complete analysis of somatic and constitutional DNA variation, methylation patterns and expression in a large number of constitutional/tumor pairs. CAGEKID is a part of the International Cancer Genome Consortium, ICGC.

  Dataset EGAD00001004018

• RNA-seq BAM files from newborn screening dried blood spot samples

  Paired-end RNA-seq BAM files from 21 newborn screening dried blood spot (DBS) samples. These DBS samples were obtained from extremely low gestional age newborns, where 10 of them were affected by a fetal inflammatory response (FIR) before birth, and 11 were unaffected. Total RNA was sequenced using an Illumina NextSeq-500 instrument. The sample preparation protocol included the depletion of rRNA and globin mRNA using the Globin Zero Gold rRNA Removal Kit from Illumina. Libraries were prepared using the NebNext Ultra TM II Directionl RNA LIbrary Prep Kit (New England Biolabs). There is one BAM file per sample and there is an additional BAM file, corresponding to sample BS13, which was downsampled to 1/4 of its original depth (see BS13_README file for details).

  Dataset EGAD00001005009

• Celiac disease-specific intestinal T cells analyzed with HLA-class II tetramers, RNA-seq and mass cytometry have a narrow, autoimmune-associated phenotype

  Celiac disease (CD) is an HLA-DQ2/8-associated autoimmune enteropathy driven by activation of gluten-specific CD4+ T lymphocytes upon gluten consumption. Much less is known about the phenotype and function of these cells or their correlation, if any, to disease-relevant cells in other autoimmune disorders. Here we use mass cytometry and RNA seq to show that gluten-specific blood and gut T cells occupy a small and phenotypically distinct T-cell subset.

  Study EGAS00001003017

• Perceptions, Prevalence, and Patterns of Cannabis Use among Cancer Patients in NCI-Designated Cancer Centers

  This study aimed to better understand the evolving landscape of cannabis use among patients undergoing cancer treatment across diverse geographic regions. It sought to characterize patterns of use, patient perceptions, and contextual influences such as legal status and provider engagement that affect cannabis use. Given the shifting legal environment, this study addresses a critical gap in the literature by providing data on how cancer patients integrate cannabis into their care. The available delivery methods of cannabis have also undergone dramatic changes and now include edibles, oils, tinctures, topicals, and inhaled forms. State-based policy changes are rapidly changing. Yet research on cannabis use by cancer patients across a variety of geographic settings remains limited. The extent of cannabis use, the perceived and real benefits and risks of use, potential interactions with cancer treatment and other medications, and impact on comorbid conditions are uncertain. Clinicians should be aware of the extent of use in order to assess potential drug-drug interactions, side effects, and contraindications; hence, an understanding of how cancer patients and clinicians engage in discussions about cannabis use is essential. Twelve NCI-designated cancer centers were awarded supplemental funding to conduct surveys assessing cannabis use among recently diagnosed cancer patients. Selected sites were in 8 states with varied legal status for medical and recreational cannabis use at the time of the survey. The 12 cancer centers independently received approval from their institutional review boards (IRB) and collected data from September 2021 to August 2023. Eligible participants were cancer patients or survivors who resided in their respective cancer center's catchment area. Cancer centers were responsible for sampling patients within their catchment area with the goal of 1,000 completed surveys. Ten out of 12 cancer centers drew probability samples of patients from patient lists defining their catchment area with some stratifying the sample in various ways including by cancer type, by sex, by race/ethnicity or by a combination of demographic variables.

  Study phs004046