Cancer Classifiers Based on RNA Sensors in Living Cells

基于活细胞中 RNA 传感器的癌症分类器

基本信息

批准号：
10570559
负责人：
Xiaojing J Gao
金额：
$ 19.41万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-30 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10570559
关键词：
Ablation Adenosine Affect Basic Science Binding Biological Assay Biomedical Research Cancer Model Cancer cell line Cells Collection Complement DNA Detection Double-Stranded RNA Engineering Enzymes Exhibits Future Genes Genetic Transcription Genetic Vectors Goals Guanine Hallmark Cell Health Benefit Human Immune Immune system Immunology In Vitro Inosine Intervention Libraries Logic Malignant Neoplasms Mammalian Cell Medicine Messenger RNA Metaphor Methods Modification Molecular Mus Mutation Names Nature Neurosciences Nucleic Acids Oncolytic Oncolytic viruses Organism Organoids Outcome Output Pathway interactions Patients Performance Pharmacology Process Proteins Public Health RNA RNA Editing RNA Viruses Reporter Reporting Signal Transduction Specificity Testing Therapeutic Transcript Translations Variant Vesicular stomatitis Indiana virus Viral Viral Proteins Viral Vector Virus Diseases Virus Replication adenosine deaminase base cancer biomarkers cancer cell cancer therapy cell type clinically relevant conventional therapy delivery vehicle design design-build-test differential expression improved in vivo mouse model oncolytic virotherapy operation overexpression programs response senescence sensor signal processing single-cell RNA sequencing tool transcriptome transcriptomics vector vector vaccine

项目摘要

Abstract There is a critical need for RNA sensors in living mammalian cells. With the advent of single-cell RNA sequencing, the transcriptome of any cell type is readily obtainable if not already available. In contrast, we are still in urgent need for a universal method to act on such transcriptomic information. If we can genetically express arbitrary effector proteins in specific cell types according to their transcriptional markers, we would transform large swaths of basic research and biomedical applications, such as immunology, neuroscience, and cancer therapy. In addition, we would like such sensors to be programmable and operate at the post-transcription level. One promising use case that would benefit from such sensors is cancer ablation, using an approach dubbed “circuits as medicine”, where a genetic vector encoding an entire “circuit” (metaphor for a collection of biomolecules engineered to regulate each other and implement specific functions) is delivered intracellularly. The circuit will sense the cellular states based on hallmarks of cancer (i.e., the overexpression of specific RNAs or the presence of specific mutations), process the signals, and deliver specific therapeutic payloads accordingly in cancer cells, directly killing them while educating the immune system to search and destroy other cancer cells. Previous efforts largely relied on strand displacement, a successful strategy for nucleic acid-based signal processing outside cells. However, their functionality has remained inadequate inside living mammalian cells, most likely because the double-stranded RNA (dsRNA) formed during strand displacement signals viral infection and are actively engaged by mammalian proteins in the immune pathways. We hypothesize that, because it is impossible to evade the omnipresent dsRNA-interacting proteins, it is wiser to embrace them. In this proposal, we will leverage endogenous human enzymes that recognize and specifically edit dsRNA, to create sensors that can be programmed to respond to arbitrary RNA transcripts (“triggers”). First, we will use fast design-build-test cycles in vitro to optimize sensor performance. We will focus on increasing sensor output in response to triggers by engineering the sensor configuration and its sequence choice, and we will characterize how the sensor affects and is affected by the cellular context. Second, to enable the quantitative distinction of different trigger levels and the integration of multiple triggers, we will engineer threshold-setting modifications and AND logic gates. Third, leveraging the unique post-transcriptional nature of such sensors and gates, we will combine them with mRNA or an oncolytic RNA virus as delivery vectors, which has traditionally been difficult to control. Last by not least, we will validate the performance and the therapeutic potential of the sensors, gates, and the RNA vectors in cancer cell lines. The future directions of the proposed project include continual optimization of the sensors, logic gates, and vectors, testing them in more realistic cancer models including mouse models and patient-derived organoids, and applying the tools to other fields.

抽象的随着单细胞 RNA 的出现，活体哺乳动物细胞迫切需要 RNA 传感器。通过测序，任何细胞类型的转录组即使尚未可用，也很容易获得。如果我们能够通过基因表达的话，仍然迫切需要一种通用的方法来作用于此类转录组信息。根据其转录标记，特定细胞类型中的任意效应蛋白，我们将转化大量基础研究和生物医学应用，例如免疫学、神经科学和癌症此外，我们希望此类传感器能够编程并在转录后水平运行。受益于此类传感器的一个有前途的用例是癌症消融，使用一种方法被称为“电路作为医学”，其中编码整个“电路”的遗传载体（隐喻为一组被设计为相互调节并实现特定功能的生物分子）在细胞内传递。该电路将根据癌症特征（即特定 RNA 的过度表达）来感知细胞状态或特定突变的存在），处理信号并相应地传递特定的治疗有效负载在癌细胞中，直接杀死它们，同时教育免疫系统搜索并摧毁其他癌细胞。以前主要依赖于链置换工作，这是基于核酸的信号的成功策略然而，它们在活哺乳动物细胞内的功能仍然不足，很可能是因为链置换过程中形成的双链 RNA (dsRNA) 发出病毒感染的信号并且在免疫途径中被哺乳动物蛋白积极参与，我们参与其中，因为它确实如此。逃避无处不在的 dsRNA 相互作用蛋白是不可能的，但在这个提议中，拥抱它们是明智的。我们将利用识别和特异性编辑 dsRNA 的内源性人类酶来创建传感器可以通过编程来响应任意 RNA 转录本（“触发器”）。首先，我们将使用快速的体外设计-构建-测试循环来优化传感器性能。通过设计传感器配置及其序列选择来增加传感器输出以响应触发，其次，我们将描述传感器如何影响细胞环境以及如何受细胞环境影响。不同触发级别的定量区分以及多个触发的集成，我们将设计第三，利用独特的转录后性质。这样的传感器和门，我们将它们与 mRNA 或溶瘤 RNA 病毒作为传递载体结合起来，这传统上很难控制。最后，我们将验证性能和治疗效果。癌细胞系中传感器、门和 RNA 载体的潜力。该项目的未来方向包括传感器、逻辑门的持续优化，和载体，在更真实的癌症模型中测试它们，包括小鼠模型和患者来源的类器官，并将这些工具应用到其他领域。