Reversing cancer immunoediting phases with a tumor-activated and optically reinforced immunoscaffold

Abstract

In situ vaccine (ISV) is a promising immunotherapeutic tactic due to its complete tumoral antigenic repertoire. However, its efficiency is limited by extrinsic inevitable immunosuppression and intrinsic immunogenicity scarcity. To break this plight, a tumor-activated and optically reinforced immunoscaffold (TURN) is exploited to trigger cancer immunoediting phases regression, thus levering potent systemic antitumor immune responses. Upon response to tumoral reactive oxygen species, TURN will first release RGX-104 to attenuate excessive immunosuppressive cells and cytokines, and thus immunosuppression falls and immunogenicity rises. Subsequently, intermittent laser irradiation-activated photothermal agents (PL) trigger abundant tumor antigens exposure, which causes immunogenicity springs and preliminary infiltration of T cells. Finally, CD137 agonists from TURN further promotes the proliferation, function, and survival of T cells for durable antitumor effects. Therefore, cancer immunoediting phases reverse and systemic antitumor immune responses occur. TURN achieves over 90 % tumor growth inhibition in both primary and secondary tumor lesions, induces potent systemic immune responses, and triggers superior long-term immune memory in vivo. Taken together, TURN provides a prospective sight for ISV from the perspective of immunoediting phases.

Keywords: Hydrogel; Immunoediting phases; Immunoscaffold; In situ vaccine; Photothermal therapy.

Graphical abstract

Fig. 1

TURN triggered local cancer immunoediting phases regression and levered a potent systemic antitumor immune response. A) Chemical structures and synthetic routes of the hydrogel scaffold matrix, OHA and γPGA-S-ADH. B) Compositions and preparation of TURN. C) The concrete working principle of TURN to reverse cancer immunoediting phases. D) The cancer immunoediting phases progress with or without TURN treatment. Source materials of scheme illustration come from the website of app.Biorender.com.

Fig. 2

Characterization of the long-term photothermal agent repository. Size distribution and TEM images of A) PDA NPS and B) PL NPS. Scale bars, 200 nm. C) Size stability test of PL NPS within one week. D) DSC heat curves of LA, SA, and LSC. E) Thermal images and F) Temperature change curves of PL NPS at various concentrations (0, 25, 50, 100, 200, 400 μg/mL) under 808 nm laser (2 W/cm²) for 10 min. G) Temperature change curves of PDA and PL NPS at 100 μg/mL under 808 nm laser (2 W/cm²) for 10 min. H) On-off cycles of PL NPS. I) Temperature change ratios of PDA and PL NPS after being treated with hydrogen dioxide (5 μM) at different times. J) SEM image of PL-loaded hydrogel. K) Rheological properties of PL-loaded hydrogel. L) Photothermal effect of NS, G-PDA, and GP in vivo. (Data are presented as mean ± SD, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

Fig. 3

TURN promoted DCs maturation and inhibited MDSCs abundance. A) Representative flow cytometry charts and B) statistical analysis of apoptosis cells in different groups. C) The flow cytometric analysis of MDSCs after incubation with RGX-104 at different concentrations and their D) statistical analysis. E) Scheme illustration of DC maturation and MDSC induction in vitro. F) Flow cytometry plots and G) statistical analysis of mature DCs with different treatments. The groups were I) free medium, II) Gel, III) GP, IV) GP + L, V) GPCR, and VI) TURN. H) MDSCs abundance detected by flow cytometry and I) its quantitative analysis. (Data are presented as mean ± SD, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

Fig. 4

TURN inhibited local tumor progress and reversed cancer immunoediting phases. A) Scheme illustration of the subcutaneous 4T1 tumor models (n = 5). Mice were treated with I) NS, II) Gel, III) GP, IV) GP + L, V) GPCR, and VI) TURN. B) Tumor volume curves of different groups. C) Maximal fold changes of tumor volume before and after administration. D) Survival curves of tumor-bearing mice after different treatments. E) Scheme illustration of TURN-induced cancer immunoediting phases regression (n = 3). F) Flow cytometry charts and G) statistical analysis of DCs within TME. H) DCs maturation within TME. I) Infiltration and J) activation of CD3⁺ T cells in TME. K) The percentage and L) The flow cytometric plots of CD8⁺ T cells gated on CD3⁺ T cells. M) MDSCs detected by flow cytometry. Quantitative analysis of N) NK cells, O) MDSCs, and P) TAMs in tumor. Q) The ratio of M1 to M2. R) Quantitative analysis of Tregs in TME. S) Tumoral cytokines (IFN-γ, IL-10, and TGF-β) heatmap. T) Scheme illustration of T cells depleted, subcutaneous 4T1 tumor model (n = 5). U) The tumor volume and V) survival curves of mice with different treatments. (Data are presented as mean ± SD, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

Fig. 5

TURN levered a potent systemic immune response in vivo. A) Scheme illustration of TURN levered a systemic immune response (n = 5). Mice were treated with I) NS, II) Gel, III) GP, IV) GP + L, V) GPCR, and VI) TURN. Some immune cells in lymph nodes were shown from B) to D). B) Flow cytometry charts and C) statistical analysis of cDC1. D) Quantitative analysis of T cells. Some immune cells in spleen were shown from E) to N). Flow cytometric plots of E) CD4⁺ and F) CD8⁺ T cells. G) Quantitative analysis of T cells in the spleen. H) CD69⁺CD4⁺ T cells. I) IFN-γ⁺ and Granzyme B⁺CD4⁺ T cells. J) CD69⁺CD8⁺ T cells. K) IFN-γ⁺ and Granzyme B⁺CD8⁺ T cells. L) Representative flow cytometry charts and M) statistical analysis of MDSCs. N) Quantitative analysis of NK cells. O) The heatmap of cytokines (IFN-γ, IL-6) in serum. P) Scheme illustration of TURN launched a long-term immune memory (n = 3). T_CM and T_EM gated on Q) CD8⁺ T cells and R) CD3⁺ T cells in the spleen. (Data are presented as mean ± SD, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

Fig. 6

TURN in the primary tumor triggered regression of the secondary tumor. A) Scheme illustration of the subcutaneous 4T1 bilateral tumor models. Mice were treated with I) NS, II) Gel, III) GP, IV) GP + L, V) GPCR, and VI) TURN (n = 5). Tumor volume curves of B) primary tumors and C) Secondary tumors. D) Body weights of mice received different treatments. Tumor weights of E) primary tumors and F) Secondary tumors. G) Individual tumor volume curves of secondary tumors in different groups. H) Scheme illustration of tumor-infiltrating T cells analysis within the secondary tumor. Representative flow cytometry charts of I) CD4⁺ and J) CD8⁺ T cells in TME of the secondary tumor. K) Statistical analysis of tumor-infiltrating T cells within the secondary tumor. L) Scheme illustration of the establishment of T cells depleted, bilateral 4T1 tumor models (n = 5). Mice were treated by NS, TURN, αCD4+TURN, and αCD8+TURN. M) Tumor volume curves of the secondary tumor in different groups. N) Flow cytometric plots of CD4⁺ and CD8⁺ T cells gated on CD3⁺ T cells. O) Quantitative analysis of splenic T cells gated on lymphocytes with different treatments. (Data are presented as mean ± SD, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

Fig. 7

TURN exhibited the local and systemic anti-tumor effect in CT26 tumor models. A) Scheme illustration of the subcutaneous CT26 tumor models (n = 5). Mice were treated with I) NS, II) Gel, III) GP, IV) GP + L, V) GPCR, and VI) TURN. B) Tumor volume curves of different groups. C) Body weights and D) tumor weights of mice received different treatments. E) Individual tumor volume curves of different groups. F) Scheme illustration of the subcutaneous CT26 bilateral tumor models (n = 5). Mice were treated with I) NS, II) Gel, III) GP, IV) GP + L, V) GPCR, and VI) TURN. Tumor volume curves of G) primary tumors and H) secondary tumors with different treatments. I) Tumor weights and J) Individual tumor volume curves of the secondary tumors. (Data are presented as mean ± SD, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).