Detection and Surveillance of Bladder Cancer Using Urine Tumor DNA

Abstract

Current regimens for the detection and surveillance of bladder cancer are invasive and have suboptimal sensitivity. Here, we present a novel high-throughput sequencing (HTS) method for detection of urine tumor DNA (utDNA) called utDNA CAPP-Seq (uCAPP-Seq) and apply it to 67 healthy adults and 118 patients with early-stage bladder cancer who had urine collected either prior to treatment or during surveillance. Using this targeted sequencing approach, we detected a median of 6 mutations per patient with bladder cancer and observed surprisingly frequent mutations of the PLEKHS1 promoter (46%), suggesting these mutations represent a useful biomarker for detection of bladder cancer. We detected utDNA pretreatment in 93% of cases using a tumor mutation-informed approach and in 84% when blinded to tumor mutation status, with 96% to 100% specificity. In the surveillance setting, we detected utDNA in 91% of patients who ultimately recurred, with utDNA detection preceding clinical progression in 92% of cases. uCAPP-Seq outperformed a commonly used ancillary test (UroVysion, P = 0.02) and cytology and cystoscopy combined (P ≤ 0.006), detecting 100% of bladder cancer cases detected by cytology and 82% that cytology missed. Our results indicate that uCAPP-Seq is a promising approach for early detection and surveillance of bladder cancer. SIGNIFICANCE: This study shows that utDNA can be detected using HTS with high sensitivity and specificity in patients with early-stage bladder cancer and during post-treatment surveillance, significantly outperforming standard diagnostic modalities and facilitating noninvasive detection, genotyping, and monitoring.This article is highlighted in the In This Issue feature, p. 453.

Figure 1:. Schematic and validation of workflow for uCAPP-Seq.

(A) Workflow for uCAPP-Seq. Voided urine specimens were centrifuged and the cellular fraction was submitted for cytologic evaluation or other analyses. utDNA was extracted from the supernatant using a resin-based extraction protocol and then subjected to enzymatic fragmentation, library preparation, and hybrid capture with a panel optimized for bladder cancer. After next-generation sequencing, reads were processed through a bioinformatic pipeline consisting of adapter trimming, quality filtering, BWA-MEM based mapping, and variable read-length barcode-based deduplication. (B) A ~311kb hybrid capture panel was designed for bladder cancer targeting recurrently mutated regions identified in the literature, covering a median of 7 mutations per patient in the 2017 TCGA dataset on urothelial carcinoma (n=412) and 6 mutations per patient across 81 tumor and utDNA samples in this study. (C) Across samples with early-stage bladder cancer and paired tumor available for genotyping (n=18), a median of 73% of utDNA mutations were identified in paired tumor and 67% tumor mutations were identified in utDNA. (D) Tumor mutations also identified in urine had a higher median allele fraction (27% vs. 9%, p<0.0001) than those not identified in urine. The p-value was calculated by the Mann Whitney test. TCGA, The Cancer Genome Atlas; CAPP-Seq, Cancer Personalized Profiling by Deep Sequencing. BLCA, bladder cancer; BWA-MEM, Burrows-Wheeler Aligner with maximal exact matches.

Figure 2:. Genetic findings across bladder cancers profiled in study.

(A) Spectrum of genetic mutations and copy-number changes observed across 81 tumor and utDNA cases in this study, with clinicopathologic correlates. All tumor cases and all utDNA cases from patients with active cancer and at least one variant detected by genotyping were included in this analysis. All genes mutated in ≥ 10% of cases are shown, as well as all genes evaluated for copy-number variants. (B) Distribution of mutations in the TERT and PLEKHS1 promoters. (C) Comparison of mutations across high vs. low grade bladder cancers profiled in this study. (D) Enrichment of the APOBEC mutational signature in the cfDNA of patients with active bladder cancer versus healthy controls. P-values were calculated by multivariate regression controlling for total mutation count, median deduplicated sequencing depth, and the interaction between the two. APOBEC, apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like.

Figure 3:. Application of uCAPP-Seq to detect early-stage bladder cancer.

(A) Distribution of putative driver mutations identified in utDNA using tumor naïve profiling across patients with biopsy-proven bladder cancer (n=54) and controls (n=34), with associated tumor grade and cytology result. (B) Receiver operating characteristic analysis of tumor-informed profiling (n=27 cases, 34 controls), tumor-naive profiling (n=54 cases, 34 controls), and cytology (n=50 cases, 18 controls). (C) Correlates of detection by tumor-naive profiling among bladder cancer cases (n=54). P-values were calculated by the N-1 Chi Square test for comparing proportions. (D) Correlates of utDNA levels (haploid genome equivalents per mL, hGE/mL) among bladder cancer cases (n=54). P-values were calculated by the Mann Whitney test. SNVs, single-nucleotide variants; CNVs, copy-number variants; Sn, sensitivity; Sp, specificity; AUC, area under the curve; utDNA, urinary tumor DNA.

Figure 4:. Application of uCAPP-Seq to detect residual disease in the surveillance setting.

(A) Distribution of putative driver mutations identified in urine cfDNA using tumor naïve profiling across cases that developed recurrent cancer (n=37) and cases with at least 9 months of negative clinical follow-up (n=27), with recurrent cancer defined by biopsy (32 cases) or alternative clinical evidence (5 cases), as specified in Supplemental Table S12. (B) Receiver operating characteristic analysis of tumor-naive profiling (n=37 cases and 27 controls) and tumor-informed profiling (n=11 cases and 11 controls) across surveillance group. (C) Comparison of the sensitivities of cytology (n=37), cystoscopy (n=32), cytology plus cystoscopy (n=32), UroVysion (n=7), and tumor-naive profiling (n=37) in detecting residual BLCA. (D) Correlates of sensitivity for detecting disease by tumor-naive profiling. P-values for (C) and (D) were calculated by the N-1 Chi Square test for comparing proportions. (E) Kaplan-Meier analysis of recurrence-free survival stratified by utDNA detection by tumor-naive and tumor-informed profiling (HR 8.8 and 27.3), respectively and (F) by cytology (HR 4.6). P-values and HR were calculated by the log-rank test. (G) Example of patient detected by tumor-naive profiling but missed by cystoscopy, cytology, and UroVysion who later was diagnosed with muscle-invasive bladder cancer, requiring a radical cystectomy. SNVs, single-nucleotide variants; CNVs, copy-number variants; Sn, sensitivity; Sp, specificity; AUC, area under the curve; MRD, minimal residual disease; utDNA, urinary tumor DNA.