Research ArticleIMMUNOLOGY

Detection of response to tumor microenvironment–targeted cellular immunotherapy using nano-radiomics

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Science Advances  10 Jul 2020:
Vol. 6, no. 28, eaba6156
DOI: 10.1126/sciadv.aba6156
  • Fig. 1 Elimination of intratumoral MDSCs does not correlate with tumor regression.

    (A) Treatment schema for experiments assessing MDSC dose-dependent tumor burden, vessel density, and suppressive environment. (B) Tumors were harvested en bloc, and percentage of MDSCs within tumors inoculated alone (No MDSCs) or with a 25 or 50% MDSC inoculation dose was determined by flow cytometry. (C) Tumors were harvested, sectioned, and analyzed (n = 5 samples per section) for presence of microvasculature by human CD31 immunostaining and of human MDSCs by S100A9 immunostaining on hematoxylin and eosin (H&E) of tissue sections. Shown is number of areas where MDSCs and CD31 vessels colocalize within tumors inoculated alone (No MDSCs) or with a 25 or 50% MDSC inoculation dose. (D) Levels of suppressive cytokines in serum of mice with tumors alone or inoculated with 25 or 50% MDSC dose. * indicates P < 0.05 vs. same cytokine in other groups. (E) Treatment schema for experiments assessing MDSC dose-dependent immunosuppression. (F) Neuroblastoma antigen GD2-specific CAR-T cells were injected into mice bearing tumor xenograft alone (No MDSCs) or mice bearing xenografts containing 25 or 50% MDSC dose, and tumor volume was followed over time. Control mice received non-CAR modified T cells (no Tx). (G) Treatment schema for experiments assessing effect of MDSC-targeting NKG2D.ζ-modified NK cells on (H) intratumoral MDSCs and (I) tumor volume. ns, not significant; sc, subcutaneously; iv, intravenously.

  • Fig. 2 Intratumoral MDSCs localize to areas of high CD31 vessel density and are eliminated effectively by NKG2D.ζ-modified NK cells.

    (A) Experimental schema for evaluating changes in MDSC burden after TME-directed NK cell therapy by flow cytometry, IHC, and nanoparticle contrast–enhanced CT imaging. (B) Intratumoral MDSC burden in tumor-only (T), tumor + MDSC (T + M), and tumor + MDSC + NK cell immunotherapy (T + M + Tx) groups was quantified per group by flow cytometry for CD14+/HLA-DRneg/intracellular S100A9+ cells. (C) Tumors were harvested, sectioned, and analyzed (n = 5 samples per section) for presence of microvasculature by human CD31 immunostaining (brownish red) and of human MDSCs by S100A9 immunostaining (black) on H&E of tissue sections. Shown are two representative sections of tumors inoculated without (T) or with MDSCs (T + M) and tumors with MDSCs after NK cell immunotherapy (T + M + Tx). (D) Number of S100A9+ MDSCs within areas of each tumor section containing CD31+ vessels (CD31 positive) were enumerated and compared to MDSC numbers in areas devoid of CD31+ vessels (CD31 negative). (E) MVD analysis demonstrates a reduction in tumor vascularity after depletion of MDSCs in the NK cell therapy group. Data are presented as means ± SEM (n = 9 to 10 animals per group).

  • Fig. 3 Nanoparticle contrast–enhanced CT imaging.

    (A) Representative thick slab coronal images of mouse lower abdomen showing tumor signal enhancement on delayed CT image (top row) and CT angio image (bottom row) for an animal in the tumor-only control group (T, left column), tumor + MDSC untreated group (T + M, middle column), and tumor + MDSC + NK cell immunotherapy group (T + M + Tx, right column). Delayed CT images were acquired 4 days after administration of nanoparticle contrast agent. Immediately thereafter, a second dose of nanoparticle contrast agent was injected to acquire CT angiography within an hour after contrast administration. (B) CT-derived tumor volume, (C) CT-derived mean tumor attenuation [Hounsfield units (HU)], from delayed CT scan due to accumulation of nanoparticle contrast agent, an indicator of tumor vascular permeability and tumor leakiness, and (D) CT-derived mean tumor fractional blood volume, an indicator of tumor vascularity, in tumors alone (T), tumor inoculated with human MDSCs (T + M), and tumors with MDSCs after NK cell therapy (T + M + Tx). CT-derived global tumor metrics did not show significant group differences (P > 0.05). Data are presented as means ± SD (n = 9 to 10 animals per group).

  • Fig. 4 Radiomic feature maps.

    Correlation matrix maps of radiomic features generated from analysis of nanoparticle contrast–enhanced delayed CT and CT angio and noncontrast MRI. Maps are presented for the tumor + MDSC untreated group (T + M, top row) and tumor + MDSC + NK cell therapy group (T + M + Tx, bottom row). GLCM, gray level co-occurrence matrix; GLDM, gray level dependence matrix; NGTDM, neighboring gray tone difference matrix.

  • Fig. 5 Radiomic features differentiate immunotherapy-treated tumors and untreated tumor.

    Examples of radiomic features differentiating the untreated group (T + M) from the immunotherapy group (T + M + Tx) for (A) delayed CT images analyzed using GLRLM run length nonuniformity RF, (B) CT angio images analyzed using GLSZM gray level nonuniformity RF, and (C) noncontrast T2w-MR images analyzed using GLRLM run length nonuniformity RF.

  • Table 1 Radiomics-based differentiation of immunotherapy-treated tumors and untreated tumors.

    Number of radiomic features under each class that differentiated (P < 0.05) the immunotherapy group (T + M + Tx) and untreated group (T + M). Radiomic analysis was performed on nanoparticle contrast–enhanced delayed CT, CT angio, and noncontrast T2-weighted MR images.

    Radiomic
    feature class
    CT delayedCT angioT2w MRI
    First-order
    statistics
    021
    Gray level
    co-occurrence
    matrix (GLCM)
    001
    Gray level
    dependence
    matrix (GLDM)
    221
    Gray level run
    length matrix
    (GLRLM)
    221
    Gray level size
    zone matrix
    (GLSZM)
    561
    Neighboring
    gray tone
    difference
    matrix
    (NGTDM)
    110

Supplementary Materials

  • Supplementary Materials

    Detection of response to tumor microenvironment–targeted cellular immunotherapy using nano-radiomics

    Laxman Devkota, Zbigniew Starosolski, Charlotte H. Rivas, Igor Stupin, Ananth Annapragada, Ketan B. Ghaghada, Robin Parihar

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