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An integrated magneto-electrochemical device for the rapid profiling of tumour extracellular vesicles from blood plasma

Abstract

Assays for cancer diagnosis via the analysis of biomarkers on circulating extracellular vesicles (EVs) typically have lengthy sample workups, limited throughput or insufficient sensitivity, or do not use clinically validated biomarkers. Here we report the development and performance of a 96-well assay that integrates the enrichment of EVs by antibody-coated magnetic beads and the electrochemical detection, in less than one hour of total assay time, of EV-bound proteins after enzymatic amplification. By using the assay with a combination of antibodies for clinically relevant tumour biomarkers (EGFR, EpCAM, CD24 and GPA33) of colorectal cancer (CRC), we classified plasma samples from 102 patients with CRC and 40 non-CRC controls with accuracies of more than 96%, prospectively assessed a cohort of 90 patients, for whom the burden of tumour EVs was predictive of five-year disease-free survival, and longitudinally analysed plasma from 11 patients, for whom the EV burden declined after surgery and increased on relapse. Rapid assays for the detection of combinations of tumour biomarkers in plasma EVs may aid cancer detection and patient monitoring.

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Fig. 1: HiMEX approach for clinical EV analyses.
Fig. 2: HiMEX assay optimization and characterization.
Fig. 3: EV profiling for CRC detection.
Fig. 4: Analyses of prospective cohorts for CRC diagnosis.
Fig. 5: HiMEX analyses of longitudinal samples from patients with CRC.

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw patient datasets generated and analysed during the study are available from the corresponding authors on reasonable request, subject to approval from the Institutional Review Board of the Kyungpook National University Hospital. For initial marker selection, we used the public databases, the Human Protein Atlas (https://www.proteinatlas.org) and UniProt (https://www.uniprot.org). Source data are provided with this paper.

Code availability

Source codes for the marker selection are available at https://csb.mgh.harvard.edu/bme_software.

References

  1. 1.

    Heitzer, E., Haque, I. S., Roberts, C. E. S. & Speicher, M. R. Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat. Rev. Genet. 20, 77–88 (2018).

    Google Scholar 

  2. 2.

    Siravegna, G., Marsoni, S., Siena, S. & Bardelli, A. Integrating liquid biopsies into the management of cancer. Nat. Rev. Clin. Oncol. 14, 531–548 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Chi, K. R. The tumour trail left in blood. Nature 532, 269–271 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  4. 4.

    Pantel, K. & Alix-Panabieres, C. Real-time liquid biopsy in cancer patients: fact or fiction? Cancer Res. 73, 6384–6388 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  5. 5.

    Théry, C., Ostrowski, M. & Segura, E. Membrane vesicles as conveyors of immune responses. Nat. Rev. Immunol. 9, 581–593 (2009).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  6. 6.

    Xu, R. et al. Extracellular vesicles in cancer—implications for future improvements in cancer care. Nat. Rev. Clin. Oncol. 15, 617–638 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  7. 7.

    Shao, H. et al. Protein typing of circulating microvesicles allows real-time monitoring of glioblastoma therapy. Nat. Med. 18, 1835–1840 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Choi, D., Spinelli, C., Montermini, L. & Rak, J. Oncogenic regulation of extracellular vesicle proteome and heterogeneity. Proteomics 19, 1800169 (2019).

    Article  CAS  Google Scholar 

  9. 9.

    Skog, J. et al. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat. Cell Biol. 10, 1470–1476 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Shao, H. et al. Chip-based analysis of exosomal mRNA mediating drug resistance in glioblastoma. Nat. Commun. 6, 6999 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  11. 11.

    Yoshioka, Y. et al. Ultra-sensitive liquid biopsy of circulating extracellular vesicles using ExoScreen. Nat. Commun. 5, 3591 (2014).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  12. 12.

    Skotland, T., Sandvig, K. & Llorente, A. Lipids in exosomes: current knowledge and the way forward. Prog. Lipid Res. 66, 30–41 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  13. 13.

    EL Andaloussi, S., Mäger, I., Breakefield, X. O. & Wood, M. J. Extracellular vesicles: biology and emerging therapeutic opportunities. Nat. Rev. Drug Discov. 12, 347–357 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  14. 14.

    Im, H. et al. Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor. Nat. Biotechnol. 32, 490–495 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Liu, C. et al. Low-cost thermophoretic profiling of extracellular-vesicle surface proteins for the early detection and classification of cancers. Nat. Biomed. Eng. 3, 183–193 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  16. 16.

    Jeong, S. et al. Integrated magneto–electrochemical sensor for exosome analysis. ACS Nano 10, 1802–1809 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Yang, K. S. et al. Multiparametric plasma EV profiling facilitates diagnosis of pancreatic malignancy. Sci. Transl. Med. 9, eaal3226 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Zhang, P. et al. Ultrasensitive detection of circulating exosomes with a 3D-nanopatterned microfluidic chip. Nat. Biomed. Eng. 3, 438–451 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Liang, K. et al. Nanoplasmonic quantification of tumour-derived extracellular vesicles in plasma microsamples for diagnosis and treatment monitoring. Nat. Biomed. Eng. 1, 0021 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Lewis, J. M. et al. Integrated analysis of exosomal protein biomarkers on alternating current electrokinetic chips enables rapid detection of pancreatic cancer in patient blood. ACS Nano 12, 3311–3320 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  21. 21.

    Shao, H. et al. New technologies for analysis of extracellular vesicles. Chem. Rev. 118, 1917–1950 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Locker, G. Y. et al. ASCO 2006 update of recommendations for the use of tumour markers in gastrointestinal cancer. J. Clin. Oncol. 24, 5313–5327 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  23. 23.

    Tao, S., Hundt, S., Haug, U. & Brenner, H. Sensitivity estimates of blood-based tests for colorectal cancer detection: impact of overrepresentation of advanced stage disease. Am. J. Gastroenterol. 106, 242–253 (2011).

    PubMed  Article  PubMed Central  Google Scholar 

  24. 24.

    Park, J. et al. Integrated kidney exosome analysis for the detection of kidney transplant rejection. ACS Nano 11, 11041–11046 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Fraser, K. et al. Characterization of single microvesicles in plasma from glioblastoma patients. Neuro Oncol. 21, 606–615 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    Jeppesen, D. K. et al. Reassessment of exosome composition. Cell 177, 428–445 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Lee, K. et al. Multiplexed profiling of single extracellular vesicles. ACS Nano 12, 494–503 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Ramirez, M. I. et al. Technical challenges of working with extracellular vesicles. Nanoscale 10, 881–906 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  29. 29.

    Zhao, L. H. et al. CD44v6 expression in patients with stage II or stage III sporadic colorectal cancer is superior to CD44 expression for predicting progression. Int. J. Clin. Exp. Pathol. 8, 692–701 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Weichert, W. et al. Cytoplasmic CD24 expression in colorectal cancer independently correlates with shortened patient survival. Clin. Cancer Res. 11, 6574–6581 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  31. 31.

    Weichert, W., Knösel, T., Bellach, J., Dietel, M. & Kristiansen, G. ALCAM/CD166 is overexpressed in colorectal carcinoma and correlates with shortened patient survival. J. Clin. Pathol. 57, 1160–1164 (2004).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Lee, C. H. et al. The prognostic role of STEAP1 expression determined via immunohistochemistry staining in predicting prognosis of primary colorectal cancer: a survival analysis. Int. J. Mol. Sci. 17, 592 (2016).

    PubMed Central  Article  CAS  Google Scholar 

  33. 33.

    Ingebrigtsen, V. A. et al. B7-H3 expression in colorectal cancer: nuclear localization strongly predicts poor outcome in colon cancer. Int. J. Cancer 131, 2528–2536 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  34. 34.

    Deng, Y. et al. ALDH1 is an independent prognostic factor for patients with stages II-III rectal cancer after receiving radiochemotherapy. Br. J. Cancer 110, 430–434 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  35. 35.

    Ong, C. W. et al. CD133 expression predicts for non-response to chemotherapy in colorectal cancer. Mod. Pathol. 23, 450–457 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  36. 36.

    Peters, G. J. et al. Induction of thymidylate synthase as a 5-fluorouracil resistance mechanism. Biochim. Biophys. Acta 1587, 194–205 (2002).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  37. 37.

    Ekblad, L., Kjellström, J. & Johnsson, A. Reduced drug accumulation is more important in acquired resistance against oxaliplatin than against cisplatin in isogenic colon cancer cells. Anticancer Drugs 21, 523–531 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  38. 38.

    Mouradov, D. et al. Colorectal cancer cell lines are representative models of the main molecular subtypes of primary cancer. Cancer Res. 74, 3238–3247 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  39. 39.

    Skog, J. et al. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat. Cell Biol. 10, 1470 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Garinchesa, P. et al. Organ-specific expression of the colon cancer antigen A33, a cell surface target for antibody-based therapy. Int. J. Oncol. 9, 465–471 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Zou, K. H., O’Malley, A. J. & Mauri, L. Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation 115, 654–657 (2007).

    PubMed  Article  PubMed Central  Google Scholar 

  42. 42.

    Liu, J. et al. Down-regulation of GADD45A enhances chemosensitivity in melanoma. Sci. Rep. 8, 4111 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  43. 43.

    Huang, P. et al. Chemotherapy-driven increases in the CDKN1A/PTN/PTPRZ1 axis promote chemoresistance by activating the NF-κB pathway in breast cancer cells. Cell Commun. Signal. 16, 92 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Cuadrado, A. et al. Therapeutic targeting of the NRF2 and KEAP1 partnership in chronic diseases. Nat. Rev. Drug Discov. 18, 295–317 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  45. 45.

    Derdak, Z. et al. The mitochondrial uncoupling protein-2 promotes chemoresistance in cancer cells. Cancer Res. 68, 2813–2819 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Nicolussi, A., D’Inzeo, S., Capalbo, C., Giannini, G. & Coppa, A. The role of peroxiredoxins in cancer. Mol. Clin. Oncol. 6, 139–153 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Wang, J. & Li, Y. CD36 tango in cancer: signaling pathways and functions. Theranostics 9, 4893–4908 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Romano, G. et al. The TGF-β pathway is activated by 5-fluorouracil treatment in drug resistant colorectal carcinoma cells. Oncotarget 7, 22077–22091 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Wu, J. et al. Heat shock proteins and cancer. Trends Pharmacol. Sci. 38, 226–256 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  50. 50.

    Sharma, A., Upadhyay, A. K. & Bhat, M. K. Inhibition of Hsp27 and Hsp40 potentiates 5-fluorouracil and carboplatin mediated cell killing in hepatoma cells. Cancer Biol. Ther. 8, 2106–2113 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  51. 51.

    Shi, Z. et al. Activation of the PERK-ATF4 pathway promotes chemo-resistance in colon cancer cells. Sci. Rep. 9, 3210 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. 52.

    Phallen, J. et al. Direct detection of early-stage cancers using circulating tumour DNA. Sci. Transl. Med. 9, eaan2415 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  53. 53.

    Hothorn, T. & Lausen, B. On the exact distribution of maximally selected rank statistics. Comput. Stat. Data Anal. 43, 121–137 (2003).

    Article  Google Scholar 

  54. 54.

    Duffy, M. J. et al. Clinical utility of biochemical markers in colorectal cancer. Eur. J. Cancer 39, 718–727 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  55. 55.

    Das, J. et al. An electrochemical clamp assay for direct, rapid analysis of circulating nucleic acids in serum. Nat. Chem. 7, 569–575 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  56. 56.

    Thierry, A. R. et al. Clinical validation of the detection of KRAS and BRAF mutations from circulating tumour DNA. Nat. Med. 20, 430–435 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  57. 57.

    Network, C. G. A. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).

    Article  CAS  Google Scholar 

  58. 58.

    Siravegna, G. et al. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat. Med. 21, 795–801 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Hoshino, A. et al. Tumour exosome integrins determine organotropic metastasis. Nature 527, 329–335 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Peinado, H. et al. Pre-metastatic niches: organ-specific homes for metastases. Nat. Rev. Cancer 17, 302–317 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  61. 61.

    Keklikoglou, I. et al. Chemotherapy elicits pro-metastatic extracellular vesicles in breast cancer models. Nat. Cell Biol. 21, 190–202 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  62. 62.

    Syn, N., Wang, L., Sethi, G., Thiery, J. P. & Goh, B. C. Exosome-mediated metastasis: from epithelial-mesenchymal transition to escape fromimmunosurveillance. Trends Pharmacol. Sci. 37, 606–617 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  63. 63.

    Van Deun, J. et al. The impact of disparate isolation methods for extracellular vesicles on downstream RNA profiling. J. Extracell. Vesicles 3, 24858 (2014).

    Article  Google Scholar 

  64. 64.

    DeLong, E. R., DeLong, D. M. & Clarke-Pearson, D. L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837–845 (1988).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  65. 65.

    Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12, 77 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Hothorn, T. & Lausen, B. On the exact distribution of maximally selected rank statistics. Comp. Stat. Data Analysis 43, 121–137 (2003).

    Article  Google Scholar 

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Acknowledgements

We thank X. O. Breakefield (Massachusetts General Hospital) for discussions. This work was supported in part by grants from the V-Foundation for Cancer Research (R. Weissleder, C.M.C.); the American Cancer Society (C.M.C.); the Robert Wood Johnson Foundation/Amos Medical Faculty Development Program (C.M.C.); US National Institutes of Health (NIH) grant numbers R01CA229777 (H. Lee), R21DA049577 (H. Lee), R01CA204019 (R. Weissleder), U01CA233360 (H. Lee, C.M.C.), TR000931 (B.S.C.), U01CA230697 (B.S.C., L.B.), R01CA239078 (H. Lee, B.S.C.), R01CA237500 (H. Lee, B.S.C.) and CA069246 (B.S.C.); US DOD-W81XWH1910199 (H. Lee) and DOD-W81XWH1910194 (H. Lee); MGH Scholar Fund (H. Lee), MGH Fund for Medical Discovery Fellowship (H.-Y.L.); and Basic Science Research Program grants NRF-2019R1C1C1008792 (J.P.), NRF-2020R1A4A1016093 (J.P.), and NRF-2017M3A9G8083382 (J.S.P.) from the Ministry of Education, South Korea.

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J.P., J.S.P., C.M.C., R. Weissleder and H. Lee designed the study, prepared the figures and wrote the manuscript. J.S.P. and G.-S.C. collected patient samples. G.Y. performed tissue immunohistochemistry staining. C.-H.H. developed the HiMEX system. J.P., A.J., H.-Y.L., J.V.D., H. Li and J.M. performed research and analysed data. L.W., L.B. and B.S.C. guided mRNA analysis. J.S.P., G.-S.C., C.M.C. and R. Weissleder analysed clinical data. K.C., R. Wang and H. Lee performed statistical analyses.

Corresponding authors

Correspondence to Ralph Weissleder or Hakho Lee.

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Competing interests

The authors declare the filing of a patent (US20190346434A1) that was assigned to and handled by Massachusetts General Hospital. The following disclosures are not related to the subject matter of this work. R.Weissleder is a consultant to ModeRNA, Tarveda Pharmaceuticals, Lumicell, Seer, Earli, Alivio Therapeutics, Aikili Biosystems and Accure Health. H. Lee is a consultant to Exosome Diagnostics, Accure Health and Aikili Biosystems.

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Peer review information Nature Biomedical Engineering thanks Tony Hu, Philip Stahl and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Park, J., Park, J.S., Huang, CH. et al. An integrated magneto-electrochemical device for the rapid profiling of tumour extracellular vesicles from blood plasma. Nat Biomed Eng 5, 678–689 (2021). https://doi.org/10.1038/s41551-021-00752-7

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