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

• 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
• 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

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 在活哺乳动物细胞中，RNA传感器的需求迫切。随着单细胞RNA的冒险 测序，如果尚未可用，则可以轻松获得任何单元格类型的转录组。相反，我们是 仍然需要一种通用方法来对这种转录组信息采取行动。如果我们可以一般表达 根据特定细胞类型的任意效应子蛋白根据其转录标记，我们将转换 大量的基础研究和生物医学应用，例如免疫学，神经科学和癌症 治疗。此外，我们希望此类传感器可以编程并在转录后级别运行。 一种受益于此类传感器的承诺用例是癌症消融，使用一种方法 称为“电路为药物”，其中一个遗传矢量编码整个“电路”（隐喻 旨在相互调节并实施特定功能的生物分子被细胞内传递。 该电路将根据癌症的标志感知细胞状态（即，特定RNA的过表达 或特定突变的存在），处理信号并相应地交付特定的热有效载荷 在癌细胞中，直接杀死它们，同时教育免疫系统以搜索和破坏其他癌细胞。 以前的努力在很大程度上依赖于链位移，这是基于核酸的信号的成功策略 处理外部细胞。但是，它们的功能仍然不足以在活哺乳动物细胞内部 最有可能是因为在链位移期间形成的双链RNA（DSRNA）病毒感染 并由哺乳动物蛋白在免疫途径中积极参与。我们假设了这一点，因为它是 不可能逃避无所不在的dsRNA相互作用蛋白质，接受它们是明智的。在此提案中， 我们将利用内源性的人类酶，这些酶识别并专门编辑dsRNA，创建传感器 可以编程以响应任意RNA转录本（“触发器”）。 首先，我们将在体外使用快速设计构建测试周期来优化传感器性能。我们将重点关注 通过设计传感器配置及其序列选择，增加传感器输出，以响应触发器， 我们将表征传感器如何影响和受细胞环境影响。第二，启用 不同触发水平的定量差异和多个触发器的集成，我们将设计 第三设定的修改和逻辑门。第三，利用独特的转录后性质 这样的传感器和大门，我们将它们与mRNA或溶瘤RNA病毒作为递送向量相结合，它们 传统上很难控制。最后，我们将验证性能和治疗性 癌细胞系中传感器，大门和RNA载体的电势。 拟议项目的未来方向包括传感器，逻辑门的连续优化， 和向量，在更现实的癌症模型中测试它们，包括小鼠模型和患者衍生的类器官， 并将工具应用于其他字段。