A Quantitative Multiplexed Platform for the Pharmacogenomic Analysis of Lung Cancer

用于肺癌药物基因组学分析的定量多重平台

• 批准号: 9155816
• 负责人: Dmitri Petrov
• 金额: $ 55.44万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-05 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9155816
• 关键词: Alleles; Biological Models; Cancer Etiology; Cell Culture System; Cell Line; Cessation of life; Clinic; Clinical; Clinical Research; Clinical Treatment; Clinical Trials; Clinical Trials Design; Clustered Regularly Interspaced Short Palindromic Repeats; Data; Development; Environment; Frequencies; Gene Silencing; Generations; Genetic; Genetic Engineering; Genetically Engineered Mouse; Genome engineering; Genomics; Genotype; Goals; Growth; Growth Factor Receptors; Health; High-Throughput Nucleotide Sequencing; Human; Immune system; Immunotherapy; In Vitro; Individual; Investigation; Lentivirus Vector; Link; Lung Adenocarcinoma; Lung Neoplasms; Malignant Neoplasms; Malignant neoplasm of lung; Maps; Mediating; Methods; Modeling; Mus; Patients; Pharmaceutical Preparations; Pharmacogenomics; Positioning Attribute; Research Personnel; Site; Staging; System; Techniques; Testing; Time; Translating; Transplantation; Tumor Suppressor Genes; Tumor Suppressor Proteins; Tumor-Suppressor Gene Inactivation; Validation; Woman; Xenograft Model; Xenograft procedure; base; cancer care; cancer cell; cancer genetics; cancer genome; cancer therapy; cancer type; cost; cost effective; flexibility; genome editing; genome sequencing; in vivo; innovation; mathematical methods; men; mouse model; novel; novel strategies; oncology; pre-clinical; pre-clinical therapy; response; screening; targeted treatment; translational study; treatment response; tumor; vector

PROJECT SUMMARY Lung cancer is a major health burden, leading to more deaths than the next four major cancer types combined. Despite advances in clinical cancer genome sequencing and the development of many targeted therapies, understanding the relationship of tumor genotype to therapeutic response remains a major obstacle to translating existing drugs into effective cancer treatments in the clinic. Pharmacogenomic analysis of tumor response is often extrapolated from the analysis of patients' tumor responses or modeled using in vitro cultured cell line systems, but investigating the effect of tumor genotype on drug response in cell lines, patient-derived xenograft models, or patients themselves all have severe limitations. Genetically-engineered mouse models have emerged as particularly rigorous in vivo systems with which to test early stage oncology therapies and represent tractable models with which to investigate the impact of tumor genotype on therapy response. Current genetically-engineered mouse models are time-consuming, cost-intensive, and have unavoidable technical and experimental variability that has limited their use in translational studies. We have established a novel multiplexed somatic genome-editing approach that will allow the quantification of genotype-specific drug responses. This in vivo approach will increase in precision and scope of translational cancer pharmacogenomics studies. To quantify the effect of tumor suppressor gene inactivation on lung cancer growth, we established a system that combines somatic Cas9-mediated gene inactivation with existing genetically-engineered mouse models to generate ~30 different lung tumor genotypes. To quantify the exact size of each tumor and determine the size distribution of each tumor genotype, we induce tumors with barcoded vectors and use high-throughput sequencing and statistical approaches to determine the number of cancer cells in each tumor. We will combine our quantitative pooled genome-editing approach with pre-clinical treatments to uncover genotype-specific therapy responses. We will quantify the responses of ~30 different genotypes of tumors to several therapies that have been shown to have genotype-specific effects in lung adenocarcinoma models. This will extend our understanding of the genomic modifiers of treatment responses and define the experimental and statistical parameters to enable the most efficient use of these models for translational studies. Finally, by performing pre-clinical/co-clinical trials for targeted therapies across >30 tumor genotypes in parallel we will generate a pharmacogenomic map connecting lung adenocarcinoma genotype to targeted therapy response. Our ongoing clinical interactions will allow validation of our pharmacogenomic predictions in lung adenocarcinoma patients. This flexible system can incorporate additional tumor suppressors, allows for the investigation of genotype-specific responses to other therapies including immunotherapies, and be adapted to other cancer types. The techniques described in this proposal are ideally positioned to become a mainstay of pre-clinical/co-clinical trial design.

项目摘要 肺癌是一个主要的健康负担，导致的死亡人数超过了接下来的四种主要癌症类型 加起来尽管临床癌症基因组测序和许多靶向治疗的发展取得了进展， 然而，了解肿瘤基因型与治疗反应的关系仍然是一个主要障碍。 将现有的药物转化为临床上有效的癌症治疗方法。肿瘤的药物基因组学分析 反应通常从患者肿瘤反应的分析中推断或使用体外培养的细胞模型来建模。 细胞系系统，但研究肿瘤基因型对细胞系药物反应的影响，患者来源 异种移植模型或患者本身都具有严重的局限性。基因工程小鼠模型 已经成为测试早期肿瘤治疗的特别严格的体内系统， 代表研究肿瘤基因型对治疗反应的影响的易处理模型。 目前的基因工程小鼠模型是耗时的，成本密集的，并且具有不可避免的缺陷。 技术和实验的可变性限制了它们在翻译研究中的使用。我们建立了 一种新的多重体细胞基因组编辑方法，可以量化基因型特异性药物 应答这种体内方法将增加转化癌症的精确度和范围 药物基因组学研究。量化抑癌基因失活对肺癌的影响 生长，我们建立了一个系统，结合体细胞Cas9介导的基因失活与现有的 基因工程小鼠模型产生约30种不同的肺肿瘤基因型。来量化 每个肿瘤的大小，并确定每个肿瘤基因型的大小分布，我们诱导肿瘤与 条形码化载体，并使用高通量测序和统计方法来确定 每个肿瘤中的癌细胞我们将联合收割机将我们的定量合并基因组编辑方法与临床前 基因型特异性的治疗反应。我们将量化约30种不同的反应， 肿瘤的基因型与几种已显示在肺中具有基因型特异性效应的疗法 腺癌模型。这将扩展我们对治疗反应的基因组修饰剂的理解 并定义实验和统计参数，使这些模型的最有效的使用， 翻译研究最后，通过对超过30种肿瘤进行靶向治疗的临床前/协同临床试验， 我们将生成一个药物基因组学图谱，将肺腺癌基因型与 靶向治疗反应。我们正在进行的临床相互作用将使我们的药物基因组学验证 肺腺癌患者的预测。这种灵活的系统可以将额外的肿瘤 抑制子，允许研究对其他疗法的基因型特异性反应，包括 免疫疗法，并适用于其他癌症类型。本提案中描述的技术在理想情况下 定位成为临床前/协同临床试验设计的中流砥柱。