Evidence synthesis

We searched published research articles using the keywords “liquid biopsy”, “translational research” and urological cancers (including “PCa,” “BCa,” and “RCC”) in the PubMed and Embase databases. To refine the search and make it comprehensive, “liquid biopsy” was replaced by “CTC,” “ctDNA,” “cfDNA,” “EV” and “exosome.” A total of 57 results were found. After review of the abstracts and full articles, 10 original research articles that were confirmed to be clinical translational research studies were ultimately included in this review (Table 1). In addition, 13 original research articles reported the translational potential of liquid biopsies.

Table 1.

Summary of clinical translational studies of liquid biopsy for PCa

ACCEPT, automated CTC Classification Enumeration and PhenoTyping; cfDNA, cell-free DNA; CTC, circulating tumor cell; ddPCR, droplet digital PCR; EV, extracellular vesicle; mCRPC, metastatic castrate-resistant prostate cancer.

Enumeration and assessment of CTCs

CTCs are cancer cells separated from the primary tumor or metastatic foci. CTCs are rare; at most, one tumor cell can be found in a hundred million cells circulating in the blood (Nelson, Reference Nelson2010). Given the broad range of technologies used to capture CTCs, CTC detection has become more efficient; thus, various clinical translational studies of CTCs have been conducted. Presently, CellSearch (Menarini Silicon Biosystems) is the only clinical CTC enumeration method that has received Food and Drug Administration (FDA) approval (Riethdorf et al., Reference Riethdorf, O’Flaherty, Hille and Pantel2018).

The number of CTCs in blood determined by flow cytometry might predict the prognosis of metastatic castration-resistant prostate cancer (mCRPC). Higher CTC counts were associated with worse radiographic progression-free survival and OS according to the results of translational research (Di Lorenzo et al., Reference Di Lorenzo, Zappavigna, Crocetto, Giuliano, Ribera, Morra, Scafuri, Verde, Bruzzese, Iaccarino, Costabile, Onofrio, Viggiani, Palmieri, De Placido, Marretta, Pietroluongo, Luce, Abate, Navaeiseddighi, Caputo, Celentano, Longo, Ferro, Morelli, Facchini, Caraglia, De Placido and Buonerba2021). Similarly, for patients with mCRPC starting abiraterone acetate or enzalutamide therapy, higher CTCs at baseline and an increase in CTCs at follow-up were independent predictors of worse prognosis (De Laere et al., Reference De Laere, Oeyen, Van Oyen, Ghysel, Ampe, Ost, Demey, Hoekx, Schrijvers, Brouwers, Lybaert, Everaert, Van Kerckhove, De Maeseneer, Strijbos, Bols, Fransis, Beije, de Kruijff, van Dam, Brouwer, van Dam, Van den Eynden, Rutten, Sleijfer, Vandebroek, Van Laere and Dirix2018). A few years later, the same group reanalyzed the previous images using an image analysis tool called the automated CTC Classification Enumeration and PhenoTyping (ACCEPT) tool (Oeyen et al., Reference Oeyen, Liégeois, De Laere, Buys, Strijbos, Dirix and Dirix2021). The results showed that beyond enumeration, high CTC phenotypic heterogeneity was also related to worse survival outcome in mCRPC. With regard to the CTC phenotype, in 2016, Lindsay et al. established a routine for the clinical testing of Ki67 and vimentin expression in CTCs and demonstrated that the presence of Ki67 and vimentin expression in CTCs was related with poor outcome in mCRPC (Lindsay et al., Reference Lindsay, Le Moulec, Billiot, Loriot, Ngo-Camus, Vielh, Fizazi, Massard and Farace2016). Peripheral blood (7.5 mL) from patients with mCRPC was collected and tested for Ki67 and vimentin. The results showed a significant reduction in OS in patients with Ki67- or vimentin-positive CTCs. It is the presence of expression but not the exact number or proportion of Ki67- or vimentin-positive CTCs which was associated with OS. In addition to mCRPC, Mandel et al. focused on oligometastatic hormone-sensitive prostate cancer (HSPC), an earlier stage of PCA. For patients with oligometastatic HSPC who underwent cytoreductive radical prostatectomy, higher CTC numbers both before and 6 months after surgery indicated shorter PFS to CRPC. Interestingly, with regard to the prognostic value, CTC number was a better biomarker than lactate dehydrogenase, prostate-specific antigen (PSA) and bone-specific alkaline phosphatase (Mandel et al., Reference Mandel, Huland, Tiebel, Haese, Salomon, Budäus, Tilki, Chun, Heinzer, Graefen, Pantel, Riethdorf and Steuber2021).

Assessment of CTCs has also been used to predict treatment response. Abiraterone is a novel first-line hormonal therapy, and enzalutamide, an oral second-generation anti-androgen receptor (AR), was shown to be effective for patients with mCRPC before or after chemotherapy. A prospective, multicenter, translational study was conducted and showed that a higher androgen receptor splice variant 7 (AR-V7) positive CTC count in blood predicted a poorer response to enzalutamide (Di Lorenzo et al., Reference Di Lorenzo, Zappavigna, Crocetto, Giuliano, Ribera, Morra, Scafuri, Verde, Bruzzese, Iaccarino, Costabile, Onofrio, Viggiani, Palmieri, De Placido, Marretta, Pietroluongo, Luce, Abate, Navaeiseddighi, Caputo, Celentano, Longo, Ferro, Morelli, Facchini, Caraglia, De Placido and Buonerba2021). Furthermore, Antonarakis et al. (Reference Antonarakis, Lu, Luber, Wang, Chen, Zhu, Silberstein, Taylor, Maughan, Denmeade, Pienta, Paller, Carducci, Eisenberger and Luo2017) categorized samples from 202 patients with mCRPC receiving abiraterone or enzalutamide into three groups: CTC−, CTC+/AR-V7- and CTC+/AR-V7+. It was demonstrated that the treatment response and survival were the best for CTC− patients and the worst for CTC+/AR-V7+ patients.

In addition, the Spanish Oncology Genito-Urinary Group performed a prospective translational study of a cohort of 45 patients with mCRPC who were treated with radium-223 (Carles et al., Reference Carles, Castellano, Méndez-Vidal, Mellado, Saez, González Del Alba, Perez-Gracia, Jimenez, Suárez, Sepúlveda, Manneh, Porras, López, Morales-Barrera and Arranz2018). The results indicated that patients with CTC counts ≤5/7.5 mL before radium-223 treatment responded better and were more likely to continue therapy. Additionally, the survival outcomes were better in patients with few CTC counts.

Few translational studies of CTCs in RCC and BCa exist. It was supposed that the lack of translational evidence about CTC in RCC was probably owing to limitations related to the CTC isolation method. The CellSearch system mentioned above is based on the detection of epithelial cell adhesion molecule (EpCAM) and cytokeratin. However, RCC tumor cells have low EpCAM and low cytokeratin expression. Therefore, the standard methods of CTC detection might not be optimal for RCC (Li et al., Reference Li, Li, Zheng, Li, Li, Wang and Chen2023). Cappelletti et al. (Reference Cappelletti, Verzoni, Ratta, Vismara, Silvestri, Montone, Miodini, Reduzzi, Claps, Sepe, Daidone and Procopio2020) reported an efficient, marker-independent approach that assesses the expression of EpCAM, MUC1 and ERBB2 to detect CTCs in RCC. Although the predictive value of CTCs in RCC was not clear in this study due to the limited sample size, it was confirmed that the existence of CTCs with epithelial markers was correlated with shorter PFS.

Some clinical translational studies have explored CTCs, the first marker derived from liquid biopsies, to identify their predictive effect in urologic cancers. Enumerating CTCs in blood and assessing the expression of certain biomarkers can aid prognosis prediction and the development of personalized therapy in mCRPC and mHSPC. Few studies have illustrated the predictive effect of CTCs in RCC and BCa. Additionally, all the existing studies have used blood.

Cell-free DNA and circulation tumor DNA

Circulating cfDNA molecules are extracellular, short fragments of nucleic acids circulating in body fluids and are approximately 134–144 bp (Lu et al., Reference Lu, Delijani, Mecum and Goldkorn2019). ctDNA is a type of cfDNA derived from tumor cells. The quantity of ctDNA depends on the overall tumor volume. With the development of ddPCR and next-generation sequencing, cfDNA can be very sensitively quantified such that even rare mutations and recently methylated sequences can be identified (Poulet et al., Reference Poulet, Massias and Taly2019). cfDNA detection has potential applications in disease screening, prognostic prediction and evaluation of treatment response.

Similar to CTCs, ctDNAs were explored more in PCa among urological cancers by translational studies. The concentration and fragment size of seminal cfDNA were significantly higher in PCa patients than benign prostate hyperplasia (BPH) patients or healthy controls, which indicated that seminal cfDNA might function as a screening biomarker (Poulet et al., Reference Poulet, Massias and Taly2019). Furthermore, ctDNA could also be used to evaluate the prognosis of castration-resistant prostate cancer (CRPC). An analysis of 663 plasma samples from 140 CRPC patients showed that ctDNA was related to worse survival outcome (Choudhury et al., Reference Choudhury, Werner, Francini, Wei, Ha, Freeman, Rhoades, Reed, Gydush, Rotem, Lo, Taplin, Harshman, Zhang, O’Connor, Stover, Parsons, Getz, Meyerson, Love, Hahn and Adalsteinsson2018). Interestingly, the proportion of ctDNA was positively correlated with PSA and ALP and negatively correlated with hemoglobin. It was reported that methylation of SRD5A2 and CYP11A1 DNA in blood cfDNA was associated with a higher risk of biochemical recurrence in patients with HSPC after radical prostatectomy (Horning et al., Reference Horning, Awe, Wang, Liu, Lai, Wang, Jadhav, Louie, Lin, Kroczak, Chen, Jin, Abboud-Werner, Leach, Hernandez, Thompson, Saranchuk, Drachenberg, Chen, Mai and Huang2015).

Regarding value for predicting therapy response, a prospective translational study was conducted in a cohort of 28 patients with oligometastatic HSPC undergoing stereotactic body radiotherapy (SBRT) (Colosini et al., Reference Colosini, Bernardi, Foroni, Pasinetti, Guerini, Russo, Bresciani, Tomasi, Magrini, Bardoscia and Triggiani2022). Deep targeted cfDNA sequencing analysis revealed the molecular heterogeneity of metastatic PCa and helped to identify the exact characteristics to develop a specific therapeutic strategy. For instance, BRCA1 mutation in ctDNA was associated with SBRT failure.

The application of ctDNAs presented translational potential in clinical management of BCa and RCC. Shallow-depth bisulfite sequencing of urinary cfDNA by methylation deconvolution and analysis of global hypomethylation and copy number aberrations (CNAs) has been proposed as a method to detect BCa, with a sensitivity of 93.5% and a specificity of 95.8% (Cheng et al., Reference Cheng, Jiang, Teoh, Heung, Tam, Sun, Lee, Ni, Chan, Ng, Chan, Chiu and Lo2019). Furthermore, the levels of methylation and CNAs might reflect disease stage. A multiomic urinary cfDNA analysis strategy proposed by Chauhan et al. was also proven to be capable of detecting molecular residual disease of BCa (Chauhan et al., Reference Chauhan, Shiang, Alahi, Sundby, Feng, Gungoren, Nawaf, Chen, Babbra, Harris, Qaium, Hatscher, Antiporda, Brunt, Mayer, Shern, Baumann, Kim, Reimers, Smith and Chaudhuri2023). Additionally, for patients with metastatic clear cell RCC, lower cfDNA levels in the bloodstream were associated with a better response to treatment and longer PFS, but further study is required for confirmation (Del Re et al., Reference Del Re, Crucitta, Paolieri, Cucchiara, Verzoni, Bloise, Ciampi, Mercinelli, Capuano, Sportiello, Martinetti, Procopio, Galli, Porta, Bracarda and Danesi2022).

In summary, cfDNA has broad diagnostic and prognostic utility mainly for PCa and BCa. The sources of liquid samples in studies have varied greatly, including blood, seminal fluid and urine. Additionally, there are many aspects of cfDNA that are worth exploring, such as cfDNA quantity, mutations, and methylation patterns.

Extracellular vesicles and exosomes

EVs are lipid bilayer membrane-enclosed nanometer to micrometer vesicles secreted continuously by almost all mammalian cells into the extracellular space (Théry et al., Reference Théry, Witwer, Aikawa, Alcaraz, Anderson, Andriantsitohaina, Antoniou, Arab, Archer, Atkin-Smith, Ayre, Bach, Bachurski, Baharvand, Balaj, Baldacchino, Bauer, Baxter, Bebawy, Beckham, Bedina Zavec, Benmoussa, Berardi, Bergese, Bielska, Blenkiron, Bobis-Wozowicz, Boilard, Boireau, Bongiovanni, Borràs, Bosch, Boulanger, Breakefield, Breglio, Brennan, Brigstock, Brisson, Broekman, Bromberg, Bryl-Górecka, Buch, Buck, Burger, Busatto, Buschmann, Bussolati, Buzás, Byrd, Camussi, Carter, Caruso, Chamley, Chang, Chen, Chen, Cheng, Chin, Clayton, Clerici, Cocks, Cocucci, Coffey, Cordeiro-da-Silva, Couch, Coumans, Coyle, Crescitelli, Criado, D’Souza-Schorey, Das, Datta Chaudhuri, de Candia, De Santana, De Wever, Del Portillo, Demaret, Deville, Devitt, Dhondt, Di Vizio, Dieterich, Dolo, Dominguez Rubio, Dominici, Dourado, Driedonks, Duarte, Duncan, Eichenberger, Ekström, El Andaloussi, Elie-Caille, Erdbrügger, Falcón-Pérez, Fatima, Fish, Flores-Bellver, Försönits, Frelet-Barrand, Fricke, Fuhrmann, Gabrielsson, Gámez-Valero, Gardiner, Gärtner, Gaudin, Gho, Giebel, Gilbert, Gimona, Giusti, Goberdhan, Görgens, Gorski, Greening, Gross, Gualerzi, Gupta, Gustafson, Handberg, Haraszti, Harrison, Hegyesi, Hendrix, Hill, Hochberg, Hoffmann, Holder, Holthofer, Hosseinkhani, Hu, Huang, Huber, Hunt, Ibrahim, Ikezu, Inal, Isin, Ivanova, Jackson, Jacobsen, Jay, Jayachandran, Jenster, Jiang, Johnson, Jones, Jong, Jovanovic-Talisman, Jung, Kalluri, Kano, Kaur, Kawamura, Keller, Khamari, Khomyakova, Khvorova, Kierulf, Kim, Kislinger, Klingeborn, Klinke, Kornek, Kosanović, Kovács Á, Krämer-Albers, Krasemann, Krause, Kurochkin, Kusuma, Kuypers, Laitinen, Langevin, Languino, Lannigan, Lässer, Laurent, Lavieu, Lázaro-Ibáñez, Le Lay, Lee, Lee, Lemos, Lenassi, Leszczynska, Li, Liao, Libregts, Ligeti, Lim, Lim, Linē, Linnemannstöns, Llorente, Lombard, Lorenowicz, Lörincz Á, Lötvall, Lovett, Lowry, Loyer, Lu, Lukomska, Lunavat, Maas, Malhi, Marcilla, Mariani, Mariscal, Martens-Uzunova, Martin-Jaular, Martinez, Martins, Mathieu, Mathivanan, Maugeri, McGinnis, McVey, Meckes, Meehan, Mertens, Minciacchi, Möller, Møller Jørgensen, Morales-Kastresana, Morhayim, Mullier, Muraca, Musante, Mussack, Muth, Myburgh, Najrana, Nawaz, Nazarenko, Nejsum, Neri, Neri, Nieuwland, Nimrichter, Nolan, Nolte-’t Hoen, Noren Hooten, O’Driscoll, O’Grady, O’Loghlen, Ochiya, Olivier, Ortiz, Ortiz, Osteikoetxea, Østergaard, Ostrowski, Park, Pegtel, Peinado, Perut, Pfaffl, Phinney, Pieters, Pink, Pisetsky, Pogge von Strandmann, Polakovicova, Poon, Powell, Prada, Pulliam, Quesenberry, Radeghieri, Raffai, Raimondo, Rak, Ramirez, Raposo, Rayyan, Regev-Rudzki, Ricklefs, Robbins, Roberts, Rodrigues, Rohde, Rome, Rouschop, Rughetti, Russell, Saá, Sahoo, Salas-Huenuleo, Sánchez, Saugstad, Saul, Schiffelers, Schneider, Schøyen, Scott, Shahaj, Sharma, Shatnyeva, Shekari, Shelke, Shetty, Shiba, Siljander, Silva, Skowronek, Snyder, Soares, Sódar, Soekmadji, Sotillo, Stahl, Stoorvogel, Stott, Strasser, Swift, Tahara, Tewari, Timms, Tiwari, Tixeira, Tkach, Toh, Tomasini, Torrecilhas, Tosar, Toxavidis, Urbanelli, Vader, van Balkom, van der Grein, Van Deun, van Herwijnen, Van Keuren-Jensen, van Niel, van Royen, van Wijnen, Vasconcelos, Vechetti, Veit, Vella, Velot, Verweij, Vestad, Viñas, Visnovitz, Vukman, Wahlgren, Watson, Wauben, Weaver, Webber, Weber, Wehman, Weiss, Welsh, Wendt, Wheelock, Wiener, Witte, Wolfram, Xagorari, Xander, Xu, Yan, Yáñez-Mó, Yin, Yuana, Zappulli, Zarubova, Žėkas, Zhang, Zhao, Zheng, Zheutlin, Zickler, Zimmermann, Zivkovic, Zocco, Zuba-Surma and Zuba-Surma2018). They were first regarded as a means for cells to release waste. However, it was proven that EVs play an important role in intracellular communication. EVs are very heterogeneous in terms of size and characteristics, with various sizes, methods of biogenesis, origins, biological characteristics and release mechanisms. Exosomes are the most commonly studied subgroup of EVs of endocytic origin, and they range in size from 30 to 100 nm (Becker et al., Reference Becker, Thakur, Weiss, Kim, Peinado and Lyden2016), while oncosomes, as larger vesicles, can be up to 10 μm in diameter. EVs exist in different biological fluids, such as serum, plasma, urine and saliva, with various cargos, such as DNA, RNA, proteins and lipids. MicroRNAs (miRNAs) are the most frequent cargo. Tumor cells were found to release more EVs than other cells (Li et al., Reference Li, Lin, Jiang and Yu2018). Recently, EVs, especially exosomes, have been proven to contain tumor information and function in the progression and metastasis of cancer (Casanova-Salas et al., Reference Casanova-Salas, Athie, Boutros, Del Re, Miyamoto, Pienta, Posadas, Sowalsky, Stenzl, Wyatt and Mateo2021). The potential of EVs as diagnostic and prognostic markers has attracted much attention.

Centrifugation-based approaches are applied to isolate EVs, and ultracentrifugation is considered the standard method (Théry et al., Reference Théry, Witwer, Aikawa, Alcaraz, Anderson, Andriantsitohaina, Antoniou, Arab, Archer, Atkin-Smith, Ayre, Bach, Bachurski, Baharvand, Balaj, Baldacchino, Bauer, Baxter, Bebawy, Beckham, Bedina Zavec, Benmoussa, Berardi, Bergese, Bielska, Blenkiron, Bobis-Wozowicz, Boilard, Boireau, Bongiovanni, Borràs, Bosch, Boulanger, Breakefield, Breglio, Brennan, Brigstock, Brisson, Broekman, Bromberg, Bryl-Górecka, Buch, Buck, Burger, Busatto, Buschmann, Bussolati, Buzás, Byrd, Camussi, Carter, Caruso, Chamley, Chang, Chen, Chen, Cheng, Chin, Clayton, Clerici, Cocks, Cocucci, Coffey, Cordeiro-da-Silva, Couch, Coumans, Coyle, Crescitelli, Criado, D’Souza-Schorey, Das, Datta Chaudhuri, de Candia, De Santana, De Wever, Del Portillo, Demaret, Deville, Devitt, Dhondt, Di Vizio, Dieterich, Dolo, Dominguez Rubio, Dominici, Dourado, Driedonks, Duarte, Duncan, Eichenberger, Ekström, El Andaloussi, Elie-Caille, Erdbrügger, Falcón-Pérez, Fatima, Fish, Flores-Bellver, Försönits, Frelet-Barrand, Fricke, Fuhrmann, Gabrielsson, Gámez-Valero, Gardiner, Gärtner, Gaudin, Gho, Giebel, Gilbert, Gimona, Giusti, Goberdhan, Görgens, Gorski, Greening, Gross, Gualerzi, Gupta, Gustafson, Handberg, Haraszti, Harrison, Hegyesi, Hendrix, Hill, Hochberg, Hoffmann, Holder, Holthofer, Hosseinkhani, Hu, Huang, Huber, Hunt, Ibrahim, Ikezu, Inal, Isin, Ivanova, Jackson, Jacobsen, Jay, Jayachandran, Jenster, Jiang, Johnson, Jones, Jong, Jovanovic-Talisman, Jung, Kalluri, Kano, Kaur, Kawamura, Keller, Khamari, Khomyakova, Khvorova, Kierulf, Kim, Kislinger, Klingeborn, Klinke, Kornek, Kosanović, Kovács Á, Krämer-Albers, Krasemann, Krause, Kurochkin, Kusuma, Kuypers, Laitinen, Langevin, Languino, Lannigan, Lässer, Laurent, Lavieu, Lázaro-Ibáñez, Le Lay, Lee, Lee, Lemos, Lenassi, Leszczynska, Li, Liao, Libregts, Ligeti, Lim, Lim, Linē, Linnemannstöns, Llorente, Lombard, Lorenowicz, Lörincz Á, Lötvall, Lovett, Lowry, Loyer, Lu, Lukomska, Lunavat, Maas, Malhi, Marcilla, Mariani, Mariscal, Martens-Uzunova, Martin-Jaular, Martinez, Martins, Mathieu, Mathivanan, Maugeri, McGinnis, McVey, Meckes, Meehan, Mertens, Minciacchi, Möller, Møller Jørgensen, Morales-Kastresana, Morhayim, Mullier, Muraca, Musante, Mussack, Muth, Myburgh, Najrana, Nawaz, Nazarenko, Nejsum, Neri, Neri, Nieuwland, Nimrichter, Nolan, Nolte-’t Hoen, Noren Hooten, O’Driscoll, O’Grady, O’Loghlen, Ochiya, Olivier, Ortiz, Ortiz, Osteikoetxea, Østergaard, Ostrowski, Park, Pegtel, Peinado, Perut, Pfaffl, Phinney, Pieters, Pink, Pisetsky, Pogge von Strandmann, Polakovicova, Poon, Powell, Prada, Pulliam, Quesenberry, Radeghieri, Raffai, Raimondo, Rak, Ramirez, Raposo, Rayyan, Regev-Rudzki, Ricklefs, Robbins, Roberts, Rodrigues, Rohde, Rome, Rouschop, Rughetti, Russell, Saá, Sahoo, Salas-Huenuleo, Sánchez, Saugstad, Saul, Schiffelers, Schneider, Schøyen, Scott, Shahaj, Sharma, Shatnyeva, Shekari, Shelke, Shetty, Shiba, Siljander, Silva, Skowronek, Snyder, Soares, Sódar, Soekmadji, Sotillo, Stahl, Stoorvogel, Stott, Strasser, Swift, Tahara, Tewari, Timms, Tiwari, Tixeira, Tkach, Toh, Tomasini, Torrecilhas, Tosar, Toxavidis, Urbanelli, Vader, van Balkom, van der Grein, Van Deun, van Herwijnen, Van Keuren-Jensen, van Niel, van Royen, van Wijnen, Vasconcelos, Vechetti, Veit, Vella, Velot, Verweij, Vestad, Viñas, Visnovitz, Vukman, Wahlgren, Watson, Wauben, Weaver, Webber, Weber, Wehman, Weiss, Welsh, Wendt, Wheelock, Wiener, Witte, Wolfram, Xagorari, Xander, Xu, Yan, Yáñez-Mó, Yin, Yuana, Zappulli, Zarubova, Žėkas, Zhang, Zhao, Zheng, Zheutlin, Zickler, Zimmermann, Zivkovic, Zocco, Zuba-Surma and Zuba-Surma2018). However, the efficiency is disagreeable due to its low throughput and poor specificity (Gerdtsson et al., Reference Gerdtsson, Setayesh, Malihi, Ruiz, Carlsson, Nevarez, Matsumoto, Gerdtsson, Zurita, Logothetis, Corn, Aparicio, Hicks and Kuhn2021). To improve the efficiency and specificity, several methods have been explored for the detection and characterization of EVs.

Three translational studies on the use of exosomes in PCa were identified with the present search strategy. Each of the studies developed EV measurement methods. Kim et al. (Reference Kim, van der Pol, Arafa, Thapa, Britton, Kosti, Song, Joshi, R, Ali and Lucien2022) first proposed a systematic method using standardized and calibrated flow cytometry for the quantification of PCa-derived EVs from blood and urine with detailed methodologies, instrument characteristics and acquisition settings. Their study validated the effectiveness of this method and demonstrated that PSMA+ EVs and STEAP+ EVs might be surrogate markers for metastatic PCa. Reverse-phase protein microarrays, as an antibody-based proteomic technology, have also been applied to analyze EV cargo and were used to identify a series of potential biomarkers for EV-based PCa diagnosis, such as PD-1 and survivin (Signore et al., Reference Signore, Alfonsi, Federici, Nanni, Addario, Bertuccini, Simone, Costantini, Crinò, Rossi, Tabolacci, Diociaiuti, Merlino, Gallucci, Sentinelli, Papalia, De Maria and Bonci2021). The last translational study was mentioned in the CTC section. When Oeyen et al. (Reference Oeyen, Liégeois, De Laere, Buys, Strijbos, Dirix and Dirix2021) reanalyzed CellSearch images of patients with mCRPC with ACCEPT software, EVs were efficiently detected as well. It was demonstrated that a higher baseline EV level was associated with shorter PFS of patient with mCRPC, which is in line with the prognostic value of CTCs.

In addition to the translational studies mentioned above, evidence from a number of nonexplicit translational studies was reviewed, also supporting the translational value of EVs and exosomes. As early as 2012, the prognostic value of exosomes in PCa was explored. After isolation by centrifugation, the number of plasma exosomes in PCa patients was significantly higher than that in BPH patients or healthy controls. Survivin is an inhibitor of apoptosis family proteins that is associated with PCa development, and exosomal survivin was also found to be higher in PCa plasma. No difference was observed between patients with PCa with high and low Gleason scores (Khan et al., Reference Khan, Jutzy, Valenzuela, Turay, Aspe, Ashok, Mirshahidi, Mercola, Lilly and Wall2012) However, the number of plasma prostate microparticles, a kind of EV, could identify PCa patients with Gleason score ≥ 4 + 4 by nanoscale flow cytometry (Biggs et al., Reference Biggs, Siddiqui, Al-Zahrani, Pardhan, Brett, Guo, Yang, Wolf, Power, Durfee, MacMillan, Townson, Brinker, Fleshner, Izawa, Chambers, Chin and Leong2016). In addition to their screening value, EVs might also serve as prognostic biomarkers. Two blood-based exosomal miRNAs (miR-1290 and miR-375) were identified as biomarkers that could predict the prognosis of mCRPC (Huang et al., Reference Huang, Yuan, Liang, Du, Xia, Dittmar, Wang, See, Costello, Quevedo, Tan, Nandy, Bevan, Longenbach, Sun, Lu, Wang, Thibodeau, Boardman, Kohli and Wang2015). In addition, significant alteration of exosomal miRNA and protein cargos was observed as neuroendocrine differentiation occurred in PCa, and neuroendocrine prostate cancer has poor survival outcomes with limited treatment options (Bhagirath et al., Reference Bhagirath, Liston, Akoto, Lui, Bensing, Sharma and Saini2021). With regard to value for predicting treatment response, AR-V7 carried in blood exosomes could serve as a reliable biomarker of resistance to hormonal therapy (Del Re et al., Reference Del Re, Biasco, Crucitta, Derosa, Rofi, Orlandini, Miccoli, Galli, Falcone, Jenster, van Schaik and Danesi2017).

Few definite translational studies have focused on EVs in BCa or RCC, though EVs have translational value for these two cancers (Zeuschner et al., Reference Zeuschner, Linxweiler and Junker2020). For example, exosomal miR-1233 and miR-210 in blood were significantly increased in RCC patients compared with healthy controls (Zhang et al., Reference Zhang, Ni, Su, Wang, Zhu, Zhao and Li2018). Regarding the prognosis of RCC, higher miR-224 levels in serum EVs were related to poorer survival outcomes (Du et al., Reference Du, Giridhar, Tian, Tschannen, Zhu, Huang, Kilari, Kohli and Wang2017). For BCa, different studies showed that a series of long noncoding RNAs were increased in urinary EVs of BCa patients compared with healthy controls (Zhan et al., Reference Zhan, Du, Wang, Jiang, Zhang, Li, Yan, Duan, Zhao, Wang, Wang and Wang2018; Xue et al., Reference Xue, Chen and Li2021), while several cargo molecules in blood EVs, such as LNMAT2, could predict the prognosis of advanced BCa (Becker et al., Reference Becker, Thakur, Weiss, Kim, Peinado and Lyden2016).

In summary, EVs and exosomes have been well studied and shown to have strong translational potential for screening and predicting outcome. However, to date, a standard methodology with high efficiency and sensitivity to detect EVs is still lacking.

Perspective and future directions

In the past decade, a variety of studies have revealed the association between the development of urological cancer and factors detected in liquid biopsies, including CTCs, cfDNA and EVs. Undoubtedly, liquid biopsies can be used to detect promising biomarkers that can be applied for disease diagnosis and prognosis and treatment response prediction in PCa and other urological cancers. Currently, traditional tissue biopsy is still the gold standard for disease diagnosis and serves to guide the therapy strategy. Compared with tissue biopsies, liquid biopsies have their own advantages. First, due to tumor heterogeneity, tissue biopsies may not reflect the comprehensive situation of the disease, while liquid biopsies can help to provide a general assessment. Second, with advances in clinical practice, it is important to avoid unnecessary invasive procedures. The application of liquid biopsies may greatly reduce the number of invasive biopsies. For example, because of the limited specificity of PSA as a screening biomarker for PCa, some patients undergo unnecessary prostate biopsy (Duffy, Reference Duffy2020). The clinical application of liquid biopsies when appropriate would efficiently ameliorate this situation. Second, the liquid biopsy is a minimally invasive procedure with a relatively rapid and simple process, making it a better option for patients. Liquid biopsies are also generally less of an economic and socioeconomic burden than tissue biopsies. Third, liquid biopsies can be performed at all stages and thus can be used to stratify patients after radical surgery. Dynamic assessment of liquid biomarkers can be used to guide long-term clinical decisions.

Clinical translational studies function as a bridge between basic studies and clinical use, providing supporting evidence and directing actual application. We reviewed clinical translational studies of liquid biopsies in PCa, BCa and RCC. As the most frequent urological cancer, PCa has received the most attention. In PCa, CTCs and cfDNA from blood have been shown to be able to predict survival outcome and therapy response in CRPC and metastatic HSPC. Interestingly, most studies on CTCs have used the CellSearch platform, which was approved by the FDA as a method to detect CTCs, while two translational studies on cfDNA employed different methods for detection. Studies of EVs have used various detection methods.

To date, the number of specific clinical translational studies is limited and further exploration is warranted. The lack of an ideal standard method for the isolation of targeted biomarkers is one of the main reasons for the limited performance in relevant translational studies. As mentioned above, the CellSearch system is an FDA-approved method for detecting CTCs. However, it is expensive and not suitable for certain cancers, such as RCC (Li et al., Reference Li, Li, Zheng, Li, Li, Wang and Chen2023). With regard to cfDNA and EVs, there is also no standardized method. Without a standardized detection protocol, it is difficult to assess the true utility of liquid biopsies and to apply relevant strategies in the clinic. Therefore, it is essential to explore standardized methods and comprehensive protocols for the identification of different biomarkers. In addition, recent translational research has not explored the utility of liquid biopsy analysis for the selection of therapy. For example, the expression of AR-V7 in blood CTCs or EVs was found to be associated with resistance to second-generation anti-androgen receptor and hormone therapy (Del Re et al., Reference Del Re, Biasco, Crucitta, Derosa, Rofi, Orlandini, Miccoli, Galli, Falcone, Jenster, van Schaik and Danesi2017; Oeyen et al., Reference Oeyen, Liégeois, De Laere, Buys, Strijbos, Dirix and Dirix2021). Therefore, it remains to be determined whether the expression of AR-V7 can guide the selection of second-generation anti-androgen receptor therapy or chemotherapy. Finally, there is still plenty of potential for translational research on liquid biopsies. Recent translational studies have focused on biomarkers in blood, while other fluid biomarkers may also be valuable. Seminal cfDNA levels are much higher (almost 100 times higher) than blood cfDNA levels in PCa patients (Ponti et al., Reference Ponti, Maccaferri, Percesepe, Tomasi and Ozben2021). Urinary biomarkers might have potential in BCa because they have direct contact with tumors.