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Worldwide Innovative Network Consortium: Building a Common Global Cancer Database

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56Institutions déclarées
17Pays d’affiliation déclarés

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This review shares the ongoing work of the global Worldwide Innovative Network (WIN) Consortium for Precision Medicine to synthesize emerging cancer treatment data and to define the requirements for a common global cancer database that can truly support precision oncology. We performed a narrative review of emerging cancer treatment data, molecular profiling technologies, and existing clinicogenomic databases, focusing on how tumors are characterized, how subgroups are defined, and how demographic, lifestyle, and environmental factors are captured. The growth in molecular profiling technologies and the development of new targeted therapies are transforming cancer care. Tumors, regardless of tissue origin, are increasingly defined as composites of multiple, often rare, subgroups, each with distinct biology and likely response to specific therapies, based on multidimensional profiling of the tumor and its microenvironment. The solution lies in building vast databases that capture racial and ethnic diversity, reflected in genomic data, as well as diet and lifestyle factors that may have epigenetic impact on gene expression and post-translational modifications. A truly inclusive and informative data set must reflect global diversity, and there are multiple examples of demography-dependent differences in genomic signals. With members caring for and studying patients with cancer across five continents, WIN is actively exploring pathways to create a global cancer database, rich in clinical and molecular detail, granular enough for precise analysis, and large enough to power artificial intelligence-driven insights, provided appropriate data quality, validation, and governance frameworks are in place. This review surveys the current landscape and outlines practical paths forward to achieve this goal.

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Contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
Worldwide Innovative Network Consortium: Building a Common Global Cancer Database
Date Crossref
01/08/2026
Éditeur
American Society of Clinical Oncology (ASCO)
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.

Institutions déclarées

Jewish General HospitalBrown UniversityWorldwide Universities NetworkHuntsman Cancer InstituteMedical College of WisconsinThe University of SydneyChildren's Medical Research InstituteUNSW SydneySt Vincent's Hospital MelbourneThe Kinghorn Cancer CentreTel Aviv UniversitySheba Medical CenterAvera HealthBen-Gurion University of the NegevJackson LaboratoryCancer Hospital and Research InstituteFudan University Shanghai Cancer CenterSir H.N. Reliance Foundation Hospital and Research CentreHarvard UniversitySanford Burnham Prebys Medical Discovery InstituteDana-Farber Cancer InstituteRas al-Khaimah Medical and Health Sciences UniversityCentre Hospitalier de LuxembourgUniversity of LuxembourgLuxembourg Institute of HealthKing Hussein Cancer CenterNational Center for Cancer Care and ResearchHamad Medical CorporationLyon 1 UniversitéCentre National de la Recherche ScientifiqueInsermCentre Léon BérardCentre de Recherche en Cancérologie de LyonUniversitat de ValènciaInstituto de Salud Carlos IIIINCLIVA Health Research InstituteCentro de Investigación Biomédica en Red de CáncerInstitut Català d'OrnitologiaInstitut Català d'OncologiaSechenov UniversityHomi Bhabha National InstituteIuliu Hațieganu University of Medicine and PharmacyAcademia Oamenilor de Știință din RomâniaRutgers, The State University of New JerseyHebron UniversityVall d'Hebron Institut de RecercaVall d'Hebron Hospital UniversitariVall d'Hebron Institute of OncologyWake Forest UniversityUniversitat de Vic - Universitat Central de CatalunyaDuran i Reynals HospitalClinical Research OrganizationMcGill University Health CentreBeth Israel Deaconess Medical CenterUniversity of VictoriaHomewood Research Institute

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Sujets associés

Cancer Genomics and DiagnosticsFerroptosis and cancer prognosisRadiomics and Machine Learning in Medical Imaging

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