Non-Coding Mutations in Urothelial Bladder Cancer: Biological and Clinical Relevance and Potential Utility as Biomarkers

Properties of non-coding mutation hotspots as urinary biomarkers for bladder cancer detection

Mutations at specific hotspots in non-coding regions of ADGRG6, PLEKHS1, WDR74, TBC1D12 and LEPROTL1 frequently occur in bladder cancer (BC). These mutations could function as biomarkers for the non-invasive detection of BC but this remains largely unexplored. Massively-parallel sequencing of non-coding hotspots was applied to 884 urine cell pellet DNAs: 591 from haematuria clinic patients (165 BCs, 426 non-BCs) and 293 from non-muscle invasive BC surveillance patients (29 with recurrence). Urine samples from 142 non-BC haematuria clinic patients were used to optimise variant calling. Non-coding mutations are readily detectable in the urine of BC patients and undetectable, or present at much lower frequencies, in the absence of BC. The mutations can be used to detect incident BC with 66% sensitivity (95% CI 58-75) at 92% specificity (95% CI 88-95) and recurrent disease with 55% sensitivity (95% CI 36-74) at 85% specificity (95% CI 80-89%) using a 2% variant allele frequency threshold. In the NMIBC surveillance setting, the detection of non-coding mutations in urine in the absence of clinically detectable disease was associated with an increased relative risk of future recurrence (RR = 4.62 (95% CI 3.75-5.48)). As urinary biomarkers, non-coding hotspot mutations behave similarly to driver mutations in BC-associated genes and could be included in biomarker panels for BC detection.

Identification of new driver and passenger mutations within APOBEC-induced hotspot mutations in bladder cancer

BACKGROUND

APOBEC-driven mutagenesis and functional positive selection of mutated genes may synergistically drive the higher frequency of some hotspot driver mutations compared to other mutations within the same gene, as we reported for FGFR3 S249C. Only a few APOBEC-associated driver hotspot mutations have been identified in bladder cancer (BCa). Here, we systematically looked for and characterised APOBEC-associated hotspots in BCa.

METHODS

We analysed 602 published exome-sequenced BCas, for part of which gene expression data were also available. APOBEC-associated hotspots were identified by motif-mapping, mutation signature fitting and APOBEC-mediated mutagenesis comparison. Joint analysis of DNA hairpin stability and gene expression was performed to predict driver or passenger hotspots. Aryl hydrocarbon receptor (AhR) activity was calculated based on its target genes expression. Effects of AhR knockout/inhibition on BCa cell viability were analysed.

RESULTS

We established a panel of 44 APOBEC-associated hotspot mutations in BCa, which accounted for about half of the hotspot mutations. Fourteen of them overlapped with the hotspots found in other cancer types with high APOBEC activity. They mostly occurred in the DNA lagging-strand templates and the loop of DNA hairpins. APOBEC-associated hotspots presented systematically a higher prevalence than the other mutations within each APOBEC-target gene, independently of their functional impact. A combined analysis of DNA loop stability and gene expression allowed to distinguish known passenger from known driver hotspot mutations in BCa, including loss-of-function mutations affecting tumour suppressor genes, and to predict new candidate drivers, such as AHR Q383H. We further characterised AHR Q383H as an activating driver mutation associated with high AhR activity in luminal tumours. High AhR activity was also found in tumours presenting amplifications of AHR and its co-receptor ARNT. We finally showed that BCa cells presenting those different genetic alterations were sensitive to AhR inhibition.

CONCLUSIONS

Our study identified novel potential drivers within APOBEC-associated hotspot mutations in BCa reinforcing the importance of APOBEC mutagenesis in BCa. It could allow a better understanding of BCa biology and aetiology and have clinical implications such as AhR as a potential therapeutic target. Our results also challenge the dogma that all hotspot mutations are drivers and mostly gain-of-function mutations affecting oncogenes.