Skip to content
Review Open access

Worldwide Innovative Network Consortium: Building a Common Global Cancer Database

Aug 2026 · JCO Global Oncology · Vol 12 · 0 citations · 84 references
Medicine

TL;DR

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.

Abstract

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.

Read PDF

Similar papers

Review Open access 2026

Personalized Medicine in Oncology: Current Status and Future Prospects

The biological rationale rooted in the genomic basis of cancer and the concept of oncogene addiction is outlined, and the enabling technologies - next-generation sequencing, comprehensive genomic profiling, large reference atlases, and liquid biopsy - are described that have made molecular characterization feasible in...

Mohd Faizan Siddiqui · 0 citations
Review Open access Sep 2026

Integrating single-cell and spatial multi-omics for precision oncology: from tumor ecosystems to clinical decision-making

This conceptual workflow provides a stepwise translational roadmap from multi-omics discovery to clinical implementation, and is important to note that many of these applications remain at an early stage, with most evidence derived from small cohorts, preclinical models, or retrospective analyses, and prospective clini...

Sowhanur Rahman Nirob, Priyanshu Das, Md. Owafeeuzzaman Patwary et al. · 0 citations
Review Open access Sep 2026

Beyond molecular classification: integrating signaling, subcellular, and immune reprogramming in endometrial cancer

This narrative review synthesizes current evidence across several interconnected dimensions of EC biology and proposes an integrated conceptual model in which developmental signaling, subcellular protein trafficking, epigenetic and stemness reprogramming, and immune remodeling are mechanistically coupled, rather than i...

Miłosz Pietrus, Marcin Waligóra, M. Socha et al. · 0 citations
Review 2026

Emerging biomarkers in breast cancer.

C. J. Vellan, Kartthigeen Tamel Selvan, J. J. Jayapalan · 0 citations
Open access Mar 2026

Pan-Cancer Metabolic Landscapes: A Multiomics View

Metabolic reprogramming fuels cancer progression, but whether common metabolic patterns exist across tumor types remains elusive. To address this, we developed parseMetab, an R package that integrates large-scale proteomic, transcriptomic, and spatial transcriptomic datasets from 24 human cancers and 3,226 samples to m...

Yi-Guang Bai, Qiao-Ling Chen, Yuan Li · 0 citations
Review Open access Aug 2026

A multi-layer framework for computational cancer epigenomics

A structured multi-layer interpretation framework is proposed that links computational outputs across data-level processing, epigenomics-informed integrative regulatory modeling, and multi-omics-informed clinical interpretation, enabling traceable and mechanistically interpretable clinical inference.

M. Srivastava, Pratik Kumar, Ankita Chouhan et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.