Rapid ex vivo biosensor cultures to assess dependencies in gastroesophageal cancer

预测依赖性的能力，给定的分子特征， 患者的肿瘤是癌症精准医疗的核心。CRISPR/Cas9的系统性使用和药理学 在已建立的癌症模型中使用的工具显示出发现新靶点的巨大潜力。然而，现有模式 发展方法需要长时间的培养，在此期间， 降低异质性。而且，为某些肿瘤类型建立长期模型仍然具有挑战性， 基因型，使得使用扰动工具来实验性地映射依赖性具有挑战性。 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”， 我们将能够在短期的癌细胞“培养”中， 从患者活检/手术/液体收集作为一种新的研究级癌症实验模型。做 因此，我们的目标是将药物或CRISPR/Cas9扰动的时机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 患者的肿瘤，并可能最终作为临床前治疗研究的更坚实的基础。这项工作 还应该充分扩大可以被询问的患者样本的分数。 在这里，我们建议使用胃食管腺癌（GEA）作为这种策略的测试案例，因为我们 经验以及存在显著的肿瘤内异质性。然而，一旦建立，这部小说 建模平台应支持广泛的基本和转换问题（GEA和其他 肿瘤），其需要包括异质细胞群的模型形式。 我们的目标将通过两个具体目标实现，包括（1）使用在 CRISPR/Cas9编辑的快速时间框架，以验证新兴的GEA依赖性;以及（2）开发 能够直接可视化和干扰来自匹配患者腹水或解聚的原代细胞的单细胞 使用无标记成像方法离体肿瘤。我们将使用以下方法对这些方法进行基准测试： 相同的临床注释，连续收集的患者样本。根据本RFP的说明，我们 专注于技术开发的目标，而不是更深入的机械研究。我们专注于 基准预测和评估再现性、灵敏度和特异性。这项工作是创新的，在 它将功能基因组学、先进的计算方法 用于图像分析和GEA基因组学。如果成功，这一努力将产生重大影响， 基金会将这种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.