Rapid ex vivo biosensor cultures to assess dependencies in gastroesophageal cancer

预测给定分子特征的依赖性的能力 患者的肿瘤是癌症精准医学的核心。 CRISPR/Cas9的系统应用和药理学 已建立的癌症模型中的工具显示出发现新靶点的巨大潜力。然而，现有模型 发展方法需要长时间的培养时间，在此期间进化压力 减少异质性。而且，为某些肿瘤类型创建长期模型仍然具有挑战性 基因型，使得使用微扰工具通过实验映射依赖关系变得具有挑战性。 为了应对这些挑战，我们的首要目标是开发“快速离体肿瘤生物传感器”，从而 我们将能够在癌细胞的短期“培养”中询问癌症依赖性 来自患者活检/手术/液体收集作为一种新颖的研究级癌症实验模型。在做 因此，我们的目标是将药物或 CRISPR/Cas9 扰动的时间与亚细胞的保存结合起来 异质性。如果成功，我们假设这种建模方法将更准确地概括 患者肿瘤，最终可能为临床前治疗研究奠定更坚实的基础。这部作品 还应该大幅扩大可以询问的患者样本的比例。 在这里，我们建议使用胃食管腺癌（GEA）作为该策略的测试案例，因为我们 经验以及显着的肿瘤内异质性的存在。然而，这部小说一旦成立， 建模平台应支持广泛的基本问题和转化问题（对于 GEA 和其他 肿瘤），需要包含异质细胞群的模型格式。 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； (2) 开发 能够直接可视化和干扰来自匹配患者腹水或分解原代细胞的单细胞 使用无标记成像方法进行离体肿瘤。我们将使用这些方法对彼此进行基准测试 相同的临床注释、连续收集的患者样本。按照本 RFP 的说明，我们 专注于技术开发目标，而不是更深入的机制研究。我们专注于 对预测进行基准测试并评估再现性、敏感性和特异性。这项工作具有创新性，在 它汇集了功能基因组学和先进计算方法交叉领域的专业知识 用于图像分析和 GEA 基因组学。如果成功，这项努力可能会通过建立一个 基金会将该方法扩展到其他疾病（肿瘤和非癌症）适应症。

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.