Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, TN, United States.
Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, United States.
Front Immunol. 2022 Aug 19;13:936129. doi: 10.3389/fimmu.2022.936129. eCollection 2022.
With the clinical approval of T-cell-dependent immune checkpoint inhibitors for many cancers, therapeutic cancer vaccines have re-emerged as a promising immunotherapy. Cancer vaccines require the addition of immunostimulatory adjuvants to increase vaccine immunogenicity, and increasingly multiple adjuvants are used in combination to bolster further and shape cellular immunity to tumor antigens. However, rigorous quantification of adjuvants' synergistic interactions is challenging due to partial redundancy in costimulatory molecules and cytokine production, leading to the common assumption that combining both adjuvants at the maximum tolerated dose results in optimal efficacy. Herein, we examine this maximum dose assumption and find combinations of these doses are suboptimal. Instead, we optimized dendritic cell activation by extending the Multidimensional Synergy of Combinations (MuSyC) framework that measures the synergy of efficacy and potency between two vaccine adjuvants. Initially, we performed a preliminary screening of clinically translatable adjuvant receptor targets (TLR, STING, NLL, and RIG-I). We determined that STING agonist (CDN) plus TLR4 agonist (MPL-A) or TLR7/8 agonist (R848) as the best pairwise combinations for dendritic cell activation. In addition, we found that the combination of R848 and CDN is synergistically efficacious and potent in activating both murine and human antigen-presenting cells (APCs) . These two selected adjuvants were then used to estimate a MuSyC-dose optimized for T-cell priming using ovalbumin-based peptide vaccines. Finally, using B16 melanoma and MOC1 head and neck cancer models, MuSyC-dose-based adjuvating of cancer vaccines improved the antitumor response, increased tumor-infiltrating lymphocytes, and induced novel myeloid tumor infiltration changes. Further, the MuSyC-dose-based adjuvants approach did not cause additional weight changes or increased plasma cytokine levels compared to CDN alone. Collectively, our findings offer a proof of principle that our MuSyC-extended approach can be used to optimize cancer vaccine formulations for immunotherapy.
随着 T 细胞依赖性免疫检查点抑制剂在许多癌症中的临床批准，治疗性癌症疫苗作为一种有前途的免疫疗法重新出现。癌症疫苗需要添加免疫刺激性佐剂来提高疫苗的免疫原性，并且越来越多地使用多种佐剂联合使用，以进一步增强和塑造对肿瘤抗原的细胞免疫。然而，由于共刺激分子和细胞因子产生的部分冗余，严格量化佐剂的协同相互作用具有挑战性，导致人们普遍认为将两种佐剂以最大耐受剂量组合使用会产生最佳疗效。在这里，我们检查了这种最大剂量假设，并发现这些剂量的组合并不理想。相反，我们通过扩展多维组合协同作用（MuSyC）框架来优化树突状细胞的激活，该框架用于测量两种疫苗佐剂的功效和效力之间的协同作用。最初，我们对临床可转化的佐剂受体靶标（TLR、STING、NLL 和 RIG-I）进行了初步筛选。我们确定 STING 激动剂（CDN）加 TLR4 激动剂（MPL-A）或 TLR7/8 激动剂（R848）是树突状细胞激活的最佳配对组合。此外，我们发现 R848 和 CDN 的组合在激活小鼠和人抗原呈递细胞（APC）方面具有协同功效和效力。然后，我们使用基于卵清蛋白的肽疫苗来估计 MuSyC 剂量优化的 T 细胞启动。最后，使用 B16 黑色素瘤和 MOC1 头颈部癌症模型，基于 MuSyC 剂量的癌症疫苗佐剂增强了抗肿瘤反应，增加了肿瘤浸润淋巴细胞，并诱导了新的髓样肿瘤浸润变化。此外，与单独使用 CDN 相比，基于 MuSyC 剂量的佐剂方法不会引起体重变化或增加血浆细胞因子水平。总的来说，我们的研究结果提供了一个原理证明，即我们的 MuSyC 扩展方法可用于优化癌症疫苗制剂的免疫治疗。
