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Screening for lipid nanoparticles that modulate the immune activity of helper T cells towards enhanced antitumour activity

Nature Biomedical Engineering (2023)Cite this article

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Abstract

Lipid nanoparticles (LNPs) can be designed to potentiate cancer immunotherapy by promoting their uptake by antigen-presenting cells, stimulating the maturation of these cells and modulating the activity of adjuvants. Here we report an LNP-screening method for the optimization of the type of helper lipid and of lipid-component ratios to enhance the delivery of tumour-antigen-encoding mRNA to dendritic cells and their immune-activation profile towards enhanced antitumour activity. The method involves screening for LNPs that enhance the maturation of bone-marrow-derived dendritic cells and antigen presentation in vitro, followed by assessing immune activation and tumour-growth suppression in a mouse model of melanoma after subcutaneous or intramuscular delivery of the LNPs. We found that the most potent antitumour activity, especially when combined with immune checkpoint inhibitors, resulted from a coordinated attack by T cells and NK cells, triggered by LNPs that elicited strong immune activity in both type-1 and type-2 T helper cells. Our findings highlight the importance of optimizing the LNP composition of mRNA-based cancer vaccines to tailor antigen-specific immune-activation profiles.

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Fig. 1: In vitro screening of mRNA lipid nanoparticles for the transfection and induction of antigen presentation and maturation in DCs.
Fig. 2: In vivo assessments of lymph-node-cell transfection and immune activation by the top 3 LNP formulations.
Fig. 3: Antitumour efficacy of top mRNA LNP formulations as prophylactic vaccines.
Fig. 4: Antitumour efficacy of the top mRNA LNP formulations as therapeutic vaccines.
Fig. 5: A coordinated attack by T cells and NK cells was responsible for long-term protection.
Fig. 6: Local transfection, cellular uptake and endosomal escape of mRNA LNPs.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are available for research purposes from the corresponding authors on reasonable request. Source data for the figures are provided with this paper.

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Acknowledgements

S.C.M. and H.-Q.M. disclose support for the research described in this study from the National Institutes of Health (U01AI155313). J.P.S., J.J.G. and H.-Q.M. also disclose support for the publication of this study from the National Institutes of Health (P41EB028239).

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Yining Zhu, Ruochen Shen, Jinghan Lin, Jessica L. Stelzel, Leonardo Cheng, Ivan Vuong, Christine Wei, Natalie K. Livingston, Gregory P. Howard, Yizong Hu, Stephany Y. Tzeng, Jordan J. Green, Joshua C. Doloff, Sashank K. Reddy & Hai-Quan Mao

  2. Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA

    Yining Zhu, Jingyao Ma, Ruochen Shen, Jinghan Lin, Shuyi Li, Xiaoya Lu, Jessica L. Stelzel, Jiayuan Kong, Leonardo Cheng, Ivan Vuong, Zhi-Cheng Yao, Christine Wei, Nicole M. Korinetz, Wu Han Toh, Joseph Choy, Won June Cho, Natalie K. Livingston, Gregory P. Howard, Yizong Hu, Stephany Y. Tzeng, Jordan J. Green, Joshua C. Doloff, Jonathan P. Schneck, Sashank K. Reddy & Hai-Quan Mao

  3. Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Yining Zhu, Jingyao Ma, Ruochen Shen, Jinghan Lin, Shuyi Li, Xiaoya Lu, Jessica L. Stelzel, Jiayuan Kong, Leonardo Cheng, Ivan Vuong, Zhi-Cheng Yao, Christine Wei, Nicole M. Korinetz, Wu Han Toh, Joseph Choy, Natalie K. Livingston, Gregory P. Howard, Yizong Hu, Stephany Y. Tzeng, Jordan J. Green, Joshua C. Doloff & Hai-Quan Mao

  4. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA

    Jingyao Ma, Nicole M. Korinetz, Won June Cho & Jordan J. Green

  5. Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA

    Jingyao Ma, Xiaoya Lu, Jiayuan Kong, Zhi-Cheng Yao, Joseph Choy, Jordan J. Green, Joshua C. Doloff & Hai-Quan Mao

  6. Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Shuyi Li, Natalie K. Livingston & Jonathan P. Schneck

  7. Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Shuyi Li, Natalie K. Livingston & Jonathan P. Schneck

  8. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

    Wu Han Toh

  9. Department of Biology, Johns Hopkins University, Baltimore, MD, USA

    Wu Han Toh

  10. Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA

    Rebekah A. Reynolds, Melanie J. Shears & Sean C. Murphy

  11. Center for Emerging and Re-emerging Infectious Diseases, University of Washington, Seattle, WA, USA

    Rebekah A. Reynolds, Melanie J. Shears & Sean C. Murphy

  12. Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Donald J. Zack & Jordan J. Green

  13. Department of Oncology, Sidney Kimmel Comprehensive Cancer Center and the Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Jordan J. Green, Lei Zheng & Joshua C. Doloff

  14. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Jonathan P. Schneck

  15. Department of Plastic and Reconstructive Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Sashank K. Reddy

  16. Department of Microbiology, University of Washington, Seattle, WA, USA

    Sean C. Murphy

Authors
  1. Yining Zhu
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  2. Jingyao Ma
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  3. Ruochen Shen
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  5. Shuyi Li
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  6. Xiaoya Lu
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  7. Jessica L. Stelzel
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  8. Jiayuan Kong
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  9. Leonardo Cheng
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  10. Ivan Vuong
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  12. Christine Wei
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  14. Wu Han Toh
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  15. Joseph Choy
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  16. Rebekah A. Reynolds
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  17. Melanie J. Shears
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  18. Won June Cho
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  19. Natalie K. Livingston
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  20. Gregory P. Howard
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  21. Yizong Hu
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  22. Stephany Y. Tzeng
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  23. Donald J. Zack
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  24. Jordan J. Green
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  25. Lei Zheng
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  26. Joshua C. Doloff
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  27. Jonathan P. Schneck
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  28. Sashank K. Reddy
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  29. Sean C. Murphy
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  30. Hai-Quan Mao
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Contributions

Y.Z. and H.-Q.M. conceived and designed the study. H.-Q.M. and S.C.M. secured the funding for the study. Y.Z., J.M., R.S., Z.-C.Y., I.V., J.L., X.L., L.C., W.H.T., N.M.K., C.W., W.J.C. and J.K. performed the experiments. Y.Z., J.M., R.S., J.L.S., I.V., Y.H., W.J.C., R.A.R., M.J.S., N.K.L., S.L., G.P.H., S.K.R., S.Y.T., D.J.Z., J.J.G., L.Z., J.C., J.P.S., S.C.M. and H.-Q.M. participated in data analysis and interpretation. The manuscript was written by Y.Z. and H.-Q.M., with revisions made by S.C.M., C.W., R.S., I.V., S.Y.T., W.J.C., J.C.D., L.Z., J.P.S., R.A.R., S.K.R. and J.J.G., and with input from all the other authors.

Corresponding authors

Correspondence to Sean C. Murphy or Hai-Quan Mao.

Ethics declarations

Competing interests

H.-Q.M., Y.Z., J.M, R.S, L.C., I.V. and S.K.R. are co-inventors of a pending patent application (PCT/US2023/016938, filed in March 2023) covering the LNP formulation described in this paper. The patent was filed through Johns Hopkins Technology Ventures and is managed by it. The other authors declare no competing interests.

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Nature Biomedical Engineering thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Secretion levels of cytokines within the supernatant of BMDCs after 24 h of incubation.

Secretion levels of IL-6 (a), TNF-α (b) and IFN-γ (c), within the supernatant of BMDCs after 24 h incubation with the three mOVA-loaded LNPs were measured by ELISA. Data are represented as the mean ± s.e.m. Data were analyzed using one-way ANOVA and Dunnett’s multiple comparisons test. *P < 0.05, **P < 0.01, ***P < 0.001; NS, not significant; BMDC, bone marrow derived dendritic cell; ELISA, enzyme-linked immunoassay.

Source data

Extended Data Fig. 2 IL-6 and TNF-α secretion levels from antigen-stimulated splenocytes.

Splenocytes were isolated from vaccinated mice and restimulated in vitro with OVA and SIINFEKL peptide (100 μg ml−1 OVA and 2 μg ml−1 SIINFEKL) for 72 h. Secretion levels of IL-6 (a) and TNF-α (b) within the supernatant of were measured by ELISA. ‘Algel+OVA’ stands for Alhydrogel®+OVA group. Data represent the mean ± s.e.m. from a representative experiment (n = 4 biologically independent samples) of two independent experiments. Data were analyzed using one-way ANOVA and Dunnett’s multiple comparisons test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; NS, not significant; BMDC, bone marrow derived dendritic cell; ELISA, enzyme-linked immunoassay.

Source data

Extended Data Fig. 3 Titres of OVA-specific IgG subclass antibody in blood-serum samples collected on day 21 following immunization.

IgG1 (a) and IgG2a (b) antibodies in blood serum on day 21 were determined by ELISA. ‘Algel+OVA’ stands for Alhydrogel®+OVA group. Data represent the mean ± s.e.m. from a representative experiment (n = 4 biologically independent samples) of two independent experiments. Data were analyzed using one-way ANOVA and Dunnett’s multiple comparisons test. ****P < 0.0001; ELISA, enzyme-linked immunoassay.

Source data

Extended Data Fig. 4 Cytokine levels in the local injection site measured at 4 h or 24 h post-administration of the top-performing LNPs.

ELISA was employed to quantify the cytokine levels, including IFN-γ (a), TNF-α (b), and IL-4 (c), at the local injection site using OVA-encoding mRNA after the administration of three formulations (C10, D6, and F5) at 4 h and 24 h. The local injection sites were collected, homogenized, and subjected to tissue lysis to extract the proteins. The resulting lysate was centrifuged to separate the insoluble cellular debris. The protein concentration in each sample was determined using the BCA assay and normalized accordingly. Data represent the mean ± s.e.m. with n = 4 biologically independent samples. Data were analyzed using one-way ANOVA and Tukey’s multiple comparisons test. *P < 0.05; ***P < 0.001; NS, not significant.

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Zhu, Y., Ma, J., Shen, R. et al. Screening for lipid nanoparticles that modulate the immune activity of helper T cells towards enhanced antitumour activity. Nat. Biomed. Eng (2023). https://doi.org/10.1038/s41551-023-01131-0

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