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.

Urinary detection of early responses to checkpoint blockade and of resistance to it via protease-cleaved antibody-conjugated sensors

Nature Biomedical Engineering volume 6, pages 310–324 (2022)Cite this article

Subjects

Abstract

Immune checkpoint blockade (ICB) therapy does not benefit the majority of treated patients, and those who respond to the therapy can become resistant to it. Here we report the design and performance of systemically administered protease activity sensors conjugated to anti-programmed cell death protein 1 (αPD1) antibodies for the monitoring of antitumour responses to ICB therapy. The sensors consist of a library of mass-barcoded protease substrates that, when cleaved by tumour proteases and immune proteases, are released into urine, where they can be detected by mass spectrometry. By using syngeneic mouse models of colorectal cancer, we show that random forest classifiers trained on mass spectrometry signatures from a library of αPD1-conjugated mass-barcoded activity sensors for differentially expressed tumour proteases and immune proteases can be used to detect early antitumour responses and discriminate resistance to ICB therapy driven by loss-of-function mutations in either the B2m or Jak1 genes. Biomarkers of protease activity may facilitate the assessment of early responses to ICB therapy and the classification of refractory tumours based on resistance mechanisms.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Fig. 1: Antibody binding and therapeutic efficacy are unaffected by peptide conjugation.
Fig. 2: Sensing T cell killing of tumour cells by antibody–GzmB sensor conjugates.
Fig. 3: Urinary detection of ICB therapeutic response by administration of antibody–GzmB sensor conjugates.
Fig. 4: ICB resistance by LOF mutations in B2m and Jak1 leads to reduced GzmB activity.
Fig. 5: Proteases are dysregulated in ICB response and resistance.
Fig. 6: Mass-barcoded peptide sensors for multiplexed detection of protease activity.
Fig. 7: Urinary classification of ICB response and resistance.

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The sequencing datasets generated from murine tumours and analysed from human samples (Riaz, N. et al.11) have been deposited in NCBI’s Gene Expression Omnibus and are accessible through the GEO Series accession numbers GSE192796 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE192796) and GSE91061 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE91061), respectively. Other data generated and analysed during the study are available from the corresponding author on reasonable request. Source data for tumour burdens are provided with this paper.

Code availability

All codes used in this work are available on request from the corresponding author.

References

  1. Ribas, A. & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. Science 359, 1350–1355 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Sharma, P. & Allison, J. P. The future of immune checkpoint therapy. Science 348, 56–61 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kalbasi, A. & Ribas, A. Tumour-intrinsic resistance to immune checkpoint blockade. Nat. Rev. Immunol. https://doi.org/10.1038/s41577-019-0218-4 (2019).

  5. Nishino, M., Ramaiya, N. H., Hatabu, H. & Hodi, F. S. Monitoring immune-checkpoint blockade: response evaluation and biomarker development. Nat. Rev. Clin. Oncol. 14, 655–668 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hodi, F. S. et al. Evaluation of immune-related response criteria and RECIST v1.1 in patients with advanced melanoma treated with pembrolizumab. J. Clin. Oncol. 34, 1510–1517 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Garon, E. B. et al. Pembrolizumab for the treatment of non–small-cell lung cancer. N. Engl. J. Med. 372, 2018–2028 (2015).

    Article  PubMed  Google Scholar 

  8. Nishino, M. et al. Immune-related tumor response dynamics in melanoma patients treated with pembrolizumab: identifying markers for clinical outcome and treatment decisions. Clin. Cancer Res. 23, 4671–4679 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Gerwing, M. et al. The beginning of the end for conventional RECIST — novel therapies require novel imaging approaches. Nat. Rev. Clin. Oncol. 16, 442–458 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. Mandal, R. & Chan, T. A. Personalized oncology meets immunology: the path toward precision immunotherapy. Cancer Discov. 6, 703–713 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949.e16 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Fairfax, B. P. et al. Peripheral CD8+ T cell characteristics associated with durable responses to immune checkpoint blockade in patients with metastatic melanoma. Nat. Med. 26, 193–199 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Valpione, S. et al. Immune awakening revealed by peripheral T cell dynamics after one cycle of immunotherapy. Nat. Cancer 1, 210–221 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Goldberg, S. B. et al. Early assessment of lung cancer immunotherapy response via circulating tumor DNA. Clin. Cancer Res. 24, 1872–1880 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Dudani, J. S., Warren, A. D. & Bhatia, S. N. Harnessing protease activity to improve cancer care. Annu. Rev. Cancer Biol. 2, 353–376 (2018).

    Article  Google Scholar 

  17. Martínez-Lostao, L., Anel, A. & Pardo, J. How do cytotoxic lymphocytes kill cancer cells? Clin. Cancer Res. 21, 5047–5056 (2015).

    Article  PubMed  CAS  Google Scholar 

  18. Hilderbrand, S. A. & Weissleder, R. Near-infrared fluorescence: application to in vivo molecular imaging. Curr. Opin. Chem. Biol. 14, 71–79 (2010).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  20. Savariar, E. N. et al. Real-time in vivo molecular detection of primary tumors and metastases with ratiometric activatable cell-penetrating peptides. Cancer Res. 73, 855–864 (2013).

    Article  CAS  PubMed  Google Scholar 

  21. Larimer, B. M. et al. Granzyme B PET imaging as a predictive biomarker of immunotherapy response. Cancer Res. 77, 2318–2327 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kwong, G. A. et al. Synthetic biomarkers: a twenty-first century path to early cancer detection. Nat. Rev. Cancer 21, 655–668 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  24. Lin, K. Y., Kwong, G. A., Warren, A. D., Wood, D. K. & Bhatia, S. N. Nanoparticles that sense thrombin activity as synthetic urinary biomarkers of thrombosis. ACS Nano 7, 9001–9009 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Mac, Q. D. et al. Non-invasive early detection of acute transplant rejection via nanosensors of granzyme B activity. Nat. Biomed. Eng. 3, 281–291 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kirkpatrick, J. D. et al. Urinary detection of lung cancer in mice via noninvasive pulmonary protease profiling. Sci. Transl. Med. 12, eaaw0262 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Cazanave, S. C. et al. Peptide-based urinary monitoring of fibrotic nonalcoholic steatohepatitis by mass-barcoded activity-based sensors. Sci. Transl. Med. 13, eabe8939 (2021).

    Article  CAS  PubMed  Google Scholar 

  30. Casciola-Rosen, L. et al. Mouse and human granzyme B have distinct tetrapeptide specificities and abilities to recruit the bid pathway. J. Biol. Chem. 282, 4545–4552 (2007).

    Article  CAS  PubMed  Google Scholar 

  31. Harris, J. L., Peterson, E. P., Hudig, D., Thornberry, N. A. & Craik, C. S. Definition and redesign of the extended substrate specificity of granzyme B. J. Biol. Chem. 273, 27364–27373 (1998).

    Article  CAS  PubMed  Google Scholar 

  32. Ruggles, S. W., Fletterick, R. J. & Craik, C. S. Characterization of structural determinants of granzyme B reveals potent mediators of extended substrate specificity. J. Biol. Chem. 279, 30751–30759 (2004).

    Article  CAS  PubMed  Google Scholar 

  33. He, S., Li, J., Lyu, Y., Huang, J. & Pu, K. Near-infrared fluorescent macromolecular reporters for real-time imaging and urinalysis of cancer immunotherapy. J. Am. Chem. Soc. 142, 7075–7082 (2020).

    Article  CAS  PubMed  Google Scholar 

  34. Zhang, Y. et al. Activatable polymeric nanoprobe for near-infrared fluorescence and photoacoustic imaging of T lymphocytes. Angew. Chem. Int. Ed. 133, 5986–5992 (2021).

    Article  Google Scholar 

  35. Efremova, M. et al. Targeting immune checkpoints potentiates immunoediting and changes the dynamics of tumor evolution. Nat. Commun. 9, 32 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Villanueva, J. et al. Differential exoprotease activities confer tumor-specific serum peptidome patterns. J. Clin. Invest. 116, 271–284 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Villanueva, J. et al. A sequence-specific exopeptidase activity test (SSEAT) for ‘functional’ biomarker discovery. Mol. Cell. Proteomics 7, 509–518 (2008).

    Article  CAS  PubMed  Google Scholar 

  38. Werle, M. & Bernkop-Schnürch, A. Strategies to improve plasma half life time of peptide and protein drugs. Amino Acids 30, 351–367 (2006).

    Article  CAS  PubMed  Google Scholar 

  39. Diao, L. & Meibohm, B. Pharmacokinetics and pharmacokinetic-pharmacodynamic correlations of therapeutic peptides. Clin. Pharmacokinet. 52, 855–868 (2013).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  41. Strohl, W. R. Fusion proteins for half-life extension of biologics as a strategy to make biobetters. BioDrugs 29, 215–239 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Duraiswamy, J., Kaluza, K. M., Freeman, G. J. & Coukos, G. Dual blockade of PD-1 and CTLA-4 combined with tumor vaccine effectively restores T-cell rejection function in tumors. Cancer Res. 73, 3591–3603 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Selby, M. J. et al. Preclinical development of ipilimumab and nivolumab combination immunotherapy: mouse tumor models, in vitro functional studies, and cynomolgus macaque toxicology. PLoS ONE 11, e0161779 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Brinkman, E. K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Jansen, C. S. et al. An intra-tumoral niche maintains and differentiates stem-like CD8 T cells. Nature 576, 465–470 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Paley, M. A. et al. Progenitor and terminal subsets of CD8+ T cells cooperate to contain chronic viral infection. Science 338, 1220–1225 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Thommen, D. S. et al. A transcriptionally and functionally distinct PD-1+ CD8+ T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade. Nat. Med. 24, 994–1004 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Gupta, P. K. et al. CD39 expression identifies terminally exhausted CD8+ T cells. PLoS Pathog. 11, e1005177 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Canale, F. P. et al. CD39 expression defines cell exhaustion in tumor-infiltrating CD8+ T cells. Cancer Res. 78, 115–128 (2018).

    Article  CAS  PubMed  Google Scholar 

  52. Horton, B. L., Williams, J. B., Cabanov, A., Spranger, S. & Gajewski, T. F. Intratumoral CD8+ T-cell apoptosis is a major component of T-cell dysfunction and impedes anti-tumor immunity. Cancer Immunol. Res. 6, 14–24 (2018).

    Article  CAS  PubMed  Google Scholar 

  53. Liberzon, A. et al. The molecular signatures database hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Schwartz, L. H. et al. RECIST 1.1 – update and clarification: from the RECIST Committee. Eur. J. Cancer 62, 132–137 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Arlot, S. & Celisse, A. A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40–79 (2010).

    Article  Google Scholar 

  56. Patel, S. P. & Kurzrock, R. PD-L1 expression as a predictive biomarker in cancer immunotherapy. Mol. Cancer Ther. 14, 847–856 (2015).

    Article  CAS  PubMed  Google Scholar 

  57. Chan, T. A. et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann. Oncol. 30, 44–56 (2019).

    Article  CAS  PubMed  Google Scholar 

  58. Cristescu, R. et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade–based immunotherapy. Science 362, eaar3593 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Chang, L., Chang, M., Chang, H. M. & Chang, F. Microsatellite instability: a predictive biomarker for cancer immunotherapy. Appl. Immunohistochem. Mol. Morphol. 26, e15 (2018).

    Article  CAS  PubMed  Google Scholar 

  60. Bratman, S. V. et al. Personalized circulating tumor DNA analysis as a predictive biomarker in solid tumor patients treated with pembrolizumab. Nat. Cancer 1, 873–881 (2020).

    Article  CAS  PubMed  Google Scholar 

  61. Tietze, J. K. et al. The proportion of circulating CD45RO+CD8+ memory T cells is correlated with clinical response in melanoma patients treated with ipilimumab. Eur. J. Cancer 75, 268–279 (2017).

    Article  CAS  PubMed  Google Scholar 

  62. Chen, P.-L. et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov. 6, 827–837 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Jiang, P. et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med. 24, 1550–1558 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Amaria, R. N. et al. Neoadjuvant immune checkpoint blockade in high-risk resectable melanoma. Nat. Med. 24, 1649–1654 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Grasso, C. S. et al. Conserved interferon-γ signaling drives clinical response to immune checkpoint blockade therapy in melanoma. Cancer Cell 38, 500–515.e3 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Larimer, B. M. et al. The effectiveness of checkpoint inhibitor combinations and administration timing can be measured by granzyme B PET imaging. Clin. Cancer Res. 25, 1196–1205 (2019).

    Article  CAS  PubMed  Google Scholar 

  68. Nguyen, A. et al. Granzyme B nanoreporter for early monitoring of tumor response to immunotherapy. Sci. Adv. 6, eabc2777 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Zhao, N. et al. In vivo measurement of granzyme proteolysis from activated immune cells with PET. ACS Cent. Sci. 7, 1638–1649 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Carter, H. B. & Pearson, J. D. PSA velocity for the diagnosis of early prostate cancer. A new concept. Urol. Clin. North Am. 20, 665–670 (1993).

    Article  CAS  PubMed  Google Scholar 

  71. Vickers, A. J. et al. Prostate-specific antigen velocity for early detection of prostate cancer. Eur. Urol. 56, 753–760 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  72. La Thangue, N. B. & Kerr, D. J. Predictive biomarkers: a paradigm shift towards personalized cancer medicine. Nat. Rev. Clin. Oncol. 8, 587–596 (2011).

    Article  PubMed  CAS  Google Scholar 

  73. Salgado, R. et al. Steps forward for cancer precision medicine. Nat. Rev. Drug Discov. 17, 1–2 (2018).

    Article  CAS  PubMed  Google Scholar 

  74. Brown, N. A. & Elenitoba-Johnson, K. S. J. Enabling precision oncology through precision diagnostics. Annu. Rev. Pathol. 15, 97–121 (2020).

    Article  CAS  PubMed  Google Scholar 

  75. Ramos-Casals, M. et al. Immune-related adverse events of checkpoint inhibitors. Nat. Rev. Dis. Primers 6, 38 (2020).

    Article  PubMed  Google Scholar 

  76. Esfahani, K. et al. Moving towards personalized treatments of immune-related adverse events. Nat. Rev. Clin. Oncol. 17, 504–515 (2020).

    Article  CAS  PubMed  Google Scholar 

  77. Postow, M. A., Sidlow, R. & Hellmann, M. D. Immune-related adverse events associated with immune checkpoint blockade. N. Engl. J. Med. https://doi.org/10.1056/NEJMra1703481 (2018).

  78. Johnson, D. B. et al. Fulminant myocarditis with combination immune checkpoint blockade. N. Engl. J. Med. 375, 1749–1755 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Goldinger, S. M. et al. Cytotoxic cutaneous adverse drug reactions during anti-PD-1 therapy. Clin. Cancer Res. 22, 4023–4029 (2016).

    Article  CAS  PubMed  Google Scholar 

  80. Hua, C. et al. Association of vitiligo with tumor response in patients with metastatic melanoma treated with pembrolizumab. JAMA Dermatol. 152, 45–51 (2016).

    Article  PubMed  Google Scholar 

  81. Zhang, X. et al. Hepatitis B virus reactivation in cancer patients with positive hepatitis B surface antigen undergoing PD-1 inhibition. J. Immunother. Cancer 7, 322 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Del Castillo, M. et al. The spectrum of serious infections among patients receiving immune checkpoint blockade for the treatment of melanoma. Clin. Infect. Dis. 63, 1490–1493 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Fujita, K. et al. Emerging concerns of infectious diseases in lung cancer patients receiving immune checkpoint inhibitor therapy. Respir. Med. 146, 66–70 (2019).

    Article  PubMed  Google Scholar 

  84. Hutchinson, J. A. et al. Virus-specific memory T cell responses unmasked by immune checkpoint blockade cause hepatitis. Nat. Commun. 12, 1439 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Beerli, R. R., Hell, T., Merkel, A. S. & Grawunder, U. Sortase enzyme-mediated generation of site-specifically conjugated antibody drug conjugates with high in vitro and in vivo potency. PLoS ONE 10, e0131177 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Jeger, S. et al. Site-specific and stoichiometric modification of antibodies by bacterial transglutaminase. Angew. Chem. Int. Ed. Engl. 49, 9995–9997 (2010).

    Article  CAS  PubMed  Google Scholar 

  87. Yu, C. et al. Proximity-induced site-specific antibody conjugation. Bioconjug. Chem. 29, 3522–3526 (2018).

    Article  CAS  PubMed  Google Scholar 

  88. Puente, X. S., Sánchez, L. M., Overall, C. M. & López-Otín, C. Human and mouse proteases: a comparative genomic approach. Nat. Rev. Genet. 4, 544–558 (2003).

    Article  CAS  PubMed  Google Scholar 

  89. Kaiserman, D. et al. The major human and mouse granzymes are structurally and functionally divergent. J. Cell Biol. 175, 619–630 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Aguilera, T. A., Olson, E. S., Timmers, M. M., Jiang, T. & Tsien, R. Y. Systemic in vivo distribution of activatable cell penetrating peptides is superior to that of cell penetrating peptides. Integr. Biol. 1, 371–381 (2009).

    Article  CAS  Google Scholar 

  91. 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  PubMed  PubMed Central  CAS  Google Scholar 

  92. Timmer, J. C. & Salvesen, G. S. Caspase substrates. Cell Death Differ. 14, 66–72 (2007).

    Article  CAS  PubMed  Google Scholar 

  93. Poreba, M. et al. Unnatural amino acids increase sensitivity and provide for the design of highly selective caspase substrates. Cell Death Differ. 21, 1482–1492 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Rut, W. et al. Recent advances and concepts in substrate specificity determination of proteases using tailored libraries of fluorogenic substrates with unnatural amino acids. Biol. Chem. 396, 329–337 (2015).

    Article  CAS  PubMed  Google Scholar 

  95. Miller, M. A. et al. Proteolytic activity matrix analysis (PrAMA) for simultaneous determination of multiple protease activities. Integr. Biol. 3, 422–438 (2011).

    Article  CAS  Google Scholar 

  96. Zhuang, Q., Holt, B. A., Kwong, G. A. & Qiu, P. Deconvolving multiplexed protease signatures with substrate reduction and activity clustering. PLoS Comput. Biol. 15, e1006909 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Austin, R. J. et al. TriTACs, a novel class of T-cell–engaging protein constructs designed for the treatment of solid tumors. Mol. Cancer Ther. 20, 109–120 (2021).

    Article  CAS  PubMed  Google Scholar 

  98. Triplett, T. A. et al. Reversal of indoleamine 2,3-dioxygenase–mediated cancer immune suppression by systemic kynurenine depletion with a therapeutic enzyme. Nat. Biotechnol. 36, 758–764 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Clark, M. F., Lister, R. M. & Bar-Joseph, M. ELISA techniques. Methods Enzymol 118, 742–766 (1986).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was funded by the NIH Director’s New Innovator Award DP2HD091793 and the National Cancer Institute R01 grant 5R01CA237210. Q.D.M. and A.S. were supported by the NSF Graduate Research Fellowships Program (Grant No. DGE-1650044). G.A.K. holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. P.Q. is an ISAC Marylou Ingram Scholar and a Carol Ann and David D. Flanagan Faculty Fellow. This work was performed in part at the Georgia Tech Institute for Electronics and Nanotechnology, a member of the National Nanotechnology Coordinated Infrastructure, which is supported by the National Science Foundation (Grant ECCS-1542174). This content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank the staff at Georgia Tech mass spectrometry core, flow cytometry analysis core and the animal facility for their assistance in performing our studies.

Author information

Author notes

  1. These authors contributed equally: Quoc D. Mac, Anirudh Sivakumar.

Authors and Affiliations

  1. Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech College of Engineering and Emory School of Medicine, Atlanta, GA, USA

    Quoc D. Mac, Anirudh Sivakumar, Hathaichanok Phuengkham, Congmin Xu, James R. Bowen, Fang-Yi Su, Samuel Z. Stentz, Hyoungjun Sim, Adrian M. Harris, Tonia T. Li, Peng Qiu & Gabriel A. Kwong

  2. Parker H. Petit Institute for Bioengineering and Bioscience, Atlanta, GA, USA

    Peng Qiu & Gabriel A. Kwong

  3. The Georgia Immunoengineering Consortium, Emory University and Georgia Tech, Atlanta, GA, USA

    Peng Qiu & Gabriel A. Kwong

  4. Winship Cancer Institute, Emory University, Atlanta, GA, USA

    Peng Qiu & Gabriel A. Kwong

  5. Institute for Electronics and Nanotechnology, Georgia Tech, Atlanta, GA, USA

    Gabriel A. Kwong

  6. Integrated Cancer Research Center, Georgia Tech, Atlanta, GA, USA

    Gabriel A. Kwong

Authors
  1. Quoc D. Mac
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anirudh Sivakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hathaichanok Phuengkham
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Congmin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. James R. Bowen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fang-Yi Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Samuel Z. Stentz
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hyoungjun Sim
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Adrian M. Harris
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Tonia T. Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Peng Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Gabriel A. Kwong
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Q.D.M., J.R.B. and G.A.K. conceived the idea. Q.D.M., A.S., H.P., C.X., J.R.B, F.-Y.S., S.Z.S., P.Q. and G.A.K. designed experiments and interpreted results. Q.D.M., A.S., H.P., C.X., J.R.B, F.-Y.S., S.Z.S., H.S., A.M.H. and T.T.L. carried out the experiments. Q.D.M., A.S. and G.A.K. wrote the manuscript.

Corresponding author

Correspondence to Gabriel A. Kwong.

Ethics declarations

Competing interests

G.A.K. is co-founder of and serves as consultant to Glympse Bio and Satellite Bio. This study could affect his personal financial status. The terms of this arrangement have been reviewed and approved by Georgia Tech in accordance with its conflict-of-interest policies. Q.D.M., J.R.B. and G.A.K are listed as inventors on a patent application (PCT/US2019/050530) pertaining to the results of the paper. The patent applicant is the Georgia Tech Research Corporation. The names of the inventors are Quoc Mac, James Bowen and Gabriel Kwong. The patent is currently pending/published (publication number WO2020055952A1). The mass-barcoded antibody–sensor conjugates and related applications are covered in this patent.

Peer review

Peer review information

Nature Biomedical Engineering thanks Charles Craik, Matt Trau and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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 Synthesis strategy for antibody-sensor conjugates for biodistribution of antibody and GzmB cleavage.

a, Synthetic scheme of antibody-sensor conjugate labelled with near infrared fluorophore VT750 to determine antibody biodistribution. b, Synthetic scheme of NIR activatable GzmB probe, consisting of fluorescently quenched GzmB substrate conjugated to αPD1 antibody.

Extended Data Fig. 2 Biodistribution of antibody-sensor conjugates and GzmB cleavage.

a, Schematic of antibody-sensor conjugate labelled with near infrared fluorophore VT750 to determine antibody biodistribution. b, Representative whole organ images and c, quantification of distribution of fluorescent signal in tumour and major organs (dLNs, tumour draining lymph nodes) (two-way ANOVA with Sidak’s post-test and correction for multiple comparisons, ns = not significant, n = 5 biological replicates, error bars depict s.e.m.). d, Schematic and in vitro cleavage of NIR activatable GzmB probe labelled with quenched near infrared fluorophore 800CW to localize site of probe activation (FC, fold change; ****P < 0.0001, n = 3 technically independent wells, error bars depict s.e.m.). e, Representative whole organ images and f, quantification of reporter fluorescence after treatment with Iso-GS or αPD1-GS (two-way ANOVA with Sidak’s post-test and correction for multiple comparisons, *P = 0.040, n = 4-5 biological replicates, error bars depict s.e.m.).

Extended Data Fig. 3 Diagnostic performance of αPD1-GS in ICB nonresponsive models.

a, Average and b, individual tumour growth curves of CT26 tumour bearing mice treated with either αCTLA4-GS or matched IgG2 isotype control (Iso-GS) (two-way ANOVA with Sidak’s post-test and correction for multiple comparisons, ns = not significant, n = 10-11 biological replicates, error bars depict s.e.m.). Black arrows denote the treatment time points. c, Normalized urine fluorescence of mice with CT26 tumours after each administration of αCTLA4-GS or Iso-GS (two-way ANOVA with Sidak’s post-test and correction for multiple comparisons, ns = not significant, n = 10-11 biological replicates). d, Average and e, individual tumour growth curves of CT26 tumour bearing mice treated with αPD1-GS or matched IgG1 isotype control (Iso-GS) (two-way ANOVA with Sidak’s post-test and correction for multiple comparisons, ns = not significant, n = 6 biological replicates, error bars depict s.e.m.). Black arrows denote the treatment time points. f, Normalized urine fluorescence of mice with CT26 tumours after each administration of αPD1-GS or Iso-GS (two-way ANOVA with Sidak’s post-test and correction for multiple comparisons, ns = not significant, n = 6 biological replicates).

Source data

Extended Data Fig. 4 Differential expression analysis of proteases in ICB response and resistance.

a, Heatmaps showing row-normalized expression (FPKM) of proteases differentially expressed between αPD1-treated WT tumours and IgG1-treated controls (n = 5 biological replicates). b, (Left) Volcano plots summarizing differentially expressed proteases between αPD1-treated B2m−/− and Jak1−/− MC38 tumours (n = 5 biological replicates). The threshold for differentially expressed genes (opaque dots) was defined as P value ≤ 0.05 and |log2(fold change)| ≥ 1. (Right) Heatmaps showing row-normalized expression (FPKM) of proteases differentially expressed between B2m−/− and Jak1−/− MC38 tumours (n = 5 biological replicates). c, (Left) Volcano plots summarizing differentially expressed proteases between human tumours from responders (CR + PR) and non-responders (PD) (n = 5 independent patient samples). The threshold for differentially expressed genes was defined as P value ≤ 0.01 and |log2(fold change)| ≥ 1. (Right) Heatmaps showing row-normalized expression (FPKM) of proteases differentially expressed between human tumours from responders (CR + PR) and non-responders (PD) (n = 5 independent patient samples).

Supplementary information

Supplementary Information

Supplementary figures and tables.

Reporting Summary

Source data

Source Data Fig. 1

Source data for tumour burden.

Source Data Fig. 3

Source data for tumour burden.

Source Data Fig. 4

Source data for tumour burden.

Source Data Extended Data Fig. 3

Source data for tumour burden.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mac, Q.D., Sivakumar, A., Phuengkham, H. et al. Urinary detection of early responses to checkpoint blockade and of resistance to it via protease-cleaved antibody-conjugated sensors. Nat. Biomed. Eng 6, 310–324 (2022). https://doi.org/10.1038/s41551-022-00852-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-022-00852-y

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer