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. 2012 Dec;18(12):1835-40.
doi: 10.1038/nm.2994. Epub 2012 Nov 11.

Protein typing of circulating microvesicles allows real-time monitoring of glioblastoma therapy

Huilin Shao  1 , Jaehoon Chung, Leonora Balaj, Alain Charest, Darell D Bigner, Bob S Carter, Fred H Hochberg, Xandra O Breakefield, Ralph Weissleder, Hakho Lee
Affiliations

Protein typing of circulating microvesicles allows real-time monitoring of glioblastoma therapy

Huilin Shao et al. Nat Med. 2012 Dec.

Abstract

Glioblastomas shed large quantities of small, membrane-bound microvesicles into the circulation. Although these hold promise as potential biomarkers of therapeutic response, their identification and quantification remain challenging. Here, we describe a highly sensitive and rapid analytical technique for profiling circulating microvesicles directly from blood samples of patients with glioblastoma. Microvesicles, introduced onto a dedicated microfluidic chip, are labeled with target-specific magnetic nanoparticles and detected by a miniaturized nuclear magnetic resonance system. Compared with current methods, this integrated system has a much higher detection sensitivity and can differentiate glioblastoma multiforme (GBM) microvesicles from nontumor host cell-derived microvesicles. We also show that circulating GBM microvesicles can be used to analyze primary tumor mutations and as a predictive metric of treatment-induced changes. This platform could provide both an early indicator of drug efficacy and a potential molecular stratifier for human clinical trials.

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Figures

Figure 1
Figure 1. Human glioblastoma cells produce abundant microvesicles (MVs) which can be analyzed by micro nuclear magnetic resonance (μNMR)
(a) Scanning electron microscopy image of a primary human glioblastoma cell (GBM20/3) grown in culture, releasing abundant MVs. (b) High magnification image shows that many of the MVs on the cell surface assumed typical saucer-shaped characteristics of exosomes. (c) Transmission electron microscopy image of MVs (~ 80 nm) targeted with magnetic nanoparticles (MNPs) via CD63 antibody. The samples were purified by membrane filtration to collect small MVs. The MNPs appear as black dots (indicated by an arrow). (d) Labeling procedure for extravesicular markers. The two-step BOND-2 assay configuration uses bioorthogonal amplification chemistry to maximize MNP binding onto target proteins on MVs (not drawn to scale). (e) Microfluidic system for on-chip detection of circulating MVs. The system was designed to i) allow MNP-targeting of MVs, ii) concentrate MNP-tagged MVs while removing unbound MNPs, and iii) provide in-line μNMR detection.
Figure 2
Figure 2. μNMR assay for MV detection
(a) Correlation between μNMR measurements for CD63 and MV numbers in a dilution series. MV numbers were estimated by nanoparticle tracking analysis (NTA). The transverse relaxation (R2), as determined by μNMR, varied linearly with MV numbers (R2 > 98%). Importantly, the R2 changes (ΔR2CD63) were statistically identical across different cell lines (P > 0.16), which validates its use as a universal measure for quantitating MVs. Western blotting (Supplementary Fig. 4a) also revealed a consistent and higher expression of CD63 in the prepared MVs. (b) Using MVs from model cell lines, the expression levels of EGFR and EGFRvIII were measured by μNMR. The MV expression (ξ) of a target protein marker was obtained by normalizing a marker-associated R2 against R2CD63. μNMR measurements showed excellent agreement (R2 > 99%) with fluorescence ELISA. (c) Detection threshold of μNMR assay for MVs. With CD63-tagged MVs, the detection threshold of μNMR, as measured by the relative changes in the transverse relaxation time (T2 = 1/R2) with respect to controls, was approximately ~104 MVs. (d) Comparison of MV detection sensitivity. In a series of MV dilution assays, μNMR was considerably more sensitive than Western blotting (WB; Supplementary Fig. 4b), flow cytometry (FC), ELISA and NTA. All measurements were performed in triplicate, and the data is displayed as mean ± s.e.m.
Figure 3
Figure 3. Protein typing of glioblastoma multiforme (GBM)-derived MVs from cell lines and patient samples
(a) GBM markers (EGFR, EGFRvIII, PDGFR, PDPN, EphA2 and IDH1 R132H), positive MV control marker (HSP90) as well as host cell markers (CD41, MHCII) were profiled in both parental cells (left) and their corresponding MVs (right). A four GBM marker combination (EGFR, EGFRvIII, PDPN and IDH1 R132H) was able to distinguish GBM-derived MVs from host cell-derived MVs. HBMVEC, human brain microvascular endothelial cell; NHA, normal human astrocyte; buffy coat and plasma were isolated from whole blood donated by healthy volunteers. (b) Analysis of clinical patient samples. Waterfall plots show the expression levels of different biomarkers sorted from high (left) to low (right). Note the increased expression of EGFR and PDPN, as well as the unique expression of EGFRvIII and IDH1 R132H, in patient samples. (c) Receiver operating characteristic curves (left) were generated to compare the detection sensitivity, specificity and accuracy of each marker. Overall, the accuracy was < 76% for a single marker alone (right). When all markers were combined (QUAD), the detection accuracy considerably improved (> 90%). AUC: area under curve.
Figure 4
Figure 4. Effects of geldanamycin treatment on T103 GBM model
(a) On the cellular level, geldanamycin treatment did not alter the expression of CD63, but considerably reduced the amount of EGFR and EGFRvIII, as determined by flow cytometry and Western blotting. (b) Upon geldanamycin treatment, μNMR detection showed that MVs exhibit a similar decrease in EGFR and EGFRvIII profiles (ξ; normalized with respect to CD63 expression) as that observed in whole cells. (c, d) Total number of cells and MVs (c) decreased in a dose-dependent manner, upon drug treatment. However, the total EGFR and EGFRvIII levels in MVs (d) showed a steeper decline due to the combined effects of reduced MV number and decreased marker (EGFR, EGFRvIII) expression per MV. (e, f) To use MV readouts as an indicator for drug efficacy, a response index (RI) was defined, that recapitulates changes in both MV number and MV molecular expression. Compared to TMZ, the RI of geldanamycin was higher for both T103 (e) and GLI36vIII (f) cell lines due to the drug's ability to reduce both MV number as well as receptor expression. All changes with respect to untreated samples were statistically significant (P < 0.001). All analytical measurements were performed in triplicate, and the data is shown as mean ± s.e.m.
Figure 5
Figure 5. Analysis of circulating MVs in GBM mice and human patients undergoing treatment
(a) Circulating MVs in untreated tumor-bearing animals (n = 15). The tumor progression index (TPI) is used to reflect changes in both MV number and MV molecular expression. Note the close correlation between increasing tumor volumes and TPI values over time. (b) In TMZ treated mice (n = 15), TPI values from μNMR measurements revealed response to treatment before apparent changes in tumor size. (c) Since the decline rate of TPI represents a time-sensitive indicator of treatment efficacy, we define the drug efficacy index (ηMV) as the temporal change in TPI−1. With TMZ treatment, ηMV switched from negative (tumor progression) to positive (treatment response). (d, e) Clinical trial. Blood samples were collected from the same patients before and after TMZ/radiation treatment, and circulating MVs were profiled using μNMR. Both TPI (d) and ηMV (e) confirmed that longitudinal MV profiling can be used to predict treatment outcomes and differentiate between responders and non-responders. Dashed lines in (e) indicate the median values.
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