Rapid ex vivo biosensor cultures to assess dependencies in gastroesophageal cancer

在给定分子特征的情况下预测相关性的能力 患者的肿瘤是癌症精准医学的核心。CRISPR/CAS9与药理学的系统应用 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，现有的模式 发展方法需要长期的文化时间，在此期间，进化压力 减少异质性。而且，为某些肿瘤类型和组织建立长期模型仍然具有挑战性 基因类型，这使得使用微扰工具来试验性地绘制依赖关系图具有挑战性。 为了应对这些挑战，我们的首要目标是开发“快速体外肿瘤生物传感器”，从而 我们将能够在所采集的癌细胞的直接短期“培养”中询问癌症相关性。 来自患者的活组织检查/手术/液体收集作为一种新的研究级癌症实验模型。正在做 因此，我们的目标是将药物或CRISPR/Cas9干扰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 它可以帮助患者治疗肿瘤，并最终为临床前治疗研究奠定更坚实的基础。这部作品 还应大幅扩大可供讯问的患者样本的比例。 在这里，我们建议使用胃食管腺癌(GEA)作为这一策略的测试案例，因为我们的 经验以及存在明显的肿瘤内异质性。然而，一旦确立，这部小说 建模平台应支持广泛的基本问题和翻译问题(针对GEA和其他 肿瘤)，需要包括异质细胞群体的模型格式。 我们的目标将通过两个具体目标来实现，包括：(1)使用患者在 CRISPR/CAS9编辑以验证新出现的GEA依赖关系的快速时间框架；以及(2)开发 能够直接从匹配的患者腹水或分离的原代腹水中观察和干扰单个细胞 肿瘤体外使用无标记成像方法。我们将使用以下工具对这些方法进行基准测试 同样的临床注解，连续收集的患者样本。按照本RFP的说明，我们 专注于以技术开发为重点的目标，而不是更深入的机制研究。我们专注于 以预测为基准，评估重复性、敏感性和特异度。这项工作具有创新性，在 它汇集了功能基因组学、先进计算方法等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，这一努力可能会产生重大影响，建立一个 基金会将这一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.