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Research ArticleBasic Science Investigation

Dynamic Tumor-Specific MHC-II Immuno-PET Predicts the Efficacy of Checkpoint Inhibitor Immunotherapy in Melanoma

Zhen Yang, Feng Li, Yuqian Huang, Na Yin, Junjun Chu, Ying Ma, Roderic I. Pettigrew, Dale J. Hamilton, Diego R. Martin and Zheng Li
Journal of Nuclear Medicine November 2022, 63 (11) 1708-1714; DOI: https://doi.org/10.2967/jnumed.121.263151
Zhen Yang
1Department of Radiology, Houston Methodist Academic Institute, Houston, Texas;
2Center for Bioenergetics, Houston Methodist Academic Institute, Houston, Texas;
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Feng Li
1Department of Radiology, Houston Methodist Academic Institute, Houston, Texas;
2Center for Bioenergetics, Houston Methodist Academic Institute, Houston, Texas;
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Yuqian Huang
2Center for Bioenergetics, Houston Methodist Academic Institute, Houston, Texas;
3Xiangya Hospital, Central South University, Hunan, China;
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Na Yin
4Department of Pediatrics, Baylor College of Medicine, Houston, Texas;
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Junjun Chu
5Department of Medicine, Kech School of Medicine, University of Southern California, Los Angeles, California;
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Ying Ma
6NIBIB, NIH, Bethesda, Maryland;
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Roderic I. Pettigrew
7Engineering Medicine, Texas A&M University, College Station, Texas;
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Dale J. Hamilton
2Center for Bioenergetics, Houston Methodist Academic Institute, Houston, Texas;
8Department of Medicine, Weil Cornell Medicine, New York, New York; and
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Diego R. Martin
1Department of Radiology, Houston Methodist Academic Institute, Houston, Texas;
9Department of Radiology, Weil Cornell Medicine, New York, New York
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Zheng Li
1Department of Radiology, Houston Methodist Academic Institute, Houston, Texas;
2Center for Bioenergetics, Houston Methodist Academic Institute, Houston, Texas;
9Department of Radiology, Weil Cornell Medicine, New York, New York
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  • FIGURE 1.
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    FIGURE 1.

    (A) Fluorescence-activated cell sorting analysis of tsMHC-II and PD-L1 population in B16F10 and B16SIY tumors and population changes when treated with anti-PD1 and IFNγ immunity stimulants. (B) Summary of fluorescence-activated cell sorting assay (n = 6 for each cohort). Statistical analysis for MHC-II: B16F10 control vs. B16SIY control (P = 0.0382), B16F10 PD1 vs. B16SIY PD1 (P = 0.0158), B16F10 IFNγ vs. B16SIY IFNγ (P = 0.0112), B16F10 control vs. B16F10 IFNγ (P = 0.0026), and B16SIY control vs. B16SIY IFNγ (P = 0.0001). Statistical analysis for PD-L1: B16F10 control vs. B16F10 IFNγ (P = 0.0001) and B16SIY control vs. B16SIY IFNγ (P = 0.0001). *P < 0.05. ##P < 0.01. ###P < 0.001. Ctrl = control.

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    FIGURE 2.

    Three-dimensionally rendered maximum-intensity projection of PET/CT images of control, anti-PD1–treated, and IFNγ-stimulated B16F10 and B16SIY tumor-bearing mice 48 h after injection of 64Cu-DOTA-MHCII. Circled areas are tumor site. Ctrl = control.

  • FIGURE 3.
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    FIGURE 3.

    (A) Region-of-interest quantification of tumor-accumulated 64Cu-DOTA-MHCII in PET images (n = 6 per group). Unpaired Student t test was performed to compare B16F10 control vs. B16SIY control (P = 0.0082), B16F10 PD1 vs. B16SIY PD1 (P = 0.0035), B16F10 IFNγ vs. B16SIY IFNγ (P = 0.0002), B16F10 control vs. B16F10 IFNγ (P = 0.034), and B16SIY control vs. B16SIY IFNγ (P = 0.0003). (B) Tumor uptake of 64Cu-DOTA-MHCII in biodistribution study at 48 h after injection. Unpaired Student t test was performed to compare B16F10 control vs. B16SIY control (P = 0.0002), B16F10 PD1 vs. B16SIY PD1 (P = 0.0003), B16F10 IFNγ vs. B16SIY IFNγ (P = 0.0002), B16F10 control vs. B16F10 IFNγ (P = 0.0044), and B16SIY control vs. B16SIY IFNγ (P = 0.0001). (C) Biodistribution study of 64Cu-DOTA-MHCII in B16F10 tumor model. (D) Biodistribution study of 64Cu-DOTA-MHCII in B16SIY tumor model (n = 6). *P < 0.05. **P < 0.01. ***P < 0.001. #P < 0.05. ##P < 0.01. ###P < 0.001. GI = gastrointestinal.

  • FIGURE 4.
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    FIGURE 4.

    Western blot of tsMHC-II expression (A) and dot density quantified by ImageJ (B) in control, anti-PD1–treated, and IFNγ-treated B16F10 and B16SIY tumors. Expression level was normalized by GAPDH across various groups. (C) Immunohistochemistry staining of MHC-II in tumors. Ctrl = control.

  • FIGURE 5.
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    FIGURE 5.

    (A) Immunofluorescent staining of CD45 and MHC-II in control, anti-PD1–treated, and IFNγ-treated B16F10 and B16SIY tumors. (B) Fluorescence intensity quantified by ImageJ. Fluorescence intensity was normalized by nucleus numbers that represent cell numbers, and ratio was plotted as natural logarithm to compare differences across groups. Eight different views were analyzed per sample to obtain statistics. Unpaired Student t test was performed to compare B16F10 control vs. B16SIY control (P = 0.0046), B16F10 PD1 vs. B16SIY PD1 (P = 0.0031), and B16F10 IFNγ vs. B16SIY IFNγ (P < 0.0001). Meanwhile, dynamic change was also compared: B16F10 control vs. B16F10 IFNγ (P = 0.0024) and B16SIY control vs. B16SIY IFNγ (P < 0.0001). **P < 0.01. ***P < 0.001. Ctrl = control.

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Journal of Nuclear Medicine: 63 (11)
Journal of Nuclear Medicine
Vol. 63, Issue 11
November 1, 2022
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Dynamic Tumor-Specific MHC-II Immuno-PET Predicts the Efficacy of Checkpoint Inhibitor Immunotherapy in Melanoma
Zhen Yang, Feng Li, Yuqian Huang, Na Yin, Junjun Chu, Ying Ma, Roderic I. Pettigrew, Dale J. Hamilton, Diego R. Martin, Zheng Li
Journal of Nuclear Medicine Nov 2022, 63 (11) 1708-1714; DOI: 10.2967/jnumed.121.263151

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Dynamic Tumor-Specific MHC-II Immuno-PET Predicts the Efficacy of Checkpoint Inhibitor Immunotherapy in Melanoma
Zhen Yang, Feng Li, Yuqian Huang, Na Yin, Junjun Chu, Ying Ma, Roderic I. Pettigrew, Dale J. Hamilton, Diego R. Martin, Zheng Li
Journal of Nuclear Medicine Nov 2022, 63 (11) 1708-1714; DOI: 10.2967/jnumed.121.263151
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Keywords

  • immuno-PET
  • MHC-II
  • anti-PD1 immunotherapy
  • melanoma
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