Rapid ex vivo biosensor cultures to assess dependencies in gastroesophageal cancer

快速离体生物传感器培养物评估胃食管癌的依赖性

基本信息

批准号：
10543682
负责人：
Jesse Samuel Boehm
金额：
$ 69.53万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
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：
2022-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10543682
关键词：
Address Ascites Benchmarking Biopsy Biopsy Specimen Biosensor CRISPR/Cas technology Cancer Model Cell Line Cell Survival Cells Clinical Collection Data Data Collection Dependence Derivation procedure Development Disease Drug Exposure Esophagus Experimental Models Foundations Future Genomics Genotype Goals Heterogeneity Image Instruction Label Liquid substance Malignant Neoplasms Maps Methods Microscopy Mission Modeling Molecular Monitor Morbidity - disease rate Operative Surgical Procedures Organoids Outcome Study Paracrine Communication Patients Pharmaceutical Preparations Pharmacology Physiological Population Primary Neoplasm Public Health Reagent Reproducibility Research Research Personnel Research Support Resolution Sampling Sensitivity and Specificity Stomach Neoplasms Survival Rate Testing Therapeutic Therapeutic Studies Time TimeLine Tissues Work base cancer cell cell type cohort experience functional genomics gastroesophageal adenocarcinoma gastroesophageal cancer imaging approach imaging modality improved innovation light microscopy microscopic imaging model development mortality novel novel strategies patient response pre-clinical precision medicine precision oncology preservation pressure response single-cell RNA sequencing success technology development tool translational cancer research tumor tumor heterogeneity

项目摘要

The ability to predict dependencies given the molecular features of a patient’s tumor is central to cancer precision medicine. The systematic use of CRISPR/Cas9 and pharmacologic tools in established cancer models is showing great potential to discover new targets. However, existing model development approaches require long periods of culture time during which evolutionary pressures reduce heterogeneity. And, it remains challenging to create long-term models for certain tumor types and genotypes, making it challenging to use perturbational tools to experimentally map dependencies. To address these challenges, our overarching goal is to develop ‘rapid ex vivo tumor biosensors’ whereby we would be able to interrogate cancer dependencies in an immediate short-term ‘culture’ of cancer cells taken from a patient biopsy/surgery/fluid collection as a novel research-grade experimental model of cancer. In doing so, we aim to couple the timing of drug or CRISPR/Cas9 perturbation with the preservation of subcellular heterogeneity. If successful, we hypothesize that this modelling approach will more accurately recapitulate patient tumors and may ultimately serve as a stronger foundation for preclinical therapeutic studies. This work should also substantially expand the fraction of patient samples that can be interrogated. Here, we propose using gastroesophageal adenocarcinoma (GEA) as a test case for this strategy due to our experience as well as the existence of marked intra-tumor heterogeneity. However, once established, this novel modeling platform should enable a wide range of basic and translational questions (both for GEA and other tumors) that require model formats that include heterogeneous cell populations. Our goal will be achieved via two Specific Aims including (1) using patient-derived organoids created on rapid time frames for CRISPR/Cas9 editing to validate emerging GEA dependencies; and (2) developing the ability to directly visualize and perturb single cells from matching patient ascites fluid or disaggregated primary tumors ex vivo using label-free imaging methods. We will benchmark these approaches against each other using the same clinically annotated, serially collected patient samples. In following the instructions for this RFP, we focus on technology-development focused goals as opposed to deeper mechanistic studies. We focus on benchmarking predictions and assessing reproducibility, sensitivity and specificity. This work is innovative, in that it brings together expertise at the intersection of functional genomics, advanced computational approaches for image-analysis and GEA genomics. If successful, this effort could have significant impact by establishing a foundation to expand this approach to other disease (tumor and non-cancer) indications.

给定A分子特征预测依赖的能力患者的肿瘤是癌症精确药物的核心。既定的癌症模型中的工具都可以发现新目标发展方法需要长时间的文化时间，在此期间减少异质性，并为某些肿瘤类型创建长期模型仍然具有挑战性基因型，使使用与实验地图依赖性相关的挑战。为了应对挑战，我们的总体目标是开发“快速的离体肿瘤生物传感器” 我们将能够在癌细胞的短期短期“培养”中询问癌症的依赖性从患者的活检/手术/流体收集作为癌症的新型研究级实验模型。因此，我们的目标是将药物或CRISPR/CAS9的时机与亚细胞的保存联系起来异质性。如果成功，我们假设建模方法将更准确患者肿瘤并最终可以成为该工作的强大基础。还应该大大扩大可以审问的贴图样品的贴合部分。在这里，我们建议使用胃食管治疗腺癌（GEA）作为该策略的测试案例经验以及明显的肿瘤内异质石的存在。建模平台应引起广泛的基本和翻译问题（对于GEA和其他肿瘤）需要模型格式，其中包括异质细胞群。我们的目标将通过两个具体目标来实现，包括（1）使用在 CRISPR/CAS9编辑的快速时间范围，以验证新兴的GEA依赖性；能够直接可视化和扰动单细胞匹配患者accits流体或分解原发性细胞肿瘤使用无标签的成像方法进行体内。同一临床注释，串行收集的患者样品。专注于技术发展的目标，而不是更深入的机理研究基准预测和评估复制品，灵敏度和特异性。它汇集了在功能基因组学交集，高级计算方法的交集中的专业知识对于图像 - 分析和GEA基因组学将此Apporoch扩展到其他疾病（肿瘤和非癌症）的基础。