Table 3.
Studies regarding circulating DNAs in blood as potential biomarkers for bladder cancer
| Authors (year) | Sample type | No. of patients | Laboratory technique | Clinical application | Detection rate | Refs. |
|---|---|---|---|---|---|---|
| Valenzuela et al. (2002) | Serum | 135 | Methylation-specific PCR | Diagnostic biomarker | AUC = 95%, sensitivity = 22.6%, specificity = 98% | [122] |
| Domínguez et al. (2002) | Plasma | 27 | 5–4520 kit, QIAamp Blood kit, PCR | Diagnostic biomarker | Detected in 40% patients | [123] |
| Ellinger et al. (2008) | Serum | 45 | Restriction endonuclease-based assay, qRT-PCR | Increase the accuracy of the diagnosis of BC | Sensitivity = 80%, specificity = 93% | [124] |
| Lin et al. (2011) | Serum | 168 | Methylation-specific PCR | Diagnostic biomarker | Detected in 30.7% patients, higher in advanced BC | [125] |
| Hauser et al. (2013) | Serum | 227 | Methylation-specific PCR | Discrimination of patients with BC from healthy individuals | Sensitivity = 62%, specificity = 89% | [126] |
| Vandekerkhove et al. (2017) | Plasma | 51 | Targeted and exome sequencing | Revealing aggressive mutations in metastatic BC | 95% of patients harboring deleterious alterations | [2] |
| Patel et al. (2017) | Plasma | 17 | TAm-Seq, WGS | Monitoring recurrence | Positive predict value = 100%, negative predict value = 85.7% | [128] |
| Birkenkamp-Demtröder et al. (2018) | Plasma | 60 | WES, ddPCR | Monitoring recurrence | Earlier recurrence detection compared with imaging | [129] |
| Christensen et al. (2019) | Plasma | 68 | WES, ultra-deep sequencing |
• Predict metastatic recurrence • Monitoring of therapeutic efficacy |
• Sensitivity = 100%, specificity = 98%; • Changes in ctDNA during chemotherapy in high-risk patients correlated with disease recurrence (p = 0.023) |
[130] |
| Birkenkamp-Demtröder et al. (2016) | Plasma | 12 | NGS, ddPCR | Predicts disease progression and residual disease | Disease progression (p = 0.032) | [131] |
| Vandekerkhove et al. (2021) | Plasma | 104 | WES, QIAGEN DNeasy Blood and Tissue Kit | Predict prognosis | OS (p = 0.01), PFS (p = 0.02) | [118] |
| Shohdy et al. (2022) | Plasma | 182 | NGS, WES | Predict disease progression | OS (p = 0.03) | [134] |
| Grivas et al. (2019) | Blood | 124 | Exon sequencing | Predict prognosis | OS (p = 0.07), FFS (p = 0.016) | [135] |
| Powles et al. (2021) | Plasma | 581 | WES, multiplex PCR |
• Predict recurrence • Predict treatment response |
• DFS (p < 0.0001) • ctDNA can be used as a marker for MRD to predict response to adjuvant immunotherapy |
[137] |
| Zhang et al. (2021) | Plasma | 82 | Targeted sequencing | Predict prognosis | DFS (p = 0.0146) | [138] |
| Sundahl et al. (2019) | Blood | 9 | RT-PCR | Response monitoring | Predicting treatment response in metastatic uroepithelial carcinoma prior to imaging | [139] |
| Khagi et al. (2017) | Blood | 69 | NGS | Predict treatment response | ctDNA-determined hypermutated states predict improved response, PFS, and OS after checkpoint inhibitor therapy | [141] |
| Raja et al. (2018) | Plasma | 29 | Targeted sequencing | Predict treatment response | Changes in the frequency of ctDNA variant alleles early in treatment were found to identify checkpoint inhibitor monotherapy non-responders | [142] |
| Ravi et al. (2022) | Blood | 45 | NGS | Treatment monitoring | Detection of one or more genomic alterations in ctDNA before and after ICI treatment is associated with tumor resistance in advanced uroepithelial carcinoma | [143] |
ctDNA circulating tumor deoxyribonucleic acid, AUC area under the receiver operating characteristics curve, WGS whole genome sequencing, WES whole-exome sequencing, NGS next-generation sequencing, PCR polymerase chain reaction, ddPCR droplet digital PCR, OS overall survival, PFS progression free survival, DFS disease free survival, FFS failure-free survival, MRD minimal residual disease, ICIs immune checkpoint inhibitors