An integrated multi-omics analysis identifies prognostic molecular subtypes of non-muscle-invasive bladder cancer

Research output: Contribution to journalArticlepeer-review

Authors

  • SV Lindskrog
  • FF Prip
  • P Lamy
  • A Taber
  • CS Groeneveld
  • K Birkenkamp-Demtröder
  • J Jensen
  • T Strandgaard
  • I Nordentoft
  • E Christensen
  • M Sokac
  • NJ Birkbak
  • L Maretty
  • GG Hermann
  • AC Petersen
  • V Weyerer
  • MO Grimm
  • M Horstmann
  • G Sjödahl
  • M Höglund
  • T Steiniche
  • K Mogensen
  • A de Reyniès
  • R Nawroth
  • B Jordan
  • X Lin
  • D Dragicevic
  • Carolyn D Hurst
  • JD Raman
  • JI Warrick
  • U Segersten
  • D Sikic
  • KEM van Kessel
  • T Maurer
  • JJ Meeks
  • DJ DeGraff
  • Margaret A Knowles
  • T Simic
  • A Hartmann
  • EC Zwarthoff
  • PU Malmström
  • N Malats
  • FX Real
  • Lars Dyrskjøt

Colleges, School and Institutes

Abstract

The molecular landscape in non-muscle-invasive bladder cancer (NMIBC) is characterized by large biological heterogeneity with variable clinical outcomes. Here, we perform an integrative multi-omics analysis of patients diagnosed with NMIBC (n = 834). Transcriptomic analysis identifies four classes (1, 2a, 2b and 3) reflecting tumor biology and disease aggressiveness. Both transcriptome-based subtyping and the level of chromosomal instability provide independent prognostic value beyond established prognostic clinicopathological parameters. High chromosomal instability, p53-pathway disruption and APOBEC-related mutations are significantly associated with transcriptomic class 2a and poor outcome. RNA-derived immune cell infiltration is associated with chromosomally unstable tumors and enriched in class 2b. Spatial proteomics analysis confirms the higher infiltration of class 2b tumors and demonstrates an association between higher immune cell infiltration and lower recurrence rates. Finally, the independent prognostic value of the transcriptomic classes is documented in 1228 validation samples using a single sample classification tool. The classifier provides a framework for biomarker discovery and for optimizing treatment and surveillance in next-generation clinical trials.

Details

Original languageEnglish
Article number2301
JournalNature Communications
Volume12
Publication statusPublished - 16 Apr 2021