Engineering bispecific exosome activators of T cells to target immune checkpoint inhibitor-resistant metastatic melanoma

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Engineering bispecific exosome activators of T cells to target immune checkpoint inhibitor-resistant metastatic melanoma

Data availability

Sequences for all plasmids and primers are provided in Supplementary Information. The RNA-seq data for Fig. 3a were reanalyzed from the dataset deposited in GEO (GSE78220)66. Source data are provided with this paper.

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Acknowledgements

We thank B. A. Hanks from Duke University Medical Center for the invaluable gift of the BP melanoma cell line. Microscopy, flow cytometry and cell sorting were performed in the Herbert Irving Comprehensive Cancer Center at Columbia University, funded in part through the National Institutes of Health (NIH)-NIC Cancer Center Support Grant P30CA013696. This research was funded by NIH grants HL179818 (K.C.), HL170612 (K.C.), HL144002 (K.C.), HL146153 (K.C.) and HL154154 (K.C.) and American Heart Association grant 24CDA1277521 (D.Z.). K.C. also wishes to thank C. Kaganov and her late husband A. L. Kaganov for their generous support that helped make this work possible.

Author information

Author notes

  1. These authors contributed equally: Shuo Liu, Mengrui Liu, Zhenzhen Wang.

Authors and Affiliations

  1. Department of Biomedical Engineering, Columbia University, New York, NY, USA

    Shuo Liu, Mengrui Liu, Shiqi Hu, Kaiyue Zhang, Chao Lu, Xiao Cheng, Ming Shen, Dashuai Zhu & Ke Cheng

  2. Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA

    Shuo Liu, Mengrui Liu, Shiqi Hu, Kaiyue Zhang, Xiao Cheng, Ming Shen, Dashuai Zhu & Ke Cheng

  3. Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill/Raleigh, NC, USA

    Zhenzhen Wang

  4. Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, NC, USA

    Zhenzhen Wang & Jianing Bi

  5. Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA

    Ke Cheng

Authors

  1. Shuo Liu
  2. Mengrui Liu
  3. Zhenzhen Wang
  4. Shiqi Hu
  5. Kaiyue Zhang
  6. Chao Lu
  7. Xiao Cheng
  8. Ming Shen
  9. Jianing Bi
  10. Dashuai Zhu
  11. Ke Cheng

Contributions

K.C. and D.Z. supervised the project. K.C. and S.L. initiated the idea and conceived the study. S.L., M.L. and Z.W. designed and performed the in vitro and in vivo analyses. S.H. contributed to the in vivo studies and analyzed the RNA-seq results from the GEO database. K.Z., C.L., M.S. and J.B. contributed to the in vivo study. X.C. contributed to the data analysis. S.L. reviewed the statistical methods and revised the paper. S.L. wrote the paper, with input from all authors.

Corresponding authors

Correspondence to Dashuai Zhu or Ke Cheng.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Biotechnology thanks Samir El Andaloussi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Characterization of engineered exosomes.

a, Immunoblotting of exosome markers in engineered exosomes. b, NanoSight size distribution analysis of the engineered exosomes. c, Representative TEM image of the isolated engineered exosomes. Scale bar, 100nm. d, SPR analysis on the affinity between PD-L1 and engineered exosomes. e, Flow cytometry showing the percentage of rmPD1-Fc positive tumor cells post PD1-Alix-Exo treatment (n = 3 independent experiments). P values were determined by one-way ANOVA post-Tukey’s multiple comparisons test using GraphPad PRISM software. Exact P values are indicated. Results are presented as means ± s.d.

Source data

Extended Data Fig. 2 Single vesicle analysis of engineered exosomes.

a, Single exosome flow cytometry analysis of PD1 positive exosomes after engineering. b, Representative single exosomes in (a). c, Gating strategy for (a). d. Single exosome imaging for engineered PD1 exosomes generated by ONI Nanoimager.

Extended Data Fig. 3 Comparison of Inhaled PD1-Alix-Exo and Intravenously Administered Anti-PD-L1 Antibody.

a, Flow cytometry showing the percentage of anti-PD-L1 antibody positive tumor cells at 0 h, 0.5 h, 4 h, 24 h and 48 h post i.v. injection (n = 5 independent mice for each group). b, Quantification of Ex vivo fluorescent images of lungs after Cy7-labeled PD1-Alix-Exo inhalation and Cy7-labeled anti-PD-L1 antibody i.v. injection at 0 h, 0.5 h, 24 h, 48 h, 96 h, and 168 h. The normalized rate was determined by comparing the fold changes over pre-delivery. c, Quantification of Ex vivo fluorescent imaging of blood after Cy7-labeled PD1-Alix-Exo inhalation and Cy7-labeled anti-PD-L1 antibody i.v. injection at 0 h, 0.5 h, 24 h, 48 h, and 96 h. The normalized rate was determined by comparing the fold changes over pre-delivery. P values were determined by one-way ANOVA post-Tukey’s multiple comparisons test in (a) and by two-tailed unpaired Student’s t-test in (b and c). P values were determined using GraphPad PRISM software. Exact P values are indicated. Results are presented as means ± s.d.

Source data

Extended Data Fig. 4 Synergistic Targeting of BEAT.

a, Immunoblotting of immunoprecipitation assay. b, Western blot of tumor cell PD-L1 with the indicated treatment. c, Representative immunostaining images of BEAT treated tumor cells. Lysosomes were stained with lysotracker (red), BEAT were stained with DiD (gray), and nuclear were stained with Hoechst (blue). d, Schematic illustrating the assessment of antitumor efficacy following treatment. e, Statistical lung colonized tumor growth over time post BEAT and FZD8-Exo plus PD1-Exo treatment. The normalized tumor growth rate was calculated by comparing the fold-changes over the initial bioluminescence intensity from tumor cells. Experiments were conducted simultaneously with Extended Data Fig. 5f, sharing the same PBS and BEAT groups. n = 5 independent mice for each group. P values were determined by two-tailed unpaired Student’s t-test using GraphPad PRISM software. Exact P values are indicated. Results are presented as means ± s.d.

Source data

Extended Data Fig. 5 BEAT therapy exhibits dose-dependent tumor suppression and outperforms systemically delivered dual antibody.

a, Schematic illustrating the assessment of antitumor efficacy following treatment. b, Normalized luminescent intensity of tumors at the endpoint of the study. n = 5 independent mice for each group c, Dose reponse curve. n = 5 independent mice for each group. d, Confirmation of linked dual antibody. e, Schematic illustrating the assessment of antitumor efficacy following treatment. f, Statistical tumor growth over time post BEAT and dual antibody treatment. The normalized tumor growth rate was calculated by comparing the fold-changes over the initial bioluminescence intensity from tumor cells. Experiments were conducted simultaneously with Extended Data Fig. 4e, sharing the same PBS and BEAT groups. n = 5 independent mice for each group. P values were determined by two-tailed unpaired Student’s t-test using GraphPad PRISM software. Exact P values are indicated. Results are presented as means ± s.d.

Source data

Extended Data Fig. 6 BEAT inhibits the tumor growth of spontaneous lung metastasize.

a, Schematic illustrating the assessment of antitumor efficacy following treatment in a checkpoint inhibitor therapy–resistant tumor model established by subcutaneous injection of 1×10⁶ cells. b, Representative H&E staining of lungs with spontaneous metastasize after BEAT treatment. c, Statistical analysis of tumor nodules in (a). P values were determined by two-tailed unpaired Student’s t-test using GraphPad PRISM software. Exact P values are indicated. Results are presented as means ± s.d.

Source data

Extended Data Fig. 7 BEAT inhibits the melanoma liver metastasis.

a, Ex vivo imaging of tumor bearing mouse liver after 24h of Cy7-labeled BEAT intravenous injection. b, Quantification of the fluorescence density in ex vivo mouse livers (a). c, Schematic illustrating the assessment of antitumor efficacy following treatment. d, Statistical liver colonized tumor growth over time post BEAT treatment. The normalized tumor growth rate was calculated by comparing the fold-changes over the initial bioluminescence intensity from tumor cells. n = 5 independent mice for each group. P values were determined by two-tailed unpaired Student’s t-test using GraphPad PRISM software. Exact P values are indicated. Results are presented as means ± s.d.

Source data

Extended Data Fig. 8 Toxicity study of BEAT therapy in healthy mice.

a, Schematic illustrating the assessment of antitumor efficacy following treatment. b, Serum glucose levels in healthy mice after BEAT and antibody therapy. n=5 in each treatment group. c, Representative H&E staining of the pancreas after BEAT and antibody treatment. d, Representative immunostaining images of BEAT and antibody treated mouse pancreas. Insulin was stained with anti-insulin antibody (red), cleaved caspase3 was stained with anti-cleaved caspase 3 antibody (green) and and nuclear were stained with DAPI (blue). e, Serum AST levels in healthy mice after BEAT and antibody therapy. n=5 in each treatment group. f, Representative H&E staining of mouse major organs at the study endpoint. P values were determined by one-way ANOVA post-Tukey’s multiple comparisons test using GraphPad PRISM software. Exact P values are indicated. Results are presented as means ± s.d.

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Liu, S., Liu, M., Wang, Z. et al. Engineering bispecific exosome activators of T cells to target immune checkpoint inhibitor-resistant metastatic melanoma. Nat Biotechnol (2026). https://doi.org/10.1038/s41587-025-02890-8

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