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  1. Home
  3. A Central Research Portal For Mining Pancreatic Clinical And Molecular Datasets And Accessing Biobanked Samples.
  1. Home
  3. A Central Research Portal For Mining Pancreatic Clinical And Molecular Datasets And Accessing Biobanked Samples.

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A central research portal for mining pancreatic clinical and molecular datasets and accessing biobanked samples.

Jorge Oscanoa1, Helen Ross-Adams1, Abu Z M Dayem Ullah2

  • 1Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer Institute, Queen Mary University London, EC1M 6BQ, UK.

Translational Oncology
|October 4, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

The Pancreas Genome Phenome Atlas (PGPA) offers a dynamic hub for analyzing pancreatic cancer -omics data. It integrates public datasets and patient tissues to accelerate biomarker discovery and precision medicine for pancreatic cancer.

Keywords:
BiomarkersGenomicsPancreas biobankTranscriptomicsTranslational

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Pancreatic cancer research requires integrated analysis of complex -omics data.
  • Publicly available datasets are fragmented, hindering comprehensive analysis.
  • Access to patient-derived tissues is crucial for validating computational findings.

Purpose of the Study:

  • To introduce the Pancreas Genome Phenome Atlas (PGPA) as a unified resource for pancreatic cancer -omics data analysis.
  • To demonstrate the utility of PGPA in interpreting biological insights from integrated datasets.
  • To facilitate hypothesis testing and validation using public data and patient tissues.

Main Methods:

  • Integrated diverse -omics datasets from TCGA, ICGC, CCLE, and GEO (n=7760 specimens).
  • Developed user-friendly analytical tools for data exploration and integration.
  • Established PGPA as a data access point for the Pancreatic Cancer Research Fund Tissue Bank, incorporating extensive multi-modal data (>125,000 specimens).

    • Main Results:

    • Demonstrated PGPA's utility by linking somatic variants to transcriptomic subtypes and prognosis.
    • Identified clinically actionable, patient-specific variants for precision medicine applications.
    • Showcased the potential for in silico findings to be validated using patient-derived material.

    Conclusions:

    • PGPA serves as a leading analytical resource for pancreatic cancer biomarker research.
    • The platform empowers researchers to test hypotheses, validate findings, and access patient tissues.
    • PGPA accelerates translational research by bridging in silico analysis with clinical validation.