bioRxivCancer Biology
Single nucleus and spatially resolved intra-tumor subtype heterogeneity in bladder cancer
S. V. Lindskrog, S. S. Schmokel, I. Nordentoft, P. Lamy, M. Knudsen, F. Prip, T. Strandgaard, J. B. Jensen, L. Dyrskjot
Abstract
Current transcriptomic classification systems for bladder cancer do not consider the level of intra-tumor subtype heterogeneity. Here we present an investigation of the extent and possible clinical impact of intra-tumor heterogeneity across early and more advanced disease stages of bladder cancer. We performed single nucleus RNA-sequencing of 48 bladder tumors and four of these tumors were additionally analyzed using spatial transcriptomics. Total bulk RNA-sequencing and spatial proteomics data were available from the same tumors for comparison, along with detailed clinical follow-up of the patients. We demonstrate that tumors display varying levels of intra-tumor subtype heterogeneity and show that a higher class 2a weight estimated from bulk RNA-sequencing data is associated with worse outcome in patients with molecular high-risk class 2a tumors. Our results indicate that discrete subtype assignments from bulk RNA-sequencing data may lack biological granularity and continuous class scores could improve clinical risk stratification of patients. HighlightsO_LISingle nucleus RNA-sequencing of tumors from 48 bladder cancer patients. C_LIO_LITumors display varying levels of intra-tumor subtype heterogeneity at single nucleus and bulk tumor level. C_LIO_LIThe level of subtype heterogeneity could be estimated from both single nucleus and bulk RNA-sequencing data with a high concordance between the two. C_LIO_LIHigh class 2a weight estimated from bulk RNA-sequencing data is associated with worse outcome in patients with molecular high-risk class 2a tumors. C_LI
- organism: human
- assay: scRNA-seq
- assay: spatial transcriptomics
- assay: bulk RNA-seq
- assay: proteomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: clustering
- approach: deep learning
- approach: statistical model
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