Abstract 3232: Combination therapy with onco-selective mRNA LNPs targets the complex immunosuppressive tumor microenvironment and is well tolerated at efficacious doses

التفاصيل البيبلوغرافية
العنوان: Abstract 3232: Combination therapy with onco-selective mRNA LNPs targets the complex immunosuppressive tumor microenvironment and is well tolerated at efficacious doses
المؤلفون: Manfred Kraus, Rudy Christmas, Tom A. Addison, Yulia Rybakova, Leona Lee, Jieni Xu, Mark Krimmer, Cafer Ozdemir, Burak Yilmaz, Yusuf Erkul
المصدر: Cancer Research. 83:3232-3232
بيانات النشر: American Association for Cancer Research (AACR), 2023.
سنة النشر: 2023
مصطلحات موضوعية: Cancer Research, Oncology
الوصف: Novel improved cancer immunotherapies are needed, since most cancer patients exhibit a treatment resistant tumor microenvironment to currently available immunotherapeutic modalities. Due to primary resistance or the development of acquired resistance to therapeutic approaches these patients unfortunately do not benefit from a long-lasting overall survival benefit. Combination treatments are a promising approach to induce a sustained anti-tumor immune response. However, dose limiting adverse events in healthy tissues and/or the complex expensive production requirements have limited the full therapeutic potential of combinatorial applications. mRNA-based combinatorial therapy options have evolved by recent advancements in the production, purification, and delivery of mRNA to cells. Today, mRNA therapies are a rapidly growing class of medications that can redefine how many diseases are treated. These therapies enable the production of biologics directly in the patient and mRNA LNPs are easy to manufacture at scale. Despite these key advantages, mRNA therapeutics are yet to show their true potential in oncology. Dose limiting toxicities of mRNA immunotherapies are driven, in part, by systemic payload (encoded protein) toxicity, which can be avoided by engineering the mRNA to improve its onco-selectivity and thereby reduce systemic target-mediated adverse events. Kernal Biologics develops novel onco-selective mRNA therapies directed to breach the immunosuppressive tumor microenvironment. Based on our proprietary machine learning-enabled computational pipeline, we designed our next generation mRNA therapeutics. These mRNAs have the potential to increase the depth and breadth of anti-PD1/PD-L1 treatment plus enable responses in patients that are currently non-responders or refractory to the clinically approved immune checkpoint blockade therapies. Here, we describe combination therapies of tumor-selective mRNA LNPs that achieve strong and lasting anti-tumor efficacy in syngeneic tumor models. We observed regression of established tumors, complete responses (CRs) and improved overall survival. At efficacious doses the mRNA LNPs were well tolerated while driving anti-tumor immune activation and modulation of the tumor microenvironment. Mouse blood hematology and chemistry analyses were within a normal range. Similarly, pathological immunohistochemistry analysis of liver, spleen and bone marrow revealed no findings. In summary, our data support the feasibility of onco-selective mRNA combination treatment of a variety of cancers with poor T cell infiltration and immunosuppressive TME, major obstacles in cancer immunotherapy. Citation Format: Manfred Kraus, Rudy Christmas, Tom A. Addison, Yulia Rybakova, Leona Lee, Jieni Xu, Mark Krimmer, Cafer Ozdemir, Burak Yilmaz, Yusuf Erkul. Combination therapy with onco-selective mRNA LNPs targets the complex immunosuppressive tumor microenvironment and is well tolerated at efficacious doses [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3232.
تدمد: 1538-7445
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::6ad865be37f34f7eb9ca264f4992b147
https://doi.org/10.1158/1538-7445.am2023-3232
رقم الأكسشن: edsair.doi...........6ad865be37f34f7eb9ca264f4992b147
قاعدة البيانات: OpenAIRE