Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Diagnosis of early-stage bladder cancer via unprocessed urine samples at the point of care

Nature Biomedical Engineering (2024)Cite this article

Subjects

Abstract

Diagnostic kits for the optical detection of bladder cancer in urine can facilitate effective screening and surveillance. However, the heterogeneity of urine samples, owing to patients with bladder cancer often presenting with haematuria, interfere with the transduction of the optical signal. Here we describe the development and point-of-care performance of a device for the detection of bladder cancer that obviates the need for sample processing. The device leverages the enzymatic release of organogel particles carrying solvatochromic fluorophores in the presence of urinary hyaluronidases—a bladder cancer biomarker. Owing to buoyancy, the particles transfer from the urine sample into the organic phase, where the change in fluorescence can be measured via a smartphone without interference from blood proteins. In a double-blind study with 80 unprocessed urine samples from patients with bladder cancer (including samples with haematuria) or other genitourinary diseases and with 25 samples from healthy participants, our system distinguished the cancerous samples, including those with early-stage bladder cancer, with accuracies of about 90%. Obviating the need for sample pretreatment may facilitate the at-home detection of bladder cancer.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic illustrations of BLOOM for BC diagnosis.
Fig. 2: Fabrication of the buoyant organogel messenger and transduction of the solvatochromic signal.
Fig. 3: Hdase-degradable bigel film.
Fig. 4: Hdase detection in the biphasic system.
Fig. 5: Evaluation of BLOOM for BC screening using clinical patient samples.
Fig. 6: Design and diagnostic performance of the BLOOM POC device with a smartphone App.

Similar content being viewed by others

Data availability

The data supporting the findings of this study are available within the article and its Supplementary Information. The raw and analysed datasets generated during the study are available for research purposes from the corresponding authors on reasonable request. Source data are provided with this paper.

References

  1. Lenis, A. T., Lec, P. M., Chamie, K. & Mshs, M. D. Bladder cancer: a review. JAMA 324, 1980–1991 (2020).

    Article  CAS  PubMed  Google Scholar 

  2. Kamat, A. M. et al. Bladder cancer. Lancet 388, 2796–2810 (2016).

    Article  PubMed  Google Scholar 

  3. Sanli, O. et al. Bladder cancer. Nat. Rev. Dis. Prim. 3, 17022 (2017).

    Article  PubMed  Google Scholar 

  4. Jordan, B. & Meeks, J. J. T1 bladder cancer: current considerations for diagnosis and management. Nat. Rev. Urol. 16, 23–34 (2019).

    Article  PubMed  Google Scholar 

  5. Knowles, M. A. & Hurst, C. D. Molecular biology of bladder cancer: new insights into pathogenesis and clinical diversity. Nat. Rev. Cancer 15, 25–41 (2015).

    Article  CAS  PubMed  Google Scholar 

  6. Sokolov, I. et al. Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: detection of bladder cancer. Proc. Natl Acad. Sci. USA 115, 12920–12925 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Cheung, G., Sahai, A., Billia, M., Dasgupta, P. & Khan, M. S. Advances in the diagnosis and treatment of bladder cancer. BMC Med. 11, 13 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kamat, A. M. et al. ICUD-EAU international consultation on bladder cancer 2012: screening, diagnosis, and molecular markers. Eur. Urol. 63, 4–15 (2013).

    Article  PubMed  Google Scholar 

  9. Moonen, P. M. J., Kiemeney, L. A. L. M. & Witjes, J. A. Urinary NMP22® BladderChek® test in the diagnosis of superficial bladder cancer. Eur. Urol. 48, 951–956 (2005).

    Article  CAS  PubMed  Google Scholar 

  10. Yeung, C., Dinh, T. & Lee, J. The health economics of bladder cancer: an updated review of the published literature. Pharmacoeconomics 32, 1093–1104 (2014).

    Article  PubMed  Google Scholar 

  11. Zhu, C., Ting, H., Ng, K. & Ong, T. A review on the accuracy of bladder cancer detection methods. J. Cancer 10, 4038–4044 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Grossman, H. B. et al. Detection of bladder cancer using a point-of-care proteomic assay. JAMA 293, 810–816 (2005).

    Article  CAS  PubMed  Google Scholar 

  13. Hwang, E. C. et al. Use of the NMP22 BladderChek test in the diagnosis and follow-up of urothelial cancer: a cross-sectional study. Urology 77, 154–159 (2011).

    Article  PubMed  Google Scholar 

  14. Lee, M. H. et al. Electrochemical sensing of nuclear matrix protein 22 in urine with molecularly imprinted poly(ethylene-co-vinyl alcohol) coated zinc oxide nanorod arrays for clinical studies of bladder cancer diagnosis. Biosens. Bioelectron. 79, 789–795 (2016).

    Article  PubMed  Google Scholar 

  15. Arya, S. K. & Estrela, P. Electrochemical ELISA-based platform for bladder cancer protein biomarker detection in urine. Biosens. Bioelectron. 117, 620–627 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Wang, J. et al. Strand displacement amplification-coupled dynamic light scattering method to detect urinary telomerase for non-invasive detection of bladder cancer. Biosens. Bioelectron. 131, 143–148 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Sharifuzzaman, M. et al. An electrodeposited MXene-Ti3C2Tx nanosheets functionalized by task-specific ionic liquid for simultaneous and multiplexed detection of bladder cancer biomarkers. Small 16, 2002517 (2020).

    Article  CAS  Google Scholar 

  18. Yang, Y. et al. Integrated urinalysis devices based on interface-engineered field-effect transistor biosensors incorporated with electronic circuits. Adv. Mater. 34, 2203224 (2022).

    Article  CAS  Google Scholar 

  19. Sharma, G., Sharma, A., Krishna, M. & Kumar, S. Xpert bladder cancer monitor in surveillance of bladder cancer: systematic review and meta-analysis. Urol. Oncol. Semin. Orig. Investig. 40, 163.e1–163.e9 (2022).

    Google Scholar 

  20. Cochetti, G. et al. Diagnostic performance of the Bladder EpiCheck methylation test and photodynamic diagnosis-guided cystoscopy in the surveillance of high-risk non-muscle invasive bladder cancer: a single centre, prospective, blinded clinical trial. Urol. Oncol. Semin. Orig. Investig. 40, 105.e11–105.e18 (2022).

    CAS  Google Scholar 

  21. Ferro, M. et al. Liquid biopsy biomarkers in urine: a route towards molecular diagnosis and personalized medicine of bladder cancer. J. Pers. Med. 11, 237 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Trenti, E. et al. Comparison of 2 new real-time polymerase chain reaction–based urinary markers in the follow-up of patients with non–muscle-invasive bladder cancer. Cancer Cytopathol. 128, 341–347 (2020).

    Article  CAS  PubMed  Google Scholar 

  23. Roperch, J. P. & Hennion, C. A novel ultra-sensitive method for the detection of FGFR3 mutations in urine of bladder cancer patients—design of the Urodiag® PCR kit for surveillance of patients with non-muscle-invasive bladder cancer (NMIBC). BMC Med. Genet. 21, 112 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Todenhöfer, T. et al. Impact of different grades of microscopic hematuria on the performance of urine-based markers for the detection of urothelial carcinoma. Urol. Oncol. Semin. Orig. Investig. 31, 1148–1154 (2013).

    Google Scholar 

  25. Wang, Z. et al. Evaluation of the NMP22 BladderChek test for detecting bladder cancer: a systematic review and meta-analysis. Oncotarget 8, 100648–100656 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Michael, I. et al. A fidget spinner for the point-of-care diagnosis of urinary tract infection. Nat. Biomed. Eng. 4, 591–600 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Messing, E. M. et al. Home screening for hematuria: results of a multi-clinic study. J. Urol. 148, 289–292 (1992).

    Article  CAS  PubMed  Google Scholar 

  28. Raimondo, F. et al. Effects of hematuria on the proteomic profile of urinary extracellular vesicles: technical challenges. J. Proteome Res. 17, 2572–2580 (2018).

    Article  CAS  PubMed  Google Scholar 

  29. Wang, F. et al. Self-referenced synthetic urinary biomarker for quantitative monitoring of cancer development. J. Am. Chem. Soc. 145, 919–928 (2023).

    Article  CAS  PubMed  Google Scholar 

  30. Wood, S. L., Knowles, M. A., Thompson, D., Selby, P. J. & Banks, R. E. Proteomic studies of urinary biomarkers for prostate, bladder and kidney cancers. Nat. Rev. Urol. 10, 206–218 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Adkins, J. N. et al. Toward a human blood serum proteome: analysis by multidimensional separation coupled with mass spectrometry. Mol. Cell. Proteom. 1, 947–955 (2002).

    Article  CAS  Google Scholar 

  32. Lokeshwar, V. B. et al. Urinary hyaluronic acid and hyaluronidase: markers for bladder cancer detection and evaluation of grade. J. Urol. 163, 348–356 (2000).

    Article  CAS  PubMed  Google Scholar 

  33. Pham, H. T., Block, N. L. & Lokeshwar, V. B. Tumor-derived hyaluronidase: a diagnostic urine marker for high-grade bladder cancer. Cancer Res. 57, 778–783 (1997).

    CAS  PubMed  Google Scholar 

  34. Raj, P., Lee, S. Y. & Lee, T. Y. Carbon dot/naphthalimide based ratiometric fluorescence biosensor for hyaluronidase detection. Materials 14, 1313 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Wang, W. et al. One-pot synthesis of hyaluronic acid–coated gold nanoparticles as SERS substrate for the determination of hyaluronidase activity. Microchim. Acta 187, 604 (2020).

    Article  CAS  Google Scholar 

  36. Si, Y., Li, L., He, B. & Li, J. A novel surface-enhanced Raman scattering-based ratiometric approach for detection of hyaluronidase in urine. Talanta 215, 120915 (2020).

    Article  CAS  PubMed  Google Scholar 

  37. Nossier, A. I., Eissa, S., Ismail, M. F., Hamdy, M. A. & Azzazy, H. M. E. S. Direct detection of hyaluronidase in urine using cationic gold nanoparticles: a potential diagnostic test for bladder cancer. Biosens. Bioelectron. 54, 7–14 (2014).

    Article  CAS  PubMed  Google Scholar 

  38. Stocco, A. et al. Bidirectional nanoparticle crossing of oil–water interfaces induced by different stimuli: insight into phase transfer. Angew. Chem. Int. Ed. 51, 9647–9651 (2012).

    Article  CAS  Google Scholar 

  39. Maestro, A. et al. Wettability of silica nanoparticle-surfactant nanocomposite interfacial layers. Soft Matter 8, 837–843 (2012).

    Article  CAS  Google Scholar 

  40. Yang, X.-C. et al. Drug delivery using nanoparticle-stabilized nanocapsules. Angew. Chem. 123, 497–501 (2011).

    Article  Google Scholar 

  41. Shrirao, A. B. et al. Autofluorescence of blood and its application in biomedical and clinical research. Biotechnol. Bioeng. 118, 4550–4576 (2021).

    Article  CAS  PubMed  Google Scholar 

  42. Huang, X., Liu, Y., Yung, B., Xiong, Y. & Chen, X. Nanotechnology-enhanced no-wash biosensors for in vitro diagnostics of cancer. ACS Nano 11, 5238–5292 (2017).

    Article  CAS  PubMed  Google Scholar 

  43. Tan, W. S. et al. Novel urinary biomarkers for the detection of bladder cancer: a systematic review. Cancer Treat. Rev. 69, 39–52 (2018).

    Article  CAS  PubMed  Google Scholar 

  44. Borrebaeck, C. A. K. Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer. Nat. Rev. Cancer 17, 199–204 (2017).

    Article  CAS  PubMed  Google Scholar 

  45. Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).

    Article  CAS  PubMed  Google Scholar 

  46. Rifai, N., Gillette, M. A. & Carr, S. A. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol. 24, 971–983 (2006).

    Article  CAS  PubMed  Google Scholar 

  47. Kramer, M. W. et al. HYAL-1 hyaluronidase: a potential prognostic indicator for progression to muscle invasion and recurrence in bladder cancer. Eur. Urol. 57, 86–94 (2010).

    Article  CAS  PubMed  Google Scholar 

  48. Lokeshwar, V. B. et al. Bladder tumor markers for monitoring recurrence and screening comparison of hyaluronic acid-hyaluronidase and BTA-stat tests. Cancer 95, 61–72 (2002).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MSIT) (2023R1A2C100438911 and 2020M3A9D8038190). This work was also supported by the Nanomedical Devices Development Project of NNFC in 2023 and the KIST Institutional Program (2E32311 and 2E3232N). We thank C. Baek and S. Choi at the Technological Convergence Center at the Korea Institute of Science and Technology for developing the smartphone application programme.

Author information

Author notes

  1. These authors contributed equally: Changjoon Keum, Haejin Yeom, Tae Il Noh.

Authors and Affiliations

  1. Center for Advanced Biomolecular Recognition, Biomedical Research Division, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea

    Changjoon Keum, Haejin Yeom, Seung Yong Yi, Soyeong Jin, Hojun Kim, Kwan Hyi Lee & Youngdo Jeong

  2. Department of HY-KIST Bio-convergence, Hanyang University, Seoul, Republic of Korea

    Haejin Yeom, Seung Yong Yi & Youngdo Jeong

  3. Department of Urology, Korea University School of Medicine, Seoul, Republic of Korea

    Tae Il Noh, Ji Sung Shim, Sung Goo Yoon & Seok Ho Kang

  4. Department of Chemistry, Hanyang University, Seoul, Republic of Korea

    Soyeong Jin

  5. Fusion Biotechnology, Inc., Ulsan, Republic of Korea

    Chaekyu Kim

  6. KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, Republic of Korea

    Kwan Hyi Lee

Authors
  1. Changjoon Keum
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haejin Yeom
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tae Il Noh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seung Yong Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Soyeong Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chaekyu Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ji Sung Shim
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sung Goo Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hojun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kwan Hyi Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Seok Ho Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Youngdo Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C. Keum and Y.J. conceived and designed the research. C. Keum and H.Y. designed the experiments. C. Keum, H.Y., T.I.N., S.Y.Y., S.J., J.S.S. and S.G.Y. performed the experiments and analysed the data. C. Keum and Y.J. wrote the paper. C. Kim and H.K. were involved in the discussion. K.H.L., S.H.K. and Y.J. supervised the research.

Corresponding authors

Correspondence to Kwan Hyi Lee, Seok Ho Kang or Youngdo Jeong.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks Xiaoyuan Chen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 BLOOM results according to the T stage of BC.

a, Normalized fluorescence intensities of urine samples acquired from BLOOM according to the T stage of BC (n = 25 for healthy controls, n = 20 for GU, n = 24 for Ta, n = 19 for T1, and n = 13 for T2). The lines represent the mean, and the error bars represent S.D. Two-tailed Student’s t-tests were used for comparisons. ***p < 0.001 and ****p < 0.0001. b, ROC curves and AUC values of BC patients against no-tumor controls according to the T stage of BC.

Extended Data Fig. 2 BLOOM results according to the grade of BC.

a, Normalized fluorescence intensities of urine samples acquired from BLOOM according to the grade of BC (n = 45 for no-tumor controls, n = 13 for low-grade tumor, and n = 39 for high-grade tumor). The lines represent the mean, and the error bars represent S.D. Two-tailed Student’s t-tests were used for comparisons. ****p < 0.0001. b, ROC curves and AUC values of BC patients against no-tumor controls according to the grade of BC.

Extended Data Fig. 3 BLOOM results for the prediction of BC depending on the degree of haematuria.

a, Normalized fluorescence intensities of urine samples acquired from BLOOM of patients with few or no (0 ≤ RBCs < 5, n = 20), moderate (5 ≤ RBCs < 30, n = 13), and high (30≤RBCs, n = 27) degree of haematuria versus no-tumor controls, including patients with genito-urinary diseases (n = 20 clinical urine samples) and healthy controls (n = 25 clinical urine samples). The lines represent the mean, and the error bars represent S.D. Two-tailed Student’s t-tests were used for comparisons. ****p < 0.0001. b, Receiver operating characteristic curves for the prediction of BC diagnosis depending on the degree of haematuria.

Extended Data Fig. 4 BLOOM results conducted using POC device according to the T stage and grade of BC.

Normalized fluorescence intensities of urine samples acquired from BLOOM POC device and smartphone App according to the a, T stage (n = 5 for Ta, n = 8 for T1, n = 3 for T2, and n = 11 for non-BC) and b, grade of BC (n = 4 for low-grade and n = 12 for high-grade BC). Lines represent the mean, and error bars represent S.D. Two-tailed Student’s t-tests were used for comparisons. **p < 0.01, ***p < 0.001, and ****p < 0.0001.

Supplementary information

Supplementary Information

Supplementary figures, tables, references and video captions.

Reporting Summary

Peer Review File

Supplementary Video 1

BLOOM in a biphasic system.

Supplementary Video 2

BLOOM in a biphasic system using artificial haematuria samples.

Supplementary Video 3

Clinical sample test procedure using the BLOOM POC device.

Supplementary Video 4

BLOOM assay for clinical samples using POC reader and smartphone App.

Supplementary Video 5

Assembly of the BLOOM POC device.

Source data

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Keum, C., Yeom, H., Noh, T.I. et al. Diagnosis of early-stage bladder cancer via unprocessed urine samples at the point of care. Nat. Biomed. Eng (2024). https://doi.org/10.1038/s41551-024-01298-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41551-024-01298-0

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing