Nanovaccine’s rapid induction of anti-tumor immunity significantly improves malignant cancer immunotherapy
Source abstract
Abstract
The advantage of rapid tumor growth over slow immune induction often limits the efficiency of cancer therapeutic vaccines. Many functionalized micro-/nano-vaccines are shown to enhance cancer immunotherapy, but few studies correlate the therapeutic efficacy of these micro-/nano-vaccines to the vaccination route such as intravenous (IV), subcutaneous (SC) injection or their combination. Herein, we employed classical “priming + boosting” vaccination strategy to investigate the influence of four vaccination combinations (IV + IV, IV + SC, SC + IV and SC + SC) of nanovaccines on the anti-tumor therapeutic efficacy in mice bearing E.G7-OVA-lymphoma and B16F10-melanoma. The nanovaccines were constructed by loading antigen and CpG onto layered double hydroxide (LDH) nanoparticles. Our experimental data indicate that “IV-priming + SC-boosting” vaccination combination most efficiently inhibits the growth of early stage tumors, with the tumor volume reduced by >75-90 % in comparison with the control group. Based on these findings, a novel vaccination strategy, i.e. simultaneous IV and SC injection was proposed, which significantly delayed the progression of both early stage and advanced B16F10 tumors. Our current research for the first time correlates the vaccination route of nanovaccines to the anti-tumor therapeutic efficacy, and the highest efficacy of optimal vaccination combination (IV + SC) is benefited from that fact that IV priming quickly induces strong anti-tumor responses that are well sustained by subsequent SC boosting. Moreover, the simultaneous IV/SC vaccination potentially provides a novel vaccination strategy for enhanced immunotherapy of early stage and advanced tumors.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Immunotherapy and Immune Responses · RNA Interference and Gene Delivery · Cancer Immunotherapy and Biomarkers
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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