Cancer

resources

96 resources

  • 4.UNCAN.EU

    A Coordination and Support Action to prepare UNCAN.eu platform
    The 15-month coordination and support action “4.UNCAN.eu” will generate a strategic agenda to launch UNCAN.eu a European initiative to UNderstand CANcer proposed by the Mission Board and the European Beating Cancer Plan. This research agenda will be built with the final aim of achieving a new breakthrough in cancer prevention and treatment that will contribute to saving European citizens’ lives and help ensuring an optimal quality of life to disease survivors.

  • AACR GENIE

    AACR Project GENIE: Powering Precision Medicine
    Precision medicine requires an end-to-end learning health care system, wherein the treatment decisions for patients are informed by the prior experiences of similar patients. Oncology is currently leading the way in precision medicine because the genomic and other molecular characteristics of patients and their tumors are routinely collected at scale. A major challenge to realizing the promise of precision medicine is that no single institution is able to sequence and treat sufficient numbers of patients to improve clinical-decision making independently. To overcome this challenge, the AACR launched Project GENIE (Genomics Evidence Neoplasia Information Exchange).  AACR Project GENIE® is a publicly accessible cancer registry of real-world clinico-genomic data assembled through data sharing between 19 leading international cancer centers. Through the efforts of strategic partners Sage Bionetworks and cBioPortal, the registry aggregates, harmonizes, and links clinical-grade, next-generation cancer genomic sequencing data with clinical outcomes obtained during routine medical practice from nearly every cancer patient treated at these institutions. The consortium and its activities are driven by openness, transparency, and inclusion, ensuring that the project output remains accessible to the global cancer research community for the benefit of all. We see the GENIE data providing another knowledge turn in the virtuous cycle of research, accelerating the pace of drug discovery, improving clinical trial design, and ultimately benefiting cancer patients globally.

  • AIDAVA

    AI powered Data Curation & Publishing Virtual Assistant
    Integrated, high-quality personal health data (PHD) represents a potential wealth of knowledge for healthcare systems, but there is no reliable conduit for this data to become interoperable, AI-ready and reuse-ready at scale across institutions, at national and EU level. AIDAVA will fill this gap by prototyping and testing an AI-powered, virtual assistant maximizing automation of data curation & publishing of unstructured and structured, heterogeneous data. The assistant includes a backend with a library of AI-based data curation tools and a frontend based on human-AI interaction modules that will help users when automation is not possible, while adapting to users preferences. The interdisciplinary team of the consortium will develop and test two versions of this virtual assistant with hospitals and emerging personal data intermediaries, around breast cancer patient registries and longitudinal health records for cardio-vascular patients, in three languages. The team will work around four technology pillars: 1) automation of quality enhancement and FAIRification of collected health data, in compliance with EU data privacy; 2) knowledge graphs with ontology-based standards as universal representation, to increase interoperability and portability; 3) deep learning for information extraction from narrative content; and 4) AI-generated explanations during the process to increase users confidence.

  • AIS-CaP

    Interpretable Artificial Intelligence across Scales for Next-Generation Cancer Prognostics
    Computation pathology has the potential to revolutionize cancer care and research, specifically through improving assessment of patient prognosis and treatment selection by applying advanced machine learning methods to digitized tissue sections, i.e. whole-slide images (WSIs). This will allow us to replace the current state-of-the-art of human-developed cancer grading systems. However, the field is currently hindered by significant knowledge gaps: we do not know how to effectively leverage both global and local information in WSIs, how to identify pan-cancer prognostic features, and how to make machine learning models explainable and interpretable. In this project, I will address these key knowledge gaps by building on the novel stochastic streaming gradient descent developed in my group. Specifically, I will integrate innovative multi-task and cross-task learning algorithms with SSGD. Furthermore, I will leverage the latest advances in self-supervision, self-attention and natural language processing to endow deep neural networks with unprecedented transparency and explainability. Last, the project will validate our developed methodology in the largest dataset of oncological WSIs in the world, and, for the first time, identify links between morphological prognostic features and genetic features. By publicly releasing all developed tools and data, the proposed project will have a scientific multiplier effect for the fields of oncology, computational pathology and machine learning. Specifically, the derived cancer-specific and pan-cancer biomarkers can be leveraged in clinical care and cancer research, the enhanced SSGD method for other tasks in computational pathology and our novel multi-task and explainability algorithms can impact other research areas in machine learning, such as remote sensing and self-driving cars.

  • BEACON

    Cancer Care Beacon - Reducing Disparities Across the European Union
    Disparities in cancer care have a big impact on survival rates, as well as on the quality of life and mental health of patients and their families. Income, education, location and ethnicity are some of the most important factors affecting quality of care in Europe. Healthcare costs, treatment options, integration of research and innovation into care and access to information and multidisciplinary care teams are other areas to target in addressing disparities. To facilitate delivery of higher-quality cancer care, BEACON aims to identify the reasons for and reduce disparities in care across Europe. Specifically, it will: map and increase the capacity and capabilities of cancer treatment centres in EU Member States; help patients to find the best treatment options, healthcare providers to share resources and expertise, researchers to share data on disparities, and policymakers to align funding with patients' priorities; improve training and ensure that accreditation of care providers reflects quality of care.

  • Bigpicture

    A central repository of digital pathology slides to boost the development of artificial intelligence
    Bigpicture will set up the first European, ethical- and General Data Protection Regulation-compliant, quality-controlled and community-based platform, in which both large-scale data and Artificial Intelligence (AI) algorithms will coexist. Bigpicture aims to: Develop a sustainable secure and scalable infrastructure to store pathology data. Collect >3 million nonclinical and clinical high-quality pathology images with associated technical and biological information. Develop tools to enable and enhance the use of the repository, such as morphological search tools. Develop generic AI building blocks to promote the development of AI models. Advance the regulatory, legal and ethical framework around AI in non-clinical safety testing and clinical use.

  • BUMPER

    Boosting the usability of the EU mobile app for cancer prevention
    BUMPER aims at interfacing with the action that will design and programme the EU Mobile App itself and provide guidance on the content and focus of the App, contributing to ensuring its scientific validity.

  • can.HEAL

    Can.Heal EU
    The project aims to set the framework for integrating and aligning the Genome of Europe biobanking initiative into public health genomics for cancer.

  • CANCER-RADIOMICS

    Deep Learning for Automated Quantification of Radiographic Tumor Phenotypes
    Artificial Intelligence (AI), deep-learning in particular, is propelling the field of radiology forward at a rapid pace. In oncology, AI can characterize the radiomic phenotype of the entire tumor and provide a non-invasive window into the internal growth patterns of a cancer lesion. This is especially important for patients treated with immunotherapy as, despite the remarkable success of these novel therapies, the clinical benefit remains limited to a subset. As immunotherapy is expensive and could bring unnecessary toxicity there is a direct need to identify beneficial patients, but this remains difficult in clinical practice today. Radiomic biomarkers could address this, as, unlike biopsies that only represent a sample within the tumor, radiomics can depict a full picture of each cancer lesion with a single non-invasive examination. Previous work found significant connections between radiomic data, molecular pathways, and clinical outcomes. However, a direct link between radiomics and immunotherapy response has not yet been established. This project will address this problem by analyzing unique multicentre clinical data, including non-invasive imaging, clinical outcomes, and extensive biologic characterization of patients with lung or melanoma cancer. Specifically, I will develop deep-learning radiomic biomarkers to predict immunotherapy response using baseline (WP1) and follow-up imaging (WP2). I will also investigate if radiomics can characterize underlying biological factors, and, in turn, can be used to improve response predictions (WP3). Successful completion of this proposal will demonstrate the potential of radiomics to help physicians in selecting patients who will likely benefit from immunotherapy, while sparing this expensive and potentially toxic treatment for patients who don't. This work has implications for the use of imaging-based biomarkers in the clinic, as they can be applied noninvasively, repeatedly, and at low additional cost.

  • CancerModels.Org

    CancerModels.Org
    CancerModels.Org is the largest open-source and community-driven platform that aggregates, standardises, and integrates the complex and diverse data associated with patient-derived cancer models (PDCMs) using FAIR principles. It provides a unified point for PDCM stakeholders, from basic and clinical researchers to bioinformaticians and tool developers, to search and compare over 8300 PDCMs and associated data (as of March 2024), including frequently mutated genes, diagnoses, drug treatments and sequence data from patient-derived xenografts (PDXs), organoids, and cell lines. The project is driving the development of and promoting the use of descriptive standards to facilitate data interoperability and promote global sharing of models (e.g. PDX-MI standard). We provide expertise and software components to support several worldwide consortia including PDXNet and EurOPDX. The project is supported by NCI and is freely available under an Apache 2.0 licence (https://github.com/PDCMFinder). Users can search for models via a web interface or the REST API and explore molecular data summaries for models of specific cancer types. The data types include gene expression, gene mutation, CNA, biomarkers, imaging, patient treatment, and drug dosing studies. All data is available via cBioPortal. Moreover, the knowledge is enriched with links to external resources - publication platforms, cancer-specific annotation tools (e.g. COSMIC, CIViC, OncoMX, OpenCRAVAT, ClinGen), and raw data archives (EGA, dbGAP, GEO). CancerModels.Org is open-source and community-driven hence enabling researchers from various backgrounds and institutions to access and contribute to the shared knowledge pool and increase visibility and reusability of their models. This fosters collaboration across disciplines and geographies, breaking down traditional barriers to information and supporting the broader goals of global health equity in addition to accelerating cancer research and developing personalised treatments.

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