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)

## Overview This service provides a complete development of a Retrieval Augmented Generation (RAG) model for your SME’s needs. RAG is a cutting-edge AI methodology that has been gaining traction in recent years. RAG models combine the capabilities of classical large language models (LLMs) with the ability to retrieve relevant information from external knowledge sources, such as databases or document collections. This approach allows RAG models to provide more accurate and context-specific responses, stating the precise source of the information they generate. RAG models can be more resource-effective by focusing on relevant information retrieval rather than relying solely on extensive pre-training. RAG models have a wide range of potential applications, from specialized clinical tools, such as those used in molecular tumor boards, to more general applications, like serving as powerful search engines when connected to cloud storage. We provide expertise and technical support in the following areas: - Project design & management - Legal & ethical support SciLifeLab Data Centre has extensive experience in AI development and offers this comprehensive RAG service. Our service covers the entire AI lifecycle, including: #### Design - Understanding the problem - Requirements engineering - Data fitting #### Development - Modeling - Evaluation - Fine-tuning and optimization #### Deployment - Moving to production Please note that long-term maintenance and hosting responsibilities are not included in this service and will need to be managed by the SME. However, our team can provide preparation and advisory support before the service is finalized. ### How can the service help you? This service helps SMEs use advanced AI technology by creating Retrieval Augmented Generation (RAG) models designed to meet their specific needs. With expert support through every step of the process, the service ensures the model is effective, easy to use, and ready to deploy, helping SMEs make better decisions, save time and build confidence in their AI tools. ### 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. --- ## 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 The service develops RAG models, leveraging advanced AI frameworks and retrieval systems. Model development involves fine-tuning LLMs using frameworks such as PyTorch and TensorFlow and integrating them with external knowledge sources, such as databases or document collections, using retrieval mechanisms like ElasticSearch. Data preprocessing includes tokenization, vectorization, and indexing to ensure efficient information retrieval. The models are optimized for accuracy, relevance, and transparency, with quality assurance protocols applied to validate performance and compliance with ethical AI guidelines. All operations adhere to established standards for AI development and incorporate best practices for interoperability and scalability. ### Service customization The service can be customized according to your specific needs. It may be required to combine this service with other services on offer. --- ## Use case example ### Context A life science SME specializing in clinical decision support tools aims to develop an AI-driven application to assist molecular tumor boards in identifying personalized treatment options for cancer patients. The SME has access to large document collections, including scientific literature, clinical guidelines and patient case studies. However, the SME lacks the technical expertise to build a system that can retrieve relevant, context-specific information from these sources while integrating it with a large language model for natural language responses. This limitation hinders their ability to create an efficient tool for supporting oncologists, slowing product development and reducing stakeholder interest. ### Objective To develop a RAG model tailored to the SME’s needs, enabling seamless integration of large language models with their document collections. Ensure the model provides accurate, context-specific responses with transparent source references, supporting oncologists in making personalized treatment decisions. Deliver a fully optimized, production-ready model to enhance the SME's product development and scalability. ### Solution The SME collaborates with the Swedish TEF-Health node to develop a RAG model using frameworks like TensorFlow. Document collections are indexed with FAISS for efficient retrieval, and a pre-trained large language model is fine-tuned for clinical applications. The solution integrates scientific literature and clinical guidelines into the retrieval system, ensuring context-specific outputs. The final model is validated with TensorBoard for accuracy and relevance, with clear source references, and is prepared for deployment using Docker for scalability and secure handling. ### Implementation #### Ethical Agreement The SME enters into an ethical agreement with researchers from the Swedish TEF-Health node, ensuring that all data handling and model development comply with GDPR and Swedish national regulations, particularly regarding the use of sensitive clinical and scientific data. #### Data Preparation and Knowledge Integration The SME’s document collections, including clinical guidelines and scientific literature, are preprocessed for integration with the RAG model. This includes text cleaning, tokenization, and embedding generation using Hugging Face Transformers. The documents are indexed with FAISS to enable efficient and accurate information retrieval. #### Model Development and Fine-Tuning A pre-trained large language model is fine-tuned on the SME’s specific domain data using TensorFlow. The retrieval system is connected to the language model to ensure context-specific outputs. Recall@K, BLEU, and precision are used as metrics to validate the model’s accuracy and relevance during development. #### Validation and Optimization The integrated RAG model is validated using the evaluation frameworks TensorBoard, enabling real-time tracking of performance metrics and optimization of hyperparameters. This ensures the model is robust. #### Deployment Preparation The final RAG model is containerized using Docker to ensure scalability and ease of deployment. Advisory support is provided to the SME, including documentation and best practices for hosting and maintaining the model post-deployment. ### Benefits - **Insight Optimization:** Enhancing SMEs’ ability to deliver accurate, context-specific insights for molecular tumor boards. - **Credibility:** Fine-tuned and validated models build trust with stakeholders, including clinicians and regulators. - **Transparency & Compliance:** Clear source references foster trust and support ethical and legal standards. ### Impact The SME’s RAG model establishes itself as a reliable and cutting-edge tool in clinical decision support, gaining recognition within the oncology and AI research communities. The model’s ability to deliver accurate, context-aware insights with transparent references enhances trust among clinicians and regulatory bodies. This innovation fosters new collaborations with academic researchers and industry stakeholders, accelerating the adoption of AI-driven personalized treatment solutions and positioning the SME as a leader in advanced healthcare technologies.

Karolinska Institutet (KI)

The Unit: Eukaryotic Single Cell Genomics (https://www.scilifelab.se/units/eukaryotic-single-cell-genomics/) The 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. Single-cell genomics technologies are rapidly advancing and have proven to give new insights into cell type discovery and in the characterization of heterogeneity in tumors as well as in normal tissue. The Eukaryotic Single Cell Genomics (ESCG) unit aims at providing high-throughput single cell transcriptomics services through a streamlined and complete single-cell RNA sequencing service. The user provide us with single cells, we process the samples, sequence and deliver annotated gene expression data. • Study heterogeneity within putatively homogeneous cell populations • Unbiased discovery of cell types in complex tissues • Characterizing the cellular and genetic composition of tumors The service: Single Cell Multiomic profiling 10X Genomics (droplet-based) single cell RNA sequencing in combination with single cell immune profiling (VDJ), AND/OR in combination with cell surface protein detection, OR in combination with ATAC (Assay for Transposase Accessible Chromatin) to analyze thousands of unique open chromatin fragments genome-wide with single cell resolution. ESCG provides access to technology, end-to-end support from help with project planning, quality check of your sample, cDNA library preparation to analysis and associated bioinformatics support.

Karolinska Institutet (KI)

The Units: CRISPR Functional Genomics (CFG) (https://www.scilifelab.se/units/crispr-functional-genomics/) and Eukaryotic Single Cell Genomics (ESCG) (https://www.scilifelab.se/units/eukaryotic-single-cell-genomics/) Both infrastructures are 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. Both units, CFG and ESCG, have designed a pipeline of CRISPR pooled screens together with single cell RNASeq readout (Perturb-Seq, CROP-Seq). This allows to assess gene expression phenotypes at the single cell level following inactivation of a pool of genes, using Cas-9 expressing cells. Service span the entire pooled screening process, from Cas-cell line generation and library virus creation, via phenotypic selection by FACS or live/dead, to single cell library preparation, NGS and data analysis.

Karolinska Institutet (KI)

The Unit: Eukaryotic Single Cell Genomics (https://www.scilifelab.se/units/eukaryotic-single-cell-genomics/) The 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. Single-cell genomics technologies are rapidly advancing and have proven to give new insights into cell type discovery and in the characterization of heterogeneity in tumors as well as in normal tissue. The Eukaryotic Single Cell Genomics (ESCG) unit aims at providing high-throughput single cell transcriptomics services through a streamlined and complete single-cell RNA sequencing service. The user provide us with single cells, we process the samples, sequence and deliver annotated gene expression data. • Study heterogeneity within putatively homogeneous cell populations • Unbiased discovery of cell types in complex tissues • Characterizing the cellular and genetic composition of tumors The service: Single cell RNA sequencing (scRNA-seq) Depending on your sample and your need, we can offer single-cell RNA sequencing using SMART-Seq3 or 10X Genomics methods on live cells, and 10X Genomics FLEX assay on fixed cells (recommended for users outside Stockholm area). SMART-Seq3 alows full-length transcript information at a higher sensitivity compared to 10X Genomics technology (10X Genomics: generates data starting from 3' or 5' end of your transcript of interest). However, 10X Genomics has a higher cell throughput and lower cost per cell compared to Smart-seq3. Sequencing equipment: Illumina NextSeq or NovaSeq platforms. ESCG provides access to technology, end-to-end support from help with project planning, quality check of your sample, cDNA library preparation to analysis and associated bioinformatics support. Prospective Use case: Single cell transcriptomics can be used in the clinic to understand heterogeneity of cancer sub-clonal population, and potentially to follow how these different sub-clones respond differently to various therapeutic treatments. Therefore, AI algorithms trained on such datasets could help clinical decision-making process for molecular-targeted therapies.

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.