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.

Ultrasensitive tumour-penetrating nanosensors of protease activity

Abstract

The ability to identify cancer lesions with endogenous biomarkers is currently limited to tumours ~1 cm in diameter. We recently reported an exogenously administered tumour-penetrating nanosensor that sheds, in response to tumour-specific proteases, peptide fragments that can then be detected in the urine. Here, we report the optimization, informed by a pharmacokinetic mathematical model, of the surface presentation of the peptide substrates to both enhance on-target protease cleavage and minimize off-target cleavage, and of the functionalization of the nanosensors with tumour-penetrating ligands that engage active trafficking pathways to increase activation in the tumour microenvironment. The resulting nanosensor discriminated sub-5 mm lesions in human epithelial tumours and detected nodules with median diameters smaller than 2 mm in an orthotopic model of ovarian cancer. We also demonstrate enhanced receptor-dependent specificity of signal generation in the urine in an immunocompetent model of colorectal liver metastases, and in situ activation of the nanosensors in human tumour microarrays when re-engineered as fluorogenic zymography probes.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: MMP9 is upregulated across human cancers.
Figure 2: In vitro and in vivo experiments and in silico evaluation enables engineering of an optimized ABN.
Figure 3: Optimized ABNs detect sub-5 mm diameter tumours.
Figure 4: Urinary biomarkers outperform blood biomarkers in detecting millimetre-sized lesions in orthotopic xenograft models of ovarian cancer.
Figure 5: Minimally invasive receptor classification of syngeneic liver metastasis via targeted ABNs.
Figure 6: ABN zymography is responsive to human tissues.

References

  1. 1

    Etzioni, R. et al. The case for early detection. Nat. Rev. Cancer 3, 243–252 (2003).

    CAS  Article  Google Scholar 

  2. 2

    SEER Cancer Statistics Review, 1975–2013 (National Cancer Institute, 2016); http://seer.cancer.gov/csr/1975_2013

  3. 3

    Kanas, G. P. et al. Survival after liver resection in metastatic colorectal cancer: review and meta-analysis of prognostic factors. Clin. Epidemiol. 4, 283–301 (2012).

    PubMed  PubMed Central  Google Scholar 

  4. 4

    Bristow, R. E., Tomacruz, R. S., Armstrong, D. K., Trimble, E. L. & Montz, F. J. Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: a meta-analysis. J. Clin. Oncol. 20, 1248–1259 (2002).

    Article  Google Scholar 

  5. 5

    Fader, A. N. et al. The prognostic significance of pre- and post-treatment CA-125 in grade 1 serous ovarian carcinoma: a gynecologic Oncology Group study. Gynecol. Oncol. 132, 560–565 (2014).

    CAS  Article  Google Scholar 

  6. 6

    Shaukat, A. et al. Long-term mortality after screening for colorectal cancer. N. Engl. J. Med. 369, 1106–1114 (2013).

    CAS  Article  Google Scholar 

  7. 7

    Frangioni, J. V. New technologies for human cancer imaging. J. Clin. Oncol. 26, 4012–4021 (2008).

    Article  Google Scholar 

  8. 8

    Hori, S. S. & Gambhir, S. S. Mathematical model identifies blood biomarker-based early cancer detection strategies and limitations. Sci. Transl. Med. 3, 109ra116 (2011).

    Article  Google Scholar 

  9. 9

    Henry, N. L. & Hayes, D. F. Cancer biomarkers. Mol. Oncol. 6, 140–146 (2012).

    CAS  Article  Google Scholar 

  10. 10

    Kwong, G. A. et al. Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease. Nat. Biotechnol. 31, 63–70 (2013).

    CAS  Article  Google Scholar 

  11. 11

    Warren, A. D., Kwong, G. A., Wood, D. K., Lin, K. Y. & Bhatia, S. N. Point-of-care diagnostics for noncommunicable diseases using synthetic urinary biomarkers and paper microfluidics. Proc. Natl Acad. Sci. USA 111, 3671–3676 (2014).

    CAS  Article  Google Scholar 

  12. 12

    Koblinski, J. E., Ahram, M. & Sloane, B. F. Unraveling the role of proteases in cancer. Clin. Chim. Acta 291, 113–135 (2000).

    CAS  Article  Google Scholar 

  13. 13

    Kwong, G. A. et al. Mathematical framework for activity-based cancer biomarkers. Proc. Natl Acad. Sci. USA 112, 12627–12632 (2015).

    CAS  Article  Google Scholar 

  14. 14

    Kessenbrock, K., Plaks, V. & Werb, Z. Matrix metalloproteinases: regulators of the tumor microenvironment. Cell 141, 52–67 (2010).

    CAS  Article  Google Scholar 

  15. 15

    Farina, A. R. & Mackay, A. R. Gelatinase B/MMP-9 in tumour pathogenesis and progression. Cancers 6, 240–296 (2014).

    Article  Google Scholar 

  16. 16

    Wu, Z.-S. et al. Prognostic significance of MMP-9 and TIMP-1 serum and tissue expression in breast cancer. Int. J. Cancer 122, 2050–2056 (2008).

    CAS  Article  Google Scholar 

  17. 17

    Deere, J. et al. Kinetics of enzyme attack on substrates covalently attached to solid surfaces: influence of spacer chain length, immobilized substrate surface concentration and surface charge. Langmuir 24, 11762–11769 (2008).

    CAS  Article  Google Scholar 

  18. 18

    Nagase, H. & Fields, G. B. Human matrix metalloproteinase specificity studies using collagen sequence-based synthetic peptides. Peptide Sci. 40, 399–416 (1996).

    CAS  Article  Google Scholar 

  19. 19

    Miller, M. A. et al. Proteolytic Activity Matrix Analysis (PrAMA) for simultaneous determination of multiple protease activities. Integr. Biol. 3, 422–438 (2011).

    CAS  Article  Google Scholar 

  20. 20

    Whitley, M. J. et al. A mouse-human phase 1 co-clinical trial of a protease-activated fluorescent probe for imaging cancer. Sci. Transl. Med. 8, 320ra4 (2016).

    Article  Google Scholar 

  21. 21

    Desnoyers, L. R. et al. Tumor-specific activation of an EGFR-targeting probody enhances therapeutic index. Sci. Transl. Med. 5, 207ra144 (2013).

    Article  Google Scholar 

  22. 22

    Miller, M. A. et al. Predicting therapeutic nanomedicine efficacy using a companion magnetic resonance imaging nanoparticle. Sci. Transl. Med. 7, 314ra183 (2015).

    Article  Google Scholar 

  23. 23

    Bertrand, N., Wu, J., Xu, X., Kamaly, N. & Farokhzad, O. C. Cancer nanotechnology: the impact of passive and active targeting in the era of modern cancer biology. Adv. Drug Deliv. Rev. 66, 2–25 (2014).

    CAS  Article  Google Scholar 

  24. 24

    Wilhelm, S. et al. Analysis of nanoparticle delivery to tumours. Nat. Rev. Mater. 1, 16014 (2016).

    CAS  Article  Google Scholar 

  25. 25

    Davis, M. E. et al. Evidence of RNAi in humans from systemically administered siRNA via targeted nanoparticles. Nature 464, 1067–1070 (2010).

    CAS  Article  Google Scholar 

  26. 26

    Hrkach, J. et al. Preclinical development and clinical translation of a PSMA-targeted docetaxel nanoparticle with a differentiated pharmacological profile. Sci. Transl. Med. 4, 128ra39 (2012).

    Article  Google Scholar 

  27. 27

    Ruoslahti, E. Specialization of tumour vasculature. Nat. Rev. Cancer 2, 83–90 (2002).

    Article  Google Scholar 

  28. 28

    Ruoslahti, E. & Bhatia, S. N. & Sailor, M. J. Targeting of drugs and nanoparticles to tumors. J. Cell Biol. 188, 759–768 (2010).

    CAS  Article  Google Scholar 

  29. 29

    Lin, K. Y., Kwon, E. J., Lo, J. H. & Bhatia, S. N. Drug-induced amplification of nanoparticle targeting to tumors. Nano Today 9, 550–559 (2014).

    CAS  Article  Google Scholar 

  30. 30

    Pang, H.-B. et al. An endocytosis pathway initiated through neuropilin-1 and regulated by nutrient availability. Nat. Commun. 5, 4904 (2014).

    CAS  Article  Google Scholar 

  31. 31

    Fogal, V., Zhang, L., Krajewski, S. & Ruoslahti, E. Mitochondrial/cell-surface protein p32/gC1qR as a molecular target in tumor cells and tumor stroma. Cancer Res. 68, 7210–7218 (2008).

    CAS  Article  Google Scholar 

  32. 32

    Dufour, A. et al. Small-molecule anticancer compounds selectively target the hemopexin domain of matrix metalloproteinase-9. Cancer Res. 71, 4977–4988 (2011).

    CAS  Article  Google Scholar 

  33. 33

    Zurawski, V. R. et al. An initial analysis of preoperative serum CA 125 levels in patients with early stage ovarian carcinoma. Gynecol. Oncol. 30, 7–14 (1988).

    Article  Google Scholar 

  34. 34

    Ren, Y., Hauert, S., Lo, J. H. & Bhatia, S. N. Identification and characterization of receptor-specific peptides for siRNA delivery. ACS Nano 6, 8620–8631 (2012).

    CAS  Article  Google Scholar 

  35. 35

    Drapkin, R. et al. Human epididymis protein 4 (HE4) is a secreted glycoprotein that is overexpressed by serous and endometrioid ovarian carcinomas. Cancer Res. 65, 2162–2169 (2005).

    CAS  Article  Google Scholar 

  36. 36

    Rankin, E. B. et al. AXL is an essential factor and therapeutic target for metastatic ovarian cancer. Cancer Res. 70, 7570–7579 (2010).

    CAS  Article  Google Scholar 

  37. 37

    Alper, Ö. et al. Epidermal growth factor receptor signaling and the invasive phenotype of ovarian carcinoma cells. J. Natl Cancer Inst. 93, 1375–1384 (2001).

    CAS  Article  Google Scholar 

  38. 38

    Brown, P. O. & Palmer, C. The preclinical natural history of serous ovarian cancer: defining the target for early detection. PLoS Med. 6, e1000114 (2009).

    Article  Google Scholar 

  39. 39

    Sugahara, K. N. et al. Tissue-penetrating delivery of compounds and nanoparticles into tumors. Cancer Cell 16, 510–520 (2009).

    CAS  Article  Google Scholar 

  40. 40

    Desgrosellier, J. S. & Cheresh, D. A. Integrins in cancer: biological implications and therapeutic opportunities. Nat. Rev. Cancer 10, 9–22 (2010).

    CAS  Article  Google Scholar 

  41. 41

    Yao, M., Lam, E. C., Kelly, C. R., Zhou, W. & Wolfe, M. M. Cyclooxygenase-2 selective inhibition with NS-398 suppresses proliferation and invasiveness and delays liver metastasis in colorectal cancer. Br. J. Cancer 90, 712–719 (2004).

    CAS  Article  Google Scholar 

  42. 42

    Jackson, B. C., Nebert, D. W. & Vasiliou, V. Update of human and mouse matrix metalloproteinase families. Hum. Genomics 4, 194–201 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Sanman, L. E. & Bogyo, M. Activity-based profiling of proteases. Annu. Rev. Biochem. 83, 249–273 (2014).

    CAS  Article  Google Scholar 

  44. 44

    Withana, N. P. et al. Labeling of active proteases in fresh-frozen tissues by topical application of quenched activity-based probes. Nat. Protoc. 11, 184–191 (2016).

    CAS  Article  Google Scholar 

  45. 45

    Sugahara, K. N. et al. Coadministration of a tumor-penetrating peptide enhances the efficacy of cancer drugs. Science 328, 1031–1035 (2010).

    CAS  Article  Google Scholar 

  46. 46

    Van Gorp, T. et al. HE4 and CA125 as a diagnostic test in ovarian cancer: prospective validation of the Risk of Ovarian Malignancy Algorithm. Br. J. Cancer 104, 863–870 (2011).

    CAS  Article  Google Scholar 

  47. 47

    Alcázar, J. L., Guerriero, S., Laparte, C., Ajossa, S. & Jurado, M. Contribution of power Doppler blood flow mapping to gray-scale ultrasound for predicting malignancy of adnexal masses in symptomatic and asymptomatic women. Eur. J. Obstet. Gynecol. Reprod. Biol. 155, 99–105 (2011).

    Article  Google Scholar 

  48. 48

    Park, J.-H. et al. Systematic surface engineering of magnetic nanoworms for in vivo tumor targeting. Small 5, 694–700 (2009).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank H. Fleming (MIT) for critical editing of the manuscript, and A. Warren (MIT), G. Kwong (Georgia Institute of Technology), J. Voog (MIT, Massachusetts General Hospital), V. Ramanan and C. Buss (MIT) for helpful discussion. We are grateful to the Koch Institute Swanson Biotechnology Core at MIT, especially S. Malstrom, K. Cormier and veterinary pathologist R. T. Bronson. This study was supported in part by a Koch Institute Support Grant P30-CA14051 from the National Cancer Institute (Swanson Biotechnology Center), a Core Center Grant P30-ES002109 from the National Institute of Environmental Health Sciences, the Ludwig Fund for Cancer Research, and the Koch Institute Marble Center for Cancer Nanomedicine. E.J.K. acknowledges support from the Ruth L. Kirschstein National Research Service Award (1F32CA177094-01). J.S.D. thanks the National Science Foundation Graduate Research Fellowship Program for support. S.N.B. is a Howard Hughes Institute Investigator.

Author information

Affiliations

Authors

Contributions

E.J.K. and J.S.D. performed the experiments and analysed the data. E.J.K., J.S.D. and S.N.B. designed the experiments and wrote the manuscript.

Corresponding author

Correspondence to Sangeeta N. Bhatia.

Ethics declarations

Competing interests

The authors are listed as inventors on patent applications related to the content of this work.

Supplementary information

Supplementary Information

Supplementary text, figures and references. (PDF 12491 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kwon, E., Dudani, J. & Bhatia, S. Ultrasensitive tumour-penetrating nanosensors of protease activity. Nat Biomed Eng 1, 0054 (2017). https://doi.org/10.1038/s41551-017-0054

Download citation

Further reading

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