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

开发和实施多重方法来了解源自患者的癌症模型的生物学和异质性

• 批准号: 10458506
• 负责人: WILLIAM C HAHN
• 金额: $ 96.37万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10458506
• 关键词: Address; Affect; Aftercare; Archives; Biological Assay; Biological Markers; Biology; Biopsy Specimen; CRISPR library; CRISPR screen; CRISPR/Cas technology; Cancer Biology; Cancer Model; Cancer cell line; Cell Line; Cell Proliferation; Cells; Cellular biology; Clinical Trials; Collection; Communities; Complex; Coupled; Data; Dependence; Development; Disease; Drug resistance; Evaluation; Evolution; Exhibits; Experimental Models; Future; Gene Expression Profile; Genes; Genetic; Genetic Transcription; Genetically Engineered Mouse; Genomic Instability; Genomics; Goals; Heterogeneity; Human; Immunofluorescence Immunologic; Informatics; Investigational Therapies; Kinetics; Libraries; Malignant Neoplasms; Malignant neoplasm of pancreas; Maps; Methodology; Methods; Modeling; Molecular; Molecular Biology; Mutation; Oncogenes; Organoids; Pancreas; Pathway interactions; Patients; Phase; Phenotype; Primary Neoplasm; Property; Proteins; Protocols documentation; Reagent; Research; Screening for cancer; Signal Transduction; Techniques; Testing; Therapeutic; Therapeutic Agents; Tissue Microarray; Tissues; Validation; Work; analysis pipeline; anticancer research; base; cancer initiation; cancer type; clinically relevant; cost effective; design; drug sensitivity; effective therapy; experience; functional genomics; genetic manipulation; genome-wide; implementation barriers; improved; innovation; innovative technologies; insight; loss of function; matrigel; mouse model; new technology; new therapeutic target; next generation; novel; novel marker; novel strategies; novel therapeutics; patient screening; precision oncology; predictive marker; programs; screening; single cell sequencing; single-cell RNA sequencing; small molecule; small molecule therapeutics; technology development; therapeutic target; three dimensional cell culture; transcriptome sequencing; treatment response; tumor; tumor progression

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.

抽象的 癌症实验模型提供了破译癌症分子基础和开发癌症的手段。 新的治疗剂。迄今为止，大多数癌症研究都采用了已建立的癌细胞系 基因工程小鼠模型。尽管这些模型为许多领域提供了深刻的见解 在癌症发生和进展的各个方面，这些模型都有重要的局限性，包括适应 培养（细胞系）、缺乏基因组不稳定性（小鼠模型）以及谱图代表性不足 人类癌症的突变和亚型。下一代癌症模型 (NGCM)，例如类器官 最近开发了模型。 NGCM 解决了先前模型的许多缺陷并承诺加速 癌症研究和实验治疗工作。 最近的方法学进展现在使得创建源自患者的癌细胞系和 类器官的效率提高。当与基因组分析相结合时，这些新模型可能会促进新的 对人类癌症的见解。然而，类器官需要复杂的培养条件并表现出独特的特征 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 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 克服这些挑战并能够研究一系列癌症表型的类器官的技术。 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， 以及我们推导许多 HCMI 模型的工作，目标是开发高吞吐量方法 对人类癌症模型创建的源自患者的类器官进行遗传和小分子筛选 倡议（HCMI）。此外，我们将使用创新方法来探究细胞状态的可塑性和异质性 在这些模型中。这些研究将使癌症研究界能够同时进行高通量和低通量研究 对源自患者的模型进行分析，并深入了解以下所代表的稳定性和表型 这些模型。虽然我们将重点使用胰腺癌类器官进行技术开发，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，我们将开发并实施一种高度多重方法来筛选源自患者的类器官 具有小分子和遗传试剂的模型。这些研究将为 以高吞吐量询问 HCMI 模型。在目标 2 中，我们将基于我们的初步研究表明： 患者来源的类器官表现出异质性和表达表型的快速变化。我们将审问 这些状态变化的动态并使用新方法评估这些模型中的异质性程度 开发了物理和测序方法。在目标 3 中，我们将在 Project Achilles 和 DepMap 的基础上构建 (www.DepMap.org) 创建并实施优化的基因组规模 CRISPR-Cas9 文库，该文库允许 对源自患者的类器官的遗传依赖性进行系统的遗传询问。 我们预计这些研究将创造新的方法，允许严格评估 HCMI 模型 以及胰腺癌新生物标志物和治疗靶点的发现。更广泛地说，这些 研究将提供关键的原理证明，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（例如类器官）的表型。

项目成果

Sparse dictionary learning recovers pleiotropy from human cell fitness screens.

• DOI: 10.1016/j.cels.2021.12.005
• 发表时间: 2022-04-20
• 期刊: Cell systems
• 影响因子: 9.3

NCOA4-Mediated Ferritinophagy Is a Pancreatic Cancer Dependency via Maintenance of Iron Bioavailability for Iron-Sulfur Cluster Proteins.

• DOI: 10.1158/2159-8290.cd-22-0043
• 发表时间: 2022-09-02
• 期刊: CANCER DISCOVERY
• 影响因子: 28.2
• 作者: Santana-Codina, Naiara;del Rey, Maria Quiles;Kapner, Kevin S.;Zhang, Huan;Gikandi, Ajami;Malcolm, Callum;Poupault, Clara;Kuljanin, Miljan;John, Kristen M.;Biancur, Douglas E.;Chen, Brandon;Das, Nupur K.;Lowder, Kristen E.;Hennessey, Connor J.;Huang, Wesley;Yang, Annan;Shah, Yatrik M.;Nowak, Jonathan A.;Aguirre, Andrew J.;Mancias, Joseph D.
• 通讯作者: Mancias, Joseph D.

Spatially Resolved Single-Cell Assessment of Pancreatic Cancer Expression Subtypes Reveals Co-expressor Phenotypes and Extensive Intratumoral Heterogeneity.

• DOI: 10.1158/0008-5472.can-22-3050
• 发表时间: 2023-02-03
• 期刊: CANCER RESEARCH
• 影响因子: 11.2
• 作者: Williams, Hannah L.;Costa, Andressa Dias;Zhang, Jinming;Raghavan, Srivatsan;Winter, Peter S.;Kapner, Kevin S.;Ginebaugh, Scott P.;Vayrynen, Sara A.;Vayrynen, Juha P.;Yuan, Chen;Navia, Andrew W.;Wang, Junning;Yang, Annan;Bosse, Timothy L.;Kalekar, Radha L.;Lowder, Kristen E.;Lau, Mai Chan;Elganainy, Dalia;Morales-Oyarvide, Vicente;Rubinson, Douglas A.;Singh, Harshabad;Perez, Kimberly;Cleary, James M.;Clancy, Thomas E.;Wang, Jiping;Mancias, Joseph D.;Brais, Lauren K.;Hill, Emma R.;Kozak, Margaret M.;Linehan, David C.;Dunne, Richard F.;Chang, Daniel T.;Koong, Albert C.;Hezel, Aram F.;Hahn, William C.;Shalek, Alex K.;Aguirre, Andrew J.;Nowak, Jonathan A.;Wolpin, Brian M.
• 通讯作者: Wolpin, Brian M.

Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer.

机器学习将 T 细胞功能和空间定位与胰腺癌的新辅助免疫治疗和临床结果联系起来。

• DOI: 10.1101/2023.10.20.563335
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Blise,KatieE;Sivagnanam,Shamilene;Betts,CourtneyB;Betre,Konjit;Kirchberger,Nell;Tate,Benjamin;Furth,EmmaE;DiasCosta,Andressa;Nowak,JonathanA;Wolpin,BrianM;Vonderheide,RobertH;Goecks,Jeremy;Coussens,LisaM;Byrne,KatelynT
• 通讯作者: Byrne,KatelynT

Structure-function analysis of the SHOC2-MRAS-PP1C holophosphatase complex.

• DOI: 10.1038/s41586-022-04928-2
• 发表时间: 2022-09
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Kwon, Jason J.;Hajian, Behnoush;Bian, Yuemin;Young, Lucy C.;Amor, Alvaro J.;Fuller, James R.;Fraley, Cara V.;Sykes, Abbey M.;So, Jonathan;Pan, Joshua;Baker, Laura;Lee, Sun Joo;Wheeler, Douglas B.;Mayhew, David L.;Persky, Nicole S.;Yang, Xiaoping;Root, David E.;Barsotti, Anthony M.;Stamford, Andrew W.;Perry, Charles K.;Burgin, Alex;McCormick, Frank;Lemke, Christopher T.;Hahn, William C.;Aguirre, Andrew J.
• 通讯作者: Aguirre, Andrew J.