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Overtreatment with cisplatin-based chemotherapy is a major issue in the management of muscle-invasive bladder cancer (MIBC), and currently none of the reported biomarkers for predicting response have been implemented in the clinic. Here we perform a comprehensive multi-omics analysis (genomics, transcriptomics, epigenomics and proteomics) of 300 MIBC patients treated with chemotherapy (neoadjuvant or first-line) to identify molecular changes associated with treatment response. DNA-based associations with response converge on genomic instability driven by a high number of chromosomal alterations, indels, signature 5 mutations and/or BRCA2 mutations. Expression data identifies the basal/squamous gene expression subtype to be associated with poor response. Immune cell infiltration and high PD-1 protein expression are associated with treatment response. Through integration of genomic and transcriptomic data, we demonstrate patient stratification to groups of low and high likelihood of cisplatin-based response. This could pave the way for future patient selection following validation in prospective clinical trials. There are currently only a few biomarkers to predict the response of muscle invasive bladder cancer to therapy. Here, the authors analyse 300 tumors using exome and RNA sequencing and find that tumors with a high degree of genomic instability and a non-basal/squamous gene expression subtype are most likely to respond to treatment.

The fragmentation patterns of whole genome sequenced cell-free DNA are promising features for tumor-agnostic cancer detection. However, systematic biases challenge their cross-cohort generalization. We introduce LIONHEART, an open source cancer detection method specifically optimized to generalize across datasets. The method correlates bias-corrected cfDNA fragment coverage across the genome with the locations of accessible chromatin regions from 898 cell and tissue type features. We use these correlations to detect changes in the cell-free DNA cell type composition caused by cancer. We test LIONHEART on nine datasets and fourteen cancer types (1106 non-cancer controls, 1449 cancers) obtained from different studies and show that it can distinguish cancer samples from non-cancer controls across cohorts with ROC AUC scores ranging from 0.62-0.95 (mean = 0.83, std = 0.12). We further validate the method on an external dataset, achieving a ROC AUC of 0.917. Fragmentation patterns of cell-free DNA are a promising biomarker source, however, correlations with different cancer types are heterogenous. Here, the authors develop LIONHEART to enable detection of 14+ cancer types from whole genome sequenced cell-free DNA.

Understanding the molecular landscape of nonmuscle-invasive bladder cancer (NMIBC) is essential to improve risk assessment and treatment regimens. We performed a comprehensive genomic analysis of patients with NMIBC using whole-exome sequencing ( n  = 438), shallow whole-genome sequencing ( n  = 362) and total RNA sequencing ( n  = 414). A large genomic variation within NMIBC was observed and correlated with different molecular subtypes. Frequent loss of heterozygosity in FGFR3 and 17p (affecting TP53 ) was found in tumors with mutations in FGFR3 and TP53 , respectively. Whole-genome doubling (WGD) was observed in 15% of the tumors and was associated with worse outcomes. Tumors with WGD were genomically unstable, with alterations in cell-cycle-related genes and an altered immune composition. Finally, integrative clustering of multi-omics data highlighted the important role of genomic instability and immune cell exhaustion in disease aggressiveness. These findings advance our understanding of genomic differences associated with disease aggressiveness in NMIBC and may ultimately improve patient stratification. A genomic and transcriptomic analysis of nonmuscle-invasive bladder cancer identifies four molecular subtypes, and associates whole-genome duplication and immune exhaustion with tumor progression.

Bladder field cancerization may be associated with disease outcome in patients with bladder cancer. To investigate this, we analyzed biopsies from bladder urothelium and urine samples by genomics and proteomics analyses. Samples were procured from multiple timepoints from 134 patients with early stage bladder cancer and detailed long term follow-up. We measured the field cancerization in normal-appearing bladder biopsies and found that high levels were associated with high tumor mutational burden, high neoantigen load, and high tumor-associated CD8 T-cell exhaustion. Non-synonymous mutations in known bladder cancer driver genes such as KDM6A and TP53 were identified as early disease drivers in normal urothelium. High field cancerization was associated with worse outcome but not with response to BCG. The level of urinary tumor DNA (utDNA) reflected the bladder tumor burden and originated from both tumors and field cancerization. High utDNA levels after BCG were associated with worse clinical outcomes for the patients. Our results indicate that the level of field cancerization may affect clinical outcome, tumor development and immune responses. utDNA measurements have significant prognostic value and reflect the disease status of the bladder.Competing Interest StatementLars Dyrskjot has sponsored research agreements with C2i, AstraZeneca, Natera, Photocure, and Ferring; has an advisory/consulting role at Ferring and UroGen; and is Chairman of the Board in BioXpedia A/S. Joergen Bjerggaard Jensen is proctor for Intuitive Surgery; is a member of advisory board for Olympus Europe, Cephaid, and Ferring; and has sponsored research agreements with Medac, Photocure ASA, Cephaid, and Ferring.

Background FGFR3 mutations are among the most frequent genetic alterations in bladder cancer and are enriched in the luminal papillary subtype of muscle-invasive tumors (MIBC) and luminal-like classes 1 and 3 of non-MIBC. To study their oncogenic properties in vivo, we developed here a genetically engineered mouse (GEM) model expressing the most frequent FGFR3 mutation, FGFR3-S249C, in urothelial cells. Methods Bladder tumorigenesis was monitored in FGFR3-S249C mice. FGFR3 expression was assessed by RT-qPCR in the transgenic mice urothelium and in various human epithelia. Transcriptomic data were obtained from mouse bladder tumors and cross-species comparisons were performed. Sex bias in FGFR3-mutated tumors was evaluated in our GEM model and in the TCGA and UROMOL cohorts of patients including 408 MIBC and 419 NMIBC, respectively. The association of androgen receptor (AR) activity, based on the expression of its target genes, with FGFR3 mutations was examined in these two cohorts. The binding of AR to its response element and AR phosphorylation in FGFR3-dependent cell lines were evaluated. Results FGFR3-S249C expression in the urothelium of mice induced spontaneous low-grade papillary bladder tumors resembling the human counterpart at the histological and transcriptomic levels. Mutant-FGFR3 expression levels impacted tumor formation incidence in mice and mutant-FGFR3-driven human tumors were restricted to epithelia presenting high normal expression levels of FGFR3. The known bladder cancer male gender bias, also found in our model, was even higher in human FGFR3-mutated compared to wild-type tumors and associated with a higher AR regulon activity considering gender adjustment. AR phosphorylation and regulon activity were modulated by FGFR3 in FGFR3-dependent models. Conclusions Mutant-FGFR3 is an oncogene per se, inducing bladder tumorigenesis. Patients with early-stage bladder lesions could thus potentially benefit from FGFR3 targeting. Our results also reinforce the interest in elucidating the role of AR in bladder carcinogenesis, specifically in FGFR3-mutated driven tumors. Finally, our results suggest FGFR3 expression level in the epithelium as a determinant for the FGFR3-driven tumors tissue specificity. Competing Interest Statement The authors have declared no competing interest.

This paper provides a laboratory workflow for single nucleus RNA-sequencing (snRNA-seq) including a protocol for gentle nuclei isolation from fresh frozen tumor biopsies, making it possible to analyze biobanked material. To develop this protocol, we used non-frozen and frozen human bladder tumors and cell lines. We tested different lysis buffers (IgePal and Nuclei EZ) and incubation times in combination with different approaches for tissue and cell dissection; sectioning, semi-automated dissociation, manual dissociation with pestles, and semi-automated dissociation combined with manual dissociation with pestles. Our results showed a combination of IgePal lysis buffer, tissue dissection by sectioning and short incubation time was the best conditions for gentle nuclei isolation applicable for snRNA-seq, and we found limited confounding transcriptomic changes based on the isolation procedure. This protocol makes it possible to analyze biobanked material from patients with well described clinical and histopathological information and known clinical outcomes with snRNAseq.Competing Interest StatementLars Dyrskjøt has sponsored research agreements with C2i, AstraZeneca, Natera, Photocure, and Ferring; has an advisory/consulting role at Ferring and UroGen; and is Chairman of the Board in BioXpedia A/S. Jørgen Bjerggaard Jensen is proctor for Intuitive Surgery; is a member of advisory board for Olympus Europe, Ambu, Cepheid, and Ferring; and has sponsored research agreements with Medac, Photocure ASA, Cepheid, and Ferring.Footnotes* The manuscript has been thoroughly revised.

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. Competing Interest Statement Lars Dyrskjøt has sponsored research agreements with C2i, AstraZeneca, Natera, Photocure, and Ferring; has an advisory/consulting role at Ferring and UroGen; and is Chairman of the Board in BioXpedia A/S. Jorgen Bjerggaard Jensen is proctor for Intuitive Surgery; is a member of advisory board for Olympus Europe, Ambu, Cepheid, and Ferring; and has sponsored research agreements with Medac, Photocure ASA, Cepheid, and Ferring.