Research ArticleBioengineering

Predicting therapeutic nanomedicine efficacy using a companion magnetic resonance imaging nanoparticle

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Science Translational Medicine  18 Nov 2015:
Vol. 7, Issue 314, pp. 314ra183
DOI: 10.1126/scitranslmed.aac6522
  • Fig. 1. High-resolution intravital imaging of ferumoxytol and polymeric NPs show similar intratumoral behavior.

    (A) Fluorescently labeled ferumoxytol (MNP) and PLGA-PEG (TNP) were co-injected intravenously into mice for real-time imaging. (B) Time-course measurement of intratumoral MNP and TNP distribution within a live xenograft mouse model of fibrosarcoma transgenically expresses membrane-localized red fluorescent protein/mApple (HT1080-membrane-mApple). Scale bar, 50 μm. (C) PK and tumor tissue uptake were quantified for MNPs and TNPs, normalized to concentration (Ct) as a fraction of initial vascular concentration (C0). Data are means (thick lines) ± SD (shading; n ≥ 7 tumor areas across n = 3 animals). (D) In the same tumor model as in (B) and (C), contrast-enhanced images show perivascular host cells (green) 10 min after NP injection, distinguishable by cellular morphology, perivascular location, lack of tumor-specific mApple, and MNP accumulation but lack of TNP uptake at early time points. Scale bar, 50 μm. (E) Zoomed-in MNP/TNP distribution within a perivascular host cell (arrows). Note the accumulation kinetics within minutes. Scale bar, 14 μm. (F) Perivascular host cells take up MNP more rapidly than TNP. Data are means (thick lines) ± SD (shading; n = 3 tumors; n > 50 cells).

  • Fig. 2. Multiscale spatial colocalization between MNP and TNP.

    (A and B) NP tumor uptake in a live xenograft model, 24 hours after injection with MNP. For low (×4) and high (×40) magnification, scale bar denotes 500 and 50 μm, respectively. Intravenous co-injection with either TNP (A) or a free, unencapsulated fluorescent derivative of docetaxel (B) was imaged after vascular clearance. (C) MNP and TNP colocalization improves at lower spatial resolution, but MNP and docetaxel colocalization does not. Microscopy images (A and B) were computationally down-sampled to reduce spatial resolution, and pixel-by-pixel Pearson’s correlations (ρ) between MNP/TNP and MNP/docetaxel intensities were calculated across a range of pixel resolutions. Data are means (thick lines) ± SE (n = 3 animals and >400 images).

  • Fig. 3. MNP and TNP colocalize to tumor-associated macrophages in a syngeneic cancer model.

    (A and B) Flow cytometric analysis of intratumoral cellular composition (A) and single-cell NP distribution (B) in KP subcutaneous xenografts cotreated with TNP and MNP for 24 hours. Cellular NP uptake was quantified by fluorescence intensity after subtracting the autofluorescence of each population and normalized to the highest average NP uptake (macrophage). (C) Cumulative NP uptake across total cell populations within the bulk tumor mass, normalized such that NP uptake across all cell populations, weighted by their relative frequency, sums to 1. Data are means ± SEM (n = 12). (D and E) KP xenografts were excised 24 hours after MNP and TNP cotreatment, stained with hematoxylin and F4/80 (brown), and imaged at ×10 (D) and ×40 (E) magnification. Scale bars, 100 μm (D); 50 μm (E). (F) Adjacent tumor sections were immunostained for EpCAM to label tumor cells and for F4/80 to label macrophages.

  • Fig. 4. Quantitative finite-element analysis describes single-cell reaction-diffusion processes of the EPR effect.

    (A) Overview of computational modeling and optimization. (i) In HT1080 xenografts, automated morphological criteria identify vessels (green/red masking), and early MNP accumulation (white) identifies macrophage (as in Fig. 1D), which were computationally segmented with manual optimization. (ii) The finite-element mesh was generated on the basis of image segmentation. (iii) Change in concentration over time of free NP (dC/dt) and bound NP (dB/dt), along with boundary conditions describing NP flux across vessel walls [D(dC/dr)], and vessel NP concentrations over time (CP) were integrated across the finite-element mesh. (iv) Parameters were iteratively optimized by fitting model results to time-lapse imaging data. PDE, partial differential equation. (B) Model-fitting validation (green and yellow bars) and spatial correlation between MNPs and TNPs, with and without nonlinear PK correction based on finite-element modeling (gray and black bars). Correlation data are means ± SEM (n > 200 regions; P value determined by permutation test). (C) Parametric sensitivity analysis showing modeling parameters that most sensitively influence total NP accumulation within the bulk tumor at 2 hours after injection. Bmax, maximum NP cellular uptake; kbind, NP uptake rate; P, vessel permeability. Data are medians ± interquartile range (IQR) (n = 5; *P = 0.01, pooled two-tailed t test). (D) Example images and corresponding modeling show heterogeneous MNP accumulation in tumor regions with few (n = 18; left) and many (n = 98; right) phagocytes. Scale bars, 50 μm. (E) Finite-element modeling predicted increased MNP accumulation in the high-TAM tumor region (D), measured as average MNP concentration in tumor tissue outside of vessels.

  • Fig. 5. MRI quantifies heterogeneous MNP accumulation and predicts initial TNP response.

    (A) Representative cross-sectional T2 images of HT1080 tumors accumulating low and high intratumoral MNP, with pseudocolor overlays indicating ΔT2 within the tumor region. (B) Heat map shows T2 mapping averaged over the entire area of each tumor, which stratified a subset of tumors as low, medium, or high MNP. (C) Experimental design for using MNP MRI to predict paclitaxel (PTX)–loaded TNP response in HT1080 xenografts. s.c., subcutaneous; i.v., intravenous. (D) Fluorescence-activated cell sorting (FACS) analysis shows drug response in tumors exhibiting either low, medium, or high MNP uptake, as determined by MRI. Tumors with high MNP showed greater populations with abnormally low DNA content (sub-G1 cells) and elevated heterogeneous levels of DNA damage response, determined by deviation (σ) in γH2A.X staining across the population. Data are medians ± IQR (two-tailed t test). a.u., arbitrary units. (E) Representative FACS data for DNA content (using a DNA-intercalating dye) and DNA damage response (using γH2A.X immunostaining) in low-MNP and high-MNP tumors. (F) Representative FACS data for DNA content (using a DNA-intercalating dye) and apoptosis (TUNEL staining) in tumor cells from a high-MNP tumor. Contour lines and colors (E and F) denote single-cell distribution density.

  • Fig. 6. MNP predicts longitudinal TNP response and accumulation of TNP payload.

    (A) Tumor progression in HT1080 tumors ranked according to low, medium, and high MNP as measured by MNP MRI. Data are means ± SEM (total n = 33). (B) In orthotopic 4T1 breast cancer tumors, MNP prediction of TNP-encapsulated docetaxel, as determined by fluorescence of excised tumors 1 day after MNP/TNP injection. (C) MNP prediction of unencapsulated solvent-docetaxel accumulation as determined by fluorometry, using the same tumor model as in (B). (D) EGFR expression–based prediction of TNP-encapsulated docetaxel accumulation, using the same tumor model as in (B). TNP-docetaxel was co-injected with fluorescent tumor-targeting (α-EGFR) antibody, which stratified tumors into low, medium, or high expression groups. In all graphs, data are medians ± IQR. P values were determined by two-tailed t tests. n.s., not significant.

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/7/314/314ra183/DC1

    Methods

    Fig. S1. Fluorescent NP synthesis and characterization.

    Fig. S2. Imaging single-cell kinetics of MNP distribution with no TNP/MNP fluorescence spectral bleed-through.

    Fig. S3. MNP matches TNP plasma kinetics and does not influence TNP uptake.

    Fig. S4. MNP and TNP uptake by perivascular host cells in ovarian cancer.

    Fig. S5. MNP uptake in tumor-associated Cx3cr1+ host cells.

    Fig. S6. Imaging cytometric analysis of single-cell NP distribution kinetics.

    Fig. S7. MNPs colocalize with liposomes in tumor-associated cells.

    Fig. S8. Characterization of paclitaxel-loaded TNP and fluorescent derivatives.

    Table S1. Optimized finite-element method modeling parameters and reference values.

    References (3338)

  • Supplementary Material for:

    Predicting therapeutic nanomedicine efficacy using a companion magnetic resonance imaging nanoparticle

    Miles A. Miller, Suresh Gadde, Christina Pfirschke, Camilla Engblom, Melissa M. Sprachman, Rainer H. Kohler, Katherine S. Yang, Ashley M. Laughney, Gregory Wojtkiewicz, Nazila Kamaly, Sushma Bhonagiri, Mikael J. Pittet, Omid C. Farokhzad,* Ralph Weissleder*

    *Corresponding author. E-mail: rweissleder{at}mgh.harvard.edu (R.W.); ofarokhzad{at}bwh.harvard.edu (O.C.F)

    Published 18 November 2015, Sci. Transl. Med. 7, 314ra183 (2015)
    DOI: 10.1126/scitranslmed.aac6522

    This PDF file includes:

    • Methods
    • Fig. S1. Fluorescent NP synthesis and characterization.
    • Fig. S2. Imaging single-cell kinetics of MNP distribution with no TNP/MNP fluorescence spectral bleed-through.
    • Fig. S3. MNP matches TNP plasma kinetics and does not influence TNP uptake.
    • Fig. S4. MNP and TNP uptake by perivascular host cells in ovarian cancer.
    • Fig. S5. MNP uptake in tumor-associated Cx3cr1+ host cells.
    • Fig. S6. Imaging cytometric analysis of single-cell NP distribution kinetics.
    • Fig. S7. MNPs colocalize with liposomes in tumor-associated cells.
    • Fig. S8. Characterization of paclitaxel-loaded TNP and fluorescent derivatives.
    • Table S1. Optimized finite-element method modeling parameters and reference values.
    • References (3338)

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