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Tracking extracellular vesicle phenotypic changes enables treatment monitoring in melanoma

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Science Advances  26 Feb 2020:
Vol. 6, no. 9, eaax3223
DOI: 10.1126/sciadv.aax3223
  • Fig. 1 Schematic for EV phenotyping by EPAC.

    (A) A melanoma cell with a BRAF V600E mutation secretes EVs into circulation or cell culture medium. (B) The sample is directly injected into EPAC, where the applied nanomixing fluid flow increases EV collisions with the capture antibody and SERS nanotags and shears off nontarget molecules (e.g., protein aggregates and apoptotic bodies) and free SERS nanotags. (C) The characterization of EV phenotypes is performed by SERS mapping. The false-color SERS spectral images are established on the basis of the characteristic peak intensities of SERS nanotags (MCSP-MBA, red; MCAM-TFMBA, blue; ErbB3-DTNB, green; LNGFR-MPY, yellow). (D) EV phenotypes defined by the relative expression levels of four biomarkers are extracted from the average signal spectra of false-color SERS spectral images. EV phenotypes are unique to each EV subpopulation. By analyzing EV samples before, during, and after BRAF inhibitor treatment, the phenotypic evolution can be tracked to provide information on treatment responses and early signs of drug resistance.

  • Fig. 2 EPAC sensitivity.

    The EPAC sensitivity was studied by analyzing designated concentrations of SK-MEL-28 cell–derived EVs from (A) the conditioned culture medium using an anti-CD63 functionalized EPAC and (B) the simulated patient plasma using an anti-MCSP functionalized EPAC, followed by labeling with MCSP-MBA SERS nanotags. The left side shows the representative false-color SERS spectral images, and the right side is the concentration-dependent average SERS intensity at 1075 cm−1. Data are represented as means ± standard deviation, where error bars represent standard deviation of three separate experiments. Means not sharing a common letter are significantly different (P < 0.05). Scale bars, 10 μm. a.u., arbitrary units.

  • Fig. 3 Anti-CD63 functionalized EPAC specificity.

    The specificity was studied using EV samples released from SK-MEL-28 and MCF7 cell lines, as well as control experiments including (++) EV-free cell culture medium, (−+) without the CD63 capture antibody, and (+−) with nontarget CD45 detection antibodies on SERS nanotags. (A) The expressions of MCSP, MCAM, ErbB3, and LNGFR in SK-MEL-28 and MCF7 cells were detected by flow cytometry. (B) Representative false-color SERS spectral images, (C) average SERS spectra obtained from corresponding SERS mapping datasets, and (D) average SERS intensities at 1075 cm−1 (red, MCSP), 1375 cm−1 (blue, MCAM), 1335 cm−1 (green, ErbB3), and 1000 cm−1 (yellow, LNGFR). Data in (D) are represented as means ± standard deviation, where error bars represent standard deviation of three separate experiments. Scale bars, 10 μm.

  • Fig. 4 The anti-CD63 functionalized EPAC for monitoring phenotypic changes of EVs from melanoma patient–derived cell lines in response to BRAF inhibitor treatment.

    EVs released from (A) LM-MEL-64 cells without treatment were used as a control and followed for 30 days. EVs derived from (B) LM-MEL-33, (C) SK-MEL-28, (D) LM-MEL-64, and (E) LM-MEL-35 cell lines were collected before (day 0), during (days 3 to 30), and after treatment (days 33 and 39). (A to E) Average biomarker signals are represented by red (MCSP), blue (MCAM), green (ErbB3), and yellow (LNGFR). LM-MEL-35 cell line is BRAF wild type but NRAS mutant, and the other three cell lines are BRAF mutant. Data in (A) to (E) are represented as means ± standard deviation, where error bars represent standard deviation of three separate experiments. (F to J) Clustering of EV populations before, during, and after treatment via LDA of SERS signals.

  • Fig. 5 The anti-MCSP functionalized EPAC for phenotyping of plasma EVs from melanoma patients.

    (A) EV phenotypes of 15 melanoma samples (P1 to P15) and 12 healthy controls (H1 to H12). P1, P4, P7, and P9 are from the same patient but different time points, as are P5 and P10. (B) Representative false-color SERS spectral images and (C) corresponding average SERS spectra from patient and normal samples (P1, P8, and H1). For (A) and (B), the biomarker signals are represented by red (MCSP), blue (MCAM), green (ErbB3), and yellow (LNGFR). Data in (A) are represented as means ± standard deviation, where error bars represent standard deviation of three separate experiments. Scale bars, 10 μm.

  • Fig. 6 The anti-MCSP functionalized EPAC for monitoring EV phenotypic evolution of patients 16 and 17 during targeted therapies.

    (A) Patient 16 was treated with the BRAF inhibitor monotherapy (dabrafenib). The radiological imaging test indicated that this patient showed stable disease (SD) on day 143 and developed progressive disease (PD) after cessation of treatment (day 263). (B) Patient 17 received the combination treatment of BRAF and MEK inhibitors (dabrafenib and trametinib). This patient showed stable disease on day 120 and progressive disease at the third visit (day 339). Data are represented as means ± standard deviation, where error bars represent standard deviation of three separate experiments. Means not sharing a common letter are significantly different (P < 0.05).

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/9/eaax3223/DC1

    Fig. S1. EPAC design and functionalization.

    Fig. S2. EPAC-captured EV characterization.

    Fig. S3. Western blot analysis of isolated EVs from SK-MEL-28 and MCF7 cells.

    Fig. S4. Performance evaluation of the anti-CD63, anti-CD9, or anti-CD81 functionalized EPAC for detection of MCSP-positive EVs from SK-MEL-28 cells.

    Fig. S5. Monitoring the changes of individual biomarker levels in EVs from drug-treated melanoma cell lines, using the anti-CD63 functionalized EPAC.

    Fig. S6. Effect of cell debris on the anti-CD63 functionalized EPAC performance.

    Fig. S7. Anti-MCSP functionalized EPAC specificity.

    Fig. S8. The ErbB3 expression in EVs derived from melanoma patient (P1 to P10) and normal plasma (H1 to H5) samples, measured with a commercial ELISA kit.

    Fig. S9. The anti-MCSP functionalized EPAC for tracking EV phenotypic changes of patients 18 to 23 during targeted therapies.

    Table S1. The anti-MCSP functionalized EPAC for measurements of plasma EVs from 12 healthy donors (H1 to H12) and 8 melanoma patients (P16 to P23).

    Table S2. Demographic data for melanoma patients and healthy donors.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. EPAC design and functionalization.
    • Fig. S2. EPAC-captured EV characterization.
    • Fig. S3. Western blot analysis of isolated EVs from SK-MEL-28 and MCF7 cells.
    • Fig. S4. Performance evaluation of the anti-CD63, anti-CD9, or anti-CD81 functionalized EPAC for detection of MCSP-positive EVs from SK-MEL-28 cells.
    • Fig. S5. Monitoring the changes of individual biomarker levels in EVs from drug-treated melanoma cell lines, using the anti-CD63 functionalized EPAC.
    • Fig. S6. Effect of cell debris on the anti-CD63 functionalized EPAC performance.
    • Fig. S7. Anti-MCSP functionalized EPAC specificity.
    • Fig. S8. The ErbB3 expression in EVs derived from melanoma patient (P1 to P10) and normal plasma (H1 to H5) samples, measured with a commercial ELISA kit.
    • Fig. S9. The anti-MCSP functionalized EPAC for tracking EV phenotypic changes of patients 18 to 23 during targeted therapies.
    • Table S1. The anti-MCSP functionalized EPAC for measurements of plasma EVs from 12 healthy donors (H1 to H12) and 8 melanoma patients (P16 to P23).
    • Table S2. Demographic data for melanoma patients and healthy donors.

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