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• Discover Cancer Image Europe, the first release of the EUCAIM platform to fuel cancer research and data sharing

  The EUCAIM consortium and the European Commission have announced the release of Cancer Image Europe, a platform to fuel collaborative innovation and data sharing across Europe. This constitutes a major milestone in EUCAIM's development and an exciting step towards achieving the project's vision and goals. The platform aims to accelerate the pace of AI development and other data-intensive cancer research activities, constituting the basis for a fully-fledged infrastructure for sharing, reusing, and exploiting cancer imaging data, especially using AI techniques. A platform paving the way for the future of cancer diagnosis and treatment Cancer Image Europe brings benefits to researchers, clinicians, and AI innovators across Europe as it addresses the fragmentation of existing cancer image repositories. The platform gathers a public catalogue of cancer imaging datasets from the repositories of the EU-funded AI for Health Imaging projects. A distributed Atlas of Cancer Imaging with over 60 million anonymised cancer image data from over 100,000 patients will be established as part of future updates. The first version of Cancer Image Europe also includes a public catalogue of cancer imaging datasets, a federated searching tool to understand the information available in the federated providers, and full integration with Life Science Login. Moreover, it reuses and adds value to key components of EU-funded research projects and infrastructure in the field of cancer. The Public Catalogue When we talk about data re-use, data discovery is key. The EUCAIM public catalogue is the result of a systematic and collaborative process to which the EGA-CRG has actively contributed. This browsing interface gathers the metadata of the available cancer imaging datasets. This descriptive information will help researchers in their data retrieval. Our contribution to the Public Catalogue comes from our experience gained in the deployment of our federated network, the Federated EGA (FEGA), and as partners in projects such as EuCanImage (AI4HI initiative), in which we worked on the definition of common data models, ontologies and data standards. The EUCAIM Project EUCAIM is a cornerstone of the European Commission-initiated European Cancer Imaging Initiative, a flagship of the Europe's Beating Cancer Plan, which aims to foster innovation and deployment of digital technologies in cancer treatment and care, to achieve more precise and faster clinical decision-making, diagnostics, treatments, and predictive medicine for cancer patients. For more information about the first release of the Cancer Image Europe platform and the EUCAIM project, please visit cancerimage.eu.

  Blog discover-cancer-image-europe

• Colorectal cancer functional annotation - Micro-C

  Ultra high resolution Micro-C data from multiple colorectal cancer cell lines

  Study EGAS50000000206

• Pancreatic Cancer NGS WGS data

  Whole genome sequencing data for Pancreatic Cancer Samples exploring the tandem duplicator phenotype

  Study EGAS50000001078

• Division of Cancer Evolution National Cancer Center Research Institute Data Access Committee

  Data Access Committee for data from Division of Cancer Evolution, National Cancer Center Research Institute

  Dac EGAC50000000737

• Mutational anysis of breast cancer stem cells

  Exome sequences of breast cancer have been investigated for many patients. Recently it has been shown that there are intra-heterogeneity in tumor tissues. When the tumor cells were sequenced in bulk condition, only the major populations��� (clones���) mutations will be detected. Based on cancer stem theory, each populations��� cells are derived from each cancer stem cells, which their numbers are not so different among different clones. Here, we separated cancer cells into cancer stem cells and non-cancer stem cells exploiting cancer stem cell specific surface markers. By comping two groups��� mutation, we identified mutations, which were only detected in stem cell population. These mutations could be the mutations in cells of small size populations, and maybe the important mutations to understand the heterogeneity of tumor tissues, progression, and/or prediction for the outcome to therapies.

  Study JGAS000304

• WGS-Lung Cancer sample 30 pair

  This study includes 30 paired samples of Korean Lung Cancer paitients. 30 Lung Cancer samples are paired with each normal(Saliva derived for control) sequencing data.The results of this WGS study were analyzed by Mutect and GATK for ranking germline variants and cancer variants to make mutational profile matrix.

  Study EGAS00001001474

• Genomic analysis of Japanese gastric cancer (345 gastric cancers of NCC)

  As a contribution to the International Cancer Genome Consortium, whole exome sequencing of 345 Japanese gastric cancer (345 gastric cancers of NCC) with various histological subtypes have been conducted. This study aims to identify unique and common driver genes and molecular subtypes in Japanese gastric cancer.

  Study EGAS00001002884

• MCO colorectal cancer genomics at UNSW

  This dataset contains DNA sequencing data from 95 colorectal cancer and matched-normal samples. The dataset contains targeted deep sequencing of selected regulatory elements in 95 cancer and matched-normal samples, and data for one sample that was additionally whole-genome sequenced (cancer and matched-normal).

  Study EGAS00001003450

• BRCA Mutation Status Shapes the Microenvironment of Pancreatic Adenocarcinoma

  This study was established in order to address how the stromal landscape is influenced by BRCA-mutated and BRCA Wild-type (WT) pancreatic ductal adenocarcinoma (PDAC). A primary cohort of 12 PDAC patients (7 BRCA-WT and 5 germline BRCA-mut) was analyzed by laser-capture microdissection, RNA-sequencing and multiplexed immunofluorescence.

  Study phs002994

• RNA sequencing of primary B-cells infected with Epstein-Barr virus (EBV), treated with heat-inactivated EBV, CpG or BCR-crosslinking in presence or absence of Linrodostat

  B cells were isolated from buffy coat preparations of healthy donors using CD19 magnetic beads. B cells were then infected with EBV using spinoculation or activated with heat-inactivated EBV, respectively. Additionally cells were treated with 5ng/ml CpG or a BCR-crosslinking mixture in presence or absence of 10 µM Linrodostat (IDO1 inhibitor). RNA samples were isolated at 2 days post-infection / post-activation and one day 0 control with non-infected B cells was included.

  Dataset EGAD50000000306