A RIPK1-specific PROTAC Degrader Achieves Potent Antitumor Activity by Enhancing Immunogenic Cell Death
Immunity(2024)
Inst Canc Res | MRC Toxicology Unit | UCL | Univ Copenhagen | LSU Hlth Shreveport | Auburn Univ
Abstract
Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) functions as a critical stress sentinel that coordinates cell survival, inflammation, and immunogenic cell death (ICD). Although the catalytic function of RIPK1 is required to trigger cell death, its non-catalytic scaffold function mediates strong pro-survival signaling. Accordingly, cancer cells can hijack RIPK1 to block necroptosis and evade immune detection. We generated a small-molecule proteolysis-targeting chimera (PROTAC) that selectively degraded human and murine RIPK1. PROTAC-mediated depletion of RIPK1 deregulated TNFR1 and TLR3/4 signaling hubs, accentuating the output of NF-κB, MAPK, and IFN signaling. Additionally, RIPK1 degradation simultaneously promoted RIPK3 activation and necroptosis induction. We further demonstrated that RIPK1 degradation enhanced the immunostimulatory effects of radio- and immunotherapy by sensitizing cancer cells to treatment-induced TNF and interferons. This promoted ICD, antitumor immunity, and durable treatment responses. Consequently, targeting RIPK1 by PROTACs emerges as a promising approach to overcome radio- or immunotherapy resistance and enhance anticancer therapies.
MoreTranslated text
Key words
RIPK1,cell death,necroptosis,inflammation,anticancer immunity,TNF,interferon,immunotherapy,TLR3,radiotherapy
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2011
被引用519 | 浏览
2012
被引用1191 | 浏览
2014
被引用302 | 浏览
2014
被引用317 | 浏览
2014
被引用230 | 浏览
2017
被引用16 | 浏览
2020
被引用30 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话