Single-cell RNAseq and longitudinal proteomic analysis of a novel semi-spontaneous urothelial cancer model reveals tumor cell heterogeneity and pretumoral urine protein alterations

Abstract

Bladder cancer, one of the most prevalent malignancies worldwide, remains hard to classify due to a staggering molecular complexity. Despite a plethora of diagnostic tools and therapies, it is hard to outline the key steps leading up to the transition from high-risk non–muscle-invasive bladder cancer (NMIBC) to muscle-invasive bladder cancer (MIBC). Carcinogen-induced murine models can recapitulate urothelial carcinogenesis and natural anti-tumor immunity. Herein, we have developed and profiled a novel model of progressive NMIBC based on 10 weeks of OH-BBN exposure in hepatocyte growth factor/cyclin dependent kinase 4 (R24C) (Hgf-Cdk4R24C) mice. The profiling of the model was performed by histology grading, single cell transcriptomic and proteomic analysis, while the derivation of a tumorigenic cell line was validated and used to assess in vivo anti-tumor effects in response to immunotherapy. Established NMIBC was present in females at 10 weeks post OH-BBN exposure while neoplasia was not as advanced in male mice, however all mice progressed to MIBC. Single cell RNA sequencing analysis revealed an intratumoral heterogeneity also described in the human disease trajectory. Moreover, although immune activation biomarkers were elevated in urine during carcinogen exposure, anti-programmed cell death protein 1 (anti-PD1) monotherapy did not prevent tumor progression. Furthermore, anti-PD1 immunotherapy did not control the growth of subcutaneous tumors formed by the newly derived urothelial cancer cell line. However, treatment with CpG-oligodeoxynucleotides (ODN) significantly decreased tumor volume, but only in females. In conclusion, the molecular map of this novel preclinical model of bladder cancer provides an opportunity to further investigate pharmacological therapies ahead with regards to both targeted drugs and immunotherapies to improve the strategies of how we should tackle the heterogeneous tumor microenvironment in urothelial bladder cancer to improve responses rates in the clinic.

Single-cell sequencing technologies in bladder cancer research: Applications and challenges

“…Bladder tumors, adjacent normal mucosae scRNA-seq, scATAC -seq (Zhang et al, 2016;Sfakianos et al, 2020;Wang et al, 2021a;Kerzeli et al, 2021;Lai et al, 2021) The genetic characteristics of bladder cancer stem cells Human Bladder tumor and normal adjacent tissue scWES-seq, scDNA-seq, scATAC-seq (Li et al, 2012;Yang et al, 2017;Wang et al, 2021a) The composition of the bladder tumor microenvironment…”

“…To further investigate the tumor heterogeneity involved in the transition from NMIBC to MIBC, Kerzeli et al established a novel urothelial carcinoma model of Hgf-Cdk4R24C mice and identified eight heterogeneous cell clusters of urinary epithelium origin by utilising single-cell transcriptomic analyses ( Kerzeli et al, 2021 ). Unlike the urothelial cells in healthy bladders, there were urothelial cell clusters with highly proliferative or cancer stemness gene expression characteristics in carcinogens-induced bladder tumors.…”

Clinical implications of single cell sequencing for bladder cancer

“…Nine studies investigated this heterogeneity by single-cell analysis of BC tumors and normal adjacent tissue (NAT). They classified cells into clusters and showed their ancestral origin [ 31 – 36 , 38 , 39 , 56 ].…”

Elevated levels of MMP12 sourced from macrophages are associated with poor prognosis in urothelial bladder cancer

“…Analysis of single-cell RNA-sequencing data from a murine UBC model and human UBC patients were explored with the goal of identifying the putative source of the mRNA transcripts coding for selected proteins from the OLINK assay. Two sets of single-cell RNAsequencing data were downloaded and processed: unique molecular identifier (UMI) count matrices originating from bladders of a urothelial cancer mouse model [22] (GSE174182) and raw sequence reads derived from patient tumour material from the European Nucleotide Archive under the accession code PRJNA662018 [23]. To generate count matrices for patient samples, the raw sequences were aligned to the GRCh38 reference genome through CellRanger 2.2.0.…”