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

Sia Viborg LindskrogFrederik PripPhilippe LamyAnn TaberClarice S GroeneveldKarin Birkenkamp-DemtröderJørgen Bjerggaard JensenTrine StrandgaardIver NordentoftEmil ChristensenMateo SokacNicolai Juul BirkbakLasse MarettyGregers G HermannAstrid C PetersenVeronika WeyererMarc-Oliver GrimmMarcus HorstmannGottfrid SjödahlMattias HöglundTorben SteinicheKarin MogensenAurélien de ReynièsRoman NawrothBrian JordanXiaoqi LinDejan DragicevicDouglas G WardAnshita GoelCarolyn D HurstJay D RamanJoshua I WarrickUlrika SegerstenDanijel SikicKim E M van KesselTobias MaurerJoshua J MeeksDavid J DeGraffRichard T BryanMargaret A KnowlesTatjana SimicArndt HartmannEllen C ZwarthoffPer-Uno MalmströmNúria MalatsFrancisco X RealLars Dyrskjøt
Published in: Nature communications (2021)
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.
Keyphrases
  • muscle invasive bladder cancer
  • single cell
  • rna seq
  • clinical trial
  • genome wide
  • high throughput
  • machine learning
  • public health
  • gene expression
  • copy number
  • randomized controlled trial
  • clinical evaluation