Detection of the ADGRG6 hotspot mutations in urine for bladder cancer early screening by ARMS-qPCR.

BACKGROUND: In bladder cancer, recurrent ADGRG6 enhancer hotspot mutations (chr. 6: 142,706,206 G>A, chr. 6:142,706,209 C>T) were reported at a high mutation rate of approximately 50%. Thus, ADGRG6 enhancer mutation status might be a candidate for diagnostic biomarker. METHODS: To improve test efficacy, an amplification refractory mutation system combined with quantitative real-time PCR (ARMS-qPCR) assay was developed to detect the ADGRG6 mutations in a patient as a clinical diagnostic test. To validate the performance of the ARMS-qPCR assay, artificial plasmids, cell DNA reference standard were used as templates, respectively. To test the clinical diagnostic ability, we detected the cell free DNA (cfDNA) and sediment DNA (sDNA) of 30 bladder cancer patients' urine by ARMS-qPCR comparing with Sanger sequencing, followed by the droplet digital PCR to confirm the results. We also tested the urine of 100 healthy individuals and 90 patients whose diagnoses urinary tract infections or urinary stones but not bladder cancer. RESULTS: Sensitivity of 100% and specificity of 96.7% were achieved when the mutation rate of the artificial plasmid was 1%, and sensitivity of 96.7% and specificity of 100% were achieved when the mutation frequency of the reference standard was 0.5%. Sanger sequencing and ARMS-qPCR both detected 30 cases of bladder cancer with 93.3% agreement. For the remaining unmatched sites, ARMS-qPCR results were consistent with droplet digital PCR. Among 100 healthy individuals, three of them carried hotspot mutations by way of ARMS-qPCR. Of 90 patients with urinary tract infections or urinary stones, no mutations were found by ARMS-qPCR. Based on clinical detection, the ARMS-qPCR assay's sensitivity is 83.3%, specificity is 98.4%. CONCLUSION: We here present a novel urine test for ADGRG6 hotspot mutations with high accuracy and sensitivity, which may potentially serve as a rapid and non-invasive tool for bladder cancer early screening and follow-up relapse monitoring.

Prognostic Significance of Lineage Diversity in Bladder Cancer Revealed by Single-Cell Sequencing.

Bladder cancer is the most common malignant tumor of the urinary system. We investigated the clinical implications of cell lineages in bladder cancer by integrating single-cell and bulk transcriptome data. By investigating the single-cell transcriptional profiles of 12,424 cells from normal bladder, eleven cell types and five types of epithelial sub-population were identified. Based on the signature of cell types identified in single-cell profiles, deconvolution analysis was employed to estimate cell types and epithelial lineages in the bulk RNA sequencing bladder cancer cohort. Cancer subtypes with clinical implications were further identified based on the heterogeneity of the epithelial lineage across patients. This study suggests that the EMT-like subtype is robustly correlated with poor prognosis and the umbrella subtype is a positive factor for the patient survival. Our research has a high potential for accurate prognostic and therapeutic stratification of bladder cancer.

Multi-Omics Characterization of Tumor Microenvironment Heterogeneity and Immunotherapy Resistance Through Cell States-Based Subtyping in Bladder Cancer.

Due to the strong heterogeneity of bladder cancer (BC), there is often substantial variation in the prognosis and efficiency of immunotherapy among BC patients. For the precision treatment and assessment of prognosis, the subtyping of BC plays a critical role. Despite various subtyping methods proposed previously, most of them are based on a limited number of molecules, and none of them is developed on the basis of cell states. In this study, we construct a single-cell atlas by integrating single cell RNA-seq, RNA microarray, and bulk RNA-seq data to identify the absolute proportion of 22 different cell states in BC, including immune and nonimmune cell states derived from tumor tissues. To explore the heterogeneity of BC, BC was identified into four different subtypes in multiple cohorts using an improved consensus clustering algorithm based on cell states. Among the four subtypes, C1 had median prognosis and best overall response rate (ORR), which characterized an immunosuppressive tumor microenvironment. C2 was enriched in epithelial-mesenchymal transition/invasion, angiogenesis, immunosuppression, and immune exhaustion. Surely, C2 performed the worst in prognosis and ORR. C3 with worse ORR than C2 was enriched in angiogenesis and almost nonimmune exhaustion. Displaying an immune effective environment, C4 performed the best in prognosis and ORR. We found that patients with just an immunosuppressive environment are suitable for immunotherapy, but patients with an immunosuppressive environment accompanied by immune exhaustion or angiogenesis may resist immunotherapy. Furthermore, we conducted exploration into the heterogeneity of the transcriptome, mutational profiles, and somatic copy-number alterations in four subtypes, which could explain the significant differences related to cell states in prognosis and ORR. We also found that PD-1 in immune and tumor cells could both influence ORR in BC. The level of TGFß in a cell state can be opposite to the overall level in the tissues, and the level in a specific cell state could predict ORR more accurately. Thus, our work furthers the understanding of heterogeneity and immunotherapy resistance in BC, which is expected to assist clinical practice and serve as a supplement to the current subtyping method from a novel perspective of cell states.