Rova-T enhances the anti-tumor activity of anti-PD1 in a murine model of small cell lung cancer with endogenous Dll3 expression

Rova-T 增强了具有内源性 Dll3 表达的小细胞肺癌小鼠模型中抗 PD1 的抗肿瘤活性

阅读:10
作者:Philip Vitorino, Chen-Hua Chuang, Alexandre Iannello, Xi Zhao, Wade Anderson, Ronald Ferrando, Zhaomei Zhang, Shravanthi Madhavan, Holger Karsunky, Laura R Saunders

Abstract

Rovalpituzumab tesirine (Rova-T) offers a targeted therapy for ~85% of SCLC patients whose tumors express DLL3, but clinical dosing is limited due to off-target toxicities. We hypothesized that a sub-efficacious dose of Rova-T combined with anti-PD1, which alone shows a clinical benefit to ~15% of SCLC patients, might elicit a novel mechanism of action and extend clinical utility. Using a pre-clinical murine SCLC tumor model that expresses Dll3 and has an intact murine immune system, we found that sub-efficacious doses of Rova-T with anti-PD1 resulted in enhanced anti-tumor activity, compared to either monotherapy. Multiplex immunohistochemistry (IHC) showed CD4 and CD8 T-cells primarily in normal tissue immediately adjacent to the tumor. Combination treatment, but not anti-PD1 alone, increased Ki67+/CD8 T-cells and Granzyme B+/CD8 in tumors by flow cytometry and IHC. Antibody depletion of T-cell populations showed CD8+ T-cells are required for in vivo anti-tumor efficacy. Whole transcriptome analysis as well as flow cytometry and IHC showed that Rova-T activates dendritic cells and increases Ccl5, Il-12, and Icam more than anti-PD1 alone. Increased tumor expression of PDL1 and MHC1 following Rova-T treatment also supports combination with anti-PD1. Mice previously treated with Rova-T + anti-PD1 withstood tumor re-challenge, demonstrating sustained anti-tumor immunity. Collectively our pre-clinical data support clinical combination of sub-efficacious Rova-T with anti-PD1 to extend the benefit of immune checkpoint inhibitors to more SCLC patients.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。