Development and implementation of multiplex methods to understand the biology and heterogeneity of patient-derived cancer models

摘要 癌症的实验模型提供了破译癌症的分子基础和开发癌症治疗的方法。 新的治疗剂。迄今为止，大多数癌症研究采用已建立的癌细胞系， 基因工程小鼠模型。尽管这些模型为许多问题提供了深刻的见解， 在癌症发生和进展方面，这些模型中的每一个都有重要的局限性，包括适应性 培养（细胞系），缺乏基因组不稳定性（小鼠模型），以及光谱的代表性不足 人类癌症的突变和亚型。下一代癌症模型（NGCM），如类器官 最近已经开发了一些模型。NGCM解决了先前模型的许多缺陷，并有望加速 癌症研究和实验性治疗工作。 最近的方法学进展现在使得有可能创建患者来源的癌细胞系， 提高效率的类器官。当与基因组分析相结合时，这些新模型可以促进新的 对人类癌症的深入研究然而，类器官需要复杂的培养条件，并显示出不同的细胞形态。 这些特性对标准分子和细胞生物学技术的实施提出了挑战。到 为了促进类器官模型在研究界的广泛使用，我们必须开发创新的 技术来克服这些挑战，并使研究类器官的一系列癌症表型。 在这个项目中，我们将利用我们在基因组规模和信息学方法开发方面的专业知识， 以及我们的工作，以开发高通量的方法，以获得许多HCMI模型的目标， 在人类癌症模型创建的患者源性类器官中进行遗传和小分子筛选 倡议（HCMI）。此外，我们将使用创新的方法来审问细胞状态的可塑性和异质性 在这些模型中。这些研究将允许癌症研究界进行高通量和低通量 分析患者衍生模型，并提供对稳定性和表型的深入了解， 这些模型。虽然我们将专注于使用胰腺癌类器官的技术开发工作，但我们 预计本提案中开发的方法将广泛适用于许多不同的模型， 一系列癌症类型。 在目标1中，我们将开发和实施一种高度多重的方法来筛选患者来源的类器官 小分子和基因试剂的模型。这些研究将提供一个强有力的方法， 以高通量询问HCMI模型。在目标2中，我们将以初步研究为基础，这些研究表明， 患者来源的类器官表现出异质性和表达表型的快速变化。我们会审问 这些状态变化的动态，并使用新的 开发了物理和测序方法。在目标3中，我们将在Project Achilles和DepMap的基础上进行构建。 （www.DepMap.org）来创建和实施优化的基因组规模CRISPR-Cas9文库，其允许将CRISPR-Cas9文库与CRISPR-Cas9文库结合。 对患者源性类器官中遗传依赖性的系统遗传询问。 我们预计，这些研究将创造新的方法，允许严格评估HCMI模型， 以及发现胰腺癌的新生物标志物和治疗靶点。更广泛地说，这些 研究将提供关键的原则证明，这些方法可以被其他人用来研究特定的 下一代癌症模型中的表型，如类器官。

Abstract Experimental models of cancer provide the means both to decipher the molecular basis of cancer and to develop new therapeutic agents. To date, most cancer research has employed established cancer cell lines and genetically engineered mouse models. Although these models have provided tremendous insight into many aspects of cancer initiation and progression, each of these models has important limitations, including adaptation to culture (cell lines), lack of genomic instability (mouse models), and inadequate representation of the spectrum of mutations and subtypes of human cancers. Next generation cancer models (NGCMs) such as organoid models have recently been developed. NGCMs address many deficits of prior models and promise to accelerate cancer research and experimental therapeutic efforts. Recent methodological advances now make it possible to create patient-derived cancer cell lines and organoids with increased efficiency. When coupled with genomic analysis, these new models may facilitate new insights into human cancers. However, organoids require complex culture conditions and display distinct properties that pose challenges for implementation of standard molecular and cell biology techniques. To facilitate widespread use of organoid models within the research community, we must develop innovative technologies to overcome these challenges and enable study of organoids for a range of cancer phenotypes. In this Project, we will build on our expertise in the development of genome scale and informatic methods as well as our work to derive many of the HCMI models with the goal of developing high throughput approaches to perform genetic and small molecule screens in patient-derived organoids created by the Human Cancer Models Initiative (HCMI). In addition, we will use innovative methods to interrogate cell state plasticity and heterogeneity in these models. These studies will allow the cancer research community to perform both high and low throughput analyses in patient-derived models and to provide deep insight into the stability and phenotypes represented by these models. While we will focus our technology development efforts using pancreatic cancer organoids, we anticipate that the approaches developed in this proposal will be widely applicable to many different models from a range of cancer types. In Aim 1, we will develop and implement a highly multiplexed method to screen patient-derived organoid models with both small molecules and genetic reagents. These studies will provide a powerful approach to interrogating HCMI models at high throughput. In Aim 2, we will build on our preliminary studies that indicate that patient-derived organoids exhibit heterogeneity and rapid shifts in expressed phenotypes. We will interrogate the dynamics of these state changes and assess the degrees of heterogeneity in these models using newly developed physical and sequencing methodology. In Aim 3, we will build on Project Achilles and the DepMap (www.DepMap.org) to create and implement an optimized genome scale CRISPR-Cas9 library that permits the systematic genetic interrogation of genetic dependencies in patient-derived organoids. We anticipate that these studies will create new methods that permit rigorous evaluation of HCMI models as well as the discovery of novel biomarkers and therapeutic targets in pancreatic cancer. More broadly, these studies will provide critical proof of principle that these methods can be used by others to study specific phenotypes in next generation cancer models such as organoids.