Multiplex immunofluorescence and histopathology dataset of cell‐cycle related proteins in renal cell carcinoma

Hazem Abdullah*, In Um, Grant D. Stewart, Alexander Laird, Kathryn Kirkwood, Chang Wook Jeong, Cheol Kwak, Kyung Chul Moon, TranSORCE Team, Tim Eisen, Elena Frangou, Anne Warren, Angela Meade, David J. Harrison

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Clear-cell renal cell carcinoma (ccRCC) accounts for the majority of kidney cancer diagnoses and exhibits widely variable clinical behaviour. The dataset described here was generated to support the discovery of robust biomarkers of tumour cell-cycle arrest and to inform the risk-stratified management of ccRCC. We assembled four independent cohorts including 480 patients from the UK arm of the SORCE adjuvant trial, 300 patients from a surgically treated series in Korea, 120 patients from a retrospective Scottish cohort, and a paired primary–metastatic cohort comprising 62 patients. Formalin-fixed paraffin-embedded nephrectomy specimens were processed for routine hematoxylin and eosin (H&E) histology, and for multiplex immunofluorescence (mIF). The mIF panels detect the cyclin-dependent kinase inhibitor p21CDKN1a, the DNA replication licencing factor MCM2, endoglin/CD105, Lamin B1 and nuclear DNA (Hoechst). Whole-slide images (WSIs) were acquired at high resolution, and artificial-intelligence pipelines were used to segment nuclei, classify individual cells into arrested phenotypes, and calculate the fraction of cells. Accompanying metadata include demographics, tumour stage, grade, Leibovich score, treatment arm (sorafenib/placebo), relapse events, and disease-free survival. All images and derived tables are released under a CC0 licence via the BioImage Archive, ensuring unrestricted reuse. This multi-cohort dataset provides a rich resource for studying cell-cycle arrest and proliferation markers, training image-analysis algorithms, and developing prognostic signatures in RCC.
Original languageEnglish
Article number27
Pages (from-to)1-10
Number of pages10
JournalData
Volume11
Issue number2
DOIs
Publication statusPublished - 1 Feb 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Renal cell carcinoma
  • p21 (CDKN1A)
  • MCM2
  • Cell-cycle arrest
  • Multiplex immunofluoresence
  • H&E
  • Histopathology
  • Dataset

Fingerprint

Dive into the research topics of 'Multiplex immunofluorescence and histopathology dataset of cell‐cycle related proteins in renal cell carcinoma'. Together they form a unique fingerprint.

Cite this