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.
Pancreatic cancer is a common and deadly disease with few effective therapies. Recent technical advances now permit the creation of patient-derived cell lines and organoid models, but novel approaches are needed to enable the systematic study of many small molecule or genetic perturbations that target pancreatic cancer. In this application, we propose to develop these methods in patient-derived organoid models created by HCMI using state of the art genetic, small molecule and single cell sequencing approaches, which will not only allow us to understand how these models recapitulate pancreatic cancer biology but also facilitate the development of new biomarkers and therapeutic approaches.
