Oncology services

Centre Hospitalier Universitaire De Rennes (CHU RENNES)

Anatomic pathology slides: preparation +/- associated patient clinical data +/- analysis +/- software evaluation +/- medical expertise; cancer, lesion, sample, biopsy, tissue, organ; histology, diagnosis, genetic; microscope, scanner

Univerzita Komenskeho V Bratislave (UK BA)

(1) The laboratory provides microsatellite instability (MSI) in FFPE cancer tissue in low throughput screening methods, including PCR and fragment analysis. The service includes insight into technology, sample preparation, and partial or full bioinformatic data processing in GeneMapper v.6 software. (2) Next, the laboratory provides DNA methylation analysis in promoter regions of genes in low and medium throughput screening methods, including pyrosequencing and/or MS-MLPA and fragment analysis. The service includes insight into technology, sample preparation, and partial or full bioinformatic data processing in Cofalyser.net softvare. (3) The laboratory also provides comparative genomic hybridization (CGH), as well as single nucleotide polymorphism (SNP) detection by microarray that enables identification of aneuploidies, microdeletions, microduplications, as well as other types of chromosomal aberrations across the genome. The service includes insight into technology, sample preparation, and partial or full bioinformatic data processing. (4) The laboratory provides detection of single nucleotide variation from various types of samples by real-time PCR or Sanger sequencing. The method is also usefull for verification of whole genome sequencing results. The service includes insight into technology, sample preparation, and partial or full bioinformatic data processing. (5) The laboratory also provides targeted sequencing of selected parts of the genome (e.g. TruSight Oncology 500 panel), followed by somatic or germline analysis. The service includes insight into technology, sample preparation, and partial or full bioinformatic data processing in Pierian software with clinical interpretation.

Eucaim

Cancer Image Europe provides a robust, trustworthy platform for researchers, clinicians, and innovators to access diverse cancer images, enabling the benchmarking, testing, and piloting of AI-driven technologies. EUCAIM Platform integrates a dashboard, a catalogue and a federated searching environment to discover Medical Imaging data related to cancer from a distributed federation of data holders. EUCAIM provides in some nodes processing capacity to securely access the data. EUCAIM is mainly intended for Data scientists to develop, validate or improve AI models or image postprocessing tools. The platform is accessible in https://dashboard.eucaim.cancerimage.eu/

Univerzita Komenskeho V Bratislave (UK BA)

Laboratory infrastructure includes flow cytometer/sorter FACS Aria II c, fluorescence microscope Olympus iX72, light microscope, BSL-2 laminar hoods, cell incubators, and electrophoretic equipment for Western blot. The laboratory develops and prepares unique in vitro models (3D and 2D cancer cell lines) derived from human tumor tissue.

Karolinska Institutet (KI)

## Overview Identifying and acquiring existing data sets—full-service data management. This service identifies data sets and makes them available to SME’s for TEF-project purposes, such as validation and testing of AI systems. Data can be collected from routine health records and local, regional, and national databases in collaboration with healthcare providers in Sweden. We provide expertise and technical support in the following areas: - Project design & management - Ethical application support - Data assembly - Data cleaning, annotation, anonymisation ### How can the service help you? Access to existing health data can help you validate your AI solution without needing to generate new data. We provide access to high quality datasets for validation and testing of your AI system to facilitate placing it on the market, and supporting to increase its market readiness level. Validation and testing data can be used for independent performance evaluation of your AI system in a real-world or simulated environment. ### How the service will be delivered? The service will be delivered according to established ethical agreements and guidelines, in collaboration with the SME and researchers from the Swedish TEF-Health node. Data usage is facilitated through a secure virtual environment managed by Karolinska Institutet, ensuring the highest standards of data protection. --- ## Additional information ### Provider description The Swedish TEF-Health node is a collaboration between Karolinska Institutet, SciLifeLab and RISE, and is led by Karolinska Institutet. Together, we offer world-leading services with our unique collection of core facilities. We can grant services in expert consulting, virtual- and physical testing in the range of in vivo imaging, ex vivo OMICS, pharmaceutical development, simulated healthcare environments, AI-system validation and development, advanced data analysis and other data-driven life science. ### Technical description Data access and usage are facilitated through a secure environment managed by Karolinska Institutet. Datasets will be identified and made available to suit your AI system, to support with testing and validating your solution. The data will be tailored to your specific request to ensure it is compatible with your AI system, with full support from Karolinska Insitutet to ensure compliance with all relevant regulations. Upon completion of this service, you will receive an evaluation of the performance of your AI system, which can support the placing of your solution on the EU market. ### Service customization The service can be customised according to your specific needs, taking into account which type of data you require. --- ## Use case example ### Context A biotech SME is developing an AI-based system designed to improve cancer diagnosis, treatment planning, and prognosis prediction. To gain regulatory approval and to facilitate market entry, the SME requires access to diverse, high-quality datasets, including cancer imaging, electronic healthcare records (EHR), and multi-omics data, for independent performance evaluation. ### Objective The goal is to validate the AI system's ability to analyze complex datasets and deliver accurate, reliable outputs in simulated and real-world scenarios. ### Solution The identification and enablement of existing health data for AI solutions service, provided by the Swedish TEF-Health node in collaboration with Karolinska Institutet, offers ethically sourced datasets tailored to the SME’s needs. ### Implementation #### Ethical Agreement The SME enters into an ethical agreement with researchers from the Swedish TEF-Health node, ensuring all data collection and usage comply with GDPR and national Swedish regulations. #### Data Collection - Cancer Imaging Data: Retrospective datasets from imaging modalities like CT, MRI, and PET scans, annotated for various cancer types and stages. - EHR Data: Pseudonymised clinical data, including patient histories, treatment outcomes, and longitudinal follow-up records. - Omics Data: Retrospective genomic, transcriptomic, and proteomic datasets linked to cancer cases. #### Secure Access Usage of the collected data is facilitated through a secure virtual environment managed by Karolinska Institutet, ensuring the highest standards of data protection. #### Validation and Testing Through a collaboration between Karolinska Institutet and the SME, the dataset is used to test the AI system's ability to: - Detect cancer accurately across imaging modalities. - Predict treatment responses and outcomes using combined EHR and omics data. - Identify biomarkers associated with different cancer subtypes and prognostic outcomes. ### Outcome Following the validation and testing, the AI system can be shown for its robustness, accuracy, and reliability, supporting regulatory approval and enhancing confidence for healthcare providers and stakeholders. ### Benefits - **Comprehensive Dataset**: Integration of imaging, EHR, and omics data ensures the AI system is tested across real-world complexities. - **Ethical and Secure**: Compliance with ethical guidelines builds trust and supports regulatory approval. - **Accelerated Innovation**: Access to retrospective data saves time, allowing the SME to focus on model optimization and deployment. ### Impact The validated AI system enhances early cancer detection and personalized treatment planning, improving patient outcomes while streamlining healthcare workflows.

Fraunhofer Gesellschaft Zur Forderung Der Angewandten Forschung Ev (Fraunhofer)

**Who Can Benefit:** - MedTech & AI Companies: For obtaining high-quality, ethically sourced clinical datasets to fuel algorithm development without navigating hospital bureaucracy. - Clinical Researchers: For systematic, regulation-compliant data collection with full documentation and audit trail. **Key Features:** - End-to-end management of ethical approval processes (ethics committee submission, informed consent design) - Prospective data collection within Universitätsklinikum Erlangen clinical departments - Expert clinical annotation and ground truth labeling by domain specialists - Full GDPR compliance with pseudonymization/anonymization pipelines - Structured data output compatible with downstream AI training and analysis workflows **Possible Applications:** - Wearable Sensor Studies (e.g. Cardiology): Prospective collection of continuous ECG, PPG, or blood pressure data from cardiac patients for digital biomarker development and remote monitoring algorithm training. - Motion & Gait Analysis (Biomechanics): Systematic acquisition of IMU, force plate, and motion capture data in clinical and sports lab settings for movement disorder assessment, rehabilitation tracking, or athletic performance evaluation. - Neurological Signal Acquisition: Collection of EEG, EMG, or tremor sensor data from neurological patient cohorts to support AI-based diagnostic or therapy monitoring tools. - Real-World Evidence Datasets: Longitudinal data collection from ambulatory patients in everyday settings, enabling the development of algorithms that generalize beyond controlled lab environments. - Athletic Performance & Injury Prevention: Structured data gathering during sports science testing protocols to build datasets for injury risk prediction, load monitoring, and return-to-play decision support. - Multi-Modal Clinical Datasets: Combined collection of sensor data, imaging, lab values, and patient-reported outcomes to enable holistic, multi-modal AI model development. **Who We Are:** The **Fraunhofer Insitute for Integrated Circuits (Fraunhofer IIS)** has established the **"Center for Sensor Technology and Digital Medicine" (CEMDIS)** in cooperation with the Universitätsklinikum Erlangen and the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) to enhance modern healthcare through **innovative sensor technology** and **digital solutions**. This center focuses on integrating **innovative medical technologies** such as **wearables** and **robotic systems** to support **medical diagnostics**, **patient monitoring** and **evaluating patient-specific therapies** by providing digital health solutions für real-life healthcare. Located at the Universitätsklinikum Erlangen, it offers unique infrastructures for the **development**, **integration**, and **validation** of novel health technologies, providing companies opportunities for **technological advancements**. For more information, visit the [Fraunhofer IIS website](https://www.iis.fraunhofer.de/de/ff/sse/health/zentrum-sensorik-medizin.html).

Karolinska Institutet (KI)

The Swedish node is a collaboration between Karolinska Institutet, SciLifeLab and RISE. Together, we offer world-leading services with our unique collection of core facilities. We can grant services in expert consulting, virtual- and physical testing in the range of in vivo imaging, ex vivo OMICS, pharmaceutical development, simulated healthcare environments, AI-system validation and development, advanced data analysis and other data-driven life science. Prospective data set collection: A full-service collection of new data sets. This service serves to create data sets on-demand according to the needs and requirements of the SME for TEF-project purposes, such as validation. Data sets will primarily comprise in vivo imaging data (PET, CT, MRI, MEG, EEG), ex vivo imaging, and OMICS data within neuro and cancer. Data can be generated by CIR and SciLifeLab infrastructures within our “Physical Testing” services or assembled from publicly available databases and research collaborations. We provide expertise and technical support in the following areas: o Project design& management o Ethical application support o Data collection o Data assembly o Data cleaning, annotation, anonymisation This service can typically directly collaborate with “physical testing services” and technology infrastructures to generate raw data material.

Karolinska Institutet (KI)

The Unit: Clinical Genomics Stockholm (https://www.scilifelab.se/units/clinical-genomics-stockholm/) This infrastructure is part of Science for Life Laboratory (SciLifeLab) which is an academic collaboration between Swedish universities (including Karolinska Institutet) and a national research infrastructure with a focus on life science. Clinical Genomics Stockholm unit is a research infrastructure for large-scale, genomics-based analyses using next generation sequencing technologies. The unit assists in translational research projects, in the translation of genomics-based tools to routine clinical care and also aims to improve the capability for national microbial surveillance and for pandemic preparedness. The Service: Transcriptome sequencing (RNA-seq) Sequencing of mRNA (poly A based method) followed by fusion detection or germline analysis. This service encompasses: o Consultation (Project design, Target capture design) o Sample management o RNA sequencing (total RNA, mRNA) o Bioinformatics (Bioinformatic analysis on data generated at CG) Equipment Our unit has access to specialized equipment that enable us to factilitate the translation of new high-throughput techniques into clinical use. These include, but are not restricted to: • Various automatic robotic systems (BRAVO NGS Workstation, Hamilton NGS Star) • Various systems for QC, quantification and fragment analyses (TapeStation, Quantification using fluorescent assay (Qubit, Quantit)) • Instrumentation for real time PCR and ddPCR (BioRad qPCR) • Instrumentation for Multispectral imaging (Saphyr optical mapper) • Sequencing platforms: o Illumina (NovaSeq 6000, NovaSeq 6000 Dx, NovaSeq X) o Nanopore (PromethION “access via NGI”) o PacBio (Revio)

Karolinska Institutet (KI)

The Unit: Clinical Genomics Stockholm (https://www.scilifelab.se/units/clinical-genomics-stockholm/) This infrastructure is part of Science for Life Laboratory (SciLifeLab) which is an academic collaboration between Swedish universities (including Karolinska Institutet) and a national research infrastructure with a focus on life science. Clinical Genomics Stockholm unit is a research infrastructure for large-scale, genomics-based analyses using next generation sequencing technologies. The unit assists in translational research projects, in the translation of genomics-based tools to routine clinical care and also aims to improve the capability for national microbial surveillance and for pandemic preparedness. The Service: Whole genome or targeted DNA sequencing Sequencing of the entire human genome (with a PCR-free protocol) or selected parts of the genome (through exome or panel sequencing), followed by germline or somatic analysis. This service encompasses: o Consultation (Project design, Target capture design) o Sample management o DNA sequencing (Whole Genome Seq (WGS), Whole Exome Seq (WES), Panels) o Bioinformatics (Bioinformatic analysis (data generated at CG) Equipment: Our unit has access to specialized equipment that enable us to factilitate the translation of new high-throughput techniques into clinical use. These include, but are not restricted to: • Various automatic robotic systems (BRAVO NGS Workstation, Hamilton NGS Star) • Various systems for QC, quantification and fragment analyses (TapeStation, Quantification using fluorescent assay (Qubit, Quantit)) • Instrumentation for real time PCR and ddPCR (BioRad qPCR) • Instrumentation for Multispectral imaging (Saphyr optical mapper) • Sequencing platforms: o Illumina (NovaSeq 6000, NovaSeq 6000 Dx, NovaSeq X) o Nanopore (PromethION “access via NGI”) o PacBio (Revio) Prospective Use case: AI based software solution uses WGS data to identify genetic biomarkers to help clinicians either during diagnostic phase and/or at the treatment phase in order to identify genetic aberrations possibly explaining the disease onset or to assist in the therapeutic decisions such as for example in breast cancer the presence of BRCA1 mutation. AI software can be trained to learn to deliver diagnostic based on WGS data (especially needed in the field of rare diseases) to recognize genetic variations (from single nucleotide to over 50bp deletion or insertion) in a more robust and accurate manner (to limit generation of false negative or false positive cases) to support clinicians in their decision process.