A patient-specific hiPSC model of breast cancer to identify genetic determinants of subclonal response and resistance to PARP inhibitors

项目总结 乳腺癌是全世界女性中最常见的癌症，每八名女性中就有一人受到影响。 在他们有生之年的美国。现有的乳腺癌患者衍生模型，包括细胞和 器官培养和患者来源的异种移植未能概括乳腺肿瘤发生的重要方面。 值得注意的是，这些模型源自晚期肿瘤，在培养中经历了遗传漂移和选择，并且 与高通量遗传和化学筛选不兼容。很明显，我们需要更强大的 乳腺癌的临床前模型，将基础癌症研究转化为有意义的 改善了患者的预后。人诱导多能干细胞(HiPSCs)是一种很有吸引力的工具 模拟乳腺癌是因为它们提供了一种可扩展的患者来源的细胞，这些细胞保留了分子 潜在癌细胞的特征，包括突变负担和药物敏感性。此外，作为HiPSCs 是从单个细胞克隆而来的，来自单个肿瘤的多个HiPSC系提供了一种工具来检查 亚克隆特异性在肿瘤发生和治疗反应中的作用。然而，到目前为止还没有研究检查过 HiPSCs在模拟乳腺肿瘤发生中的潜力。本研究旨在开发一种新的临床前模型。 通过从人的乳腺肿瘤中产生HiPSC系并使用这个模型来检测乳腺癌 患者和亚克隆对聚(ADP-核糖)聚合酶(PARP)抑制剂的特异性反应。PARP 抑制剂针对肿瘤细胞固有的DNA损伤修复缺陷，是一种有希望的治疗方法 高度侵袭性、BRCA突变的三阴性乳腺癌的亚群。在目标1中，我们将重新编写主程序 乳腺癌细胞体外分化为乳腺上皮细胞(MECs) 疾病模型。我们将使用深度测序来确定基因异质性的程度 HIPSC系的克隆异质性概括了原发肿瘤的克隆异质性。在目标2中，我们将使用不同的基因 来自BRCA突变乳腺肿瘤的HiPSC-MEC系检测DNA损伤的亚克隆差异 修复熟练，对PARP抑制剂的内在敏感性，以及获得PARP抑制剂耐药性的倾向。 将使用基因编辑从机制上验证PARP抑制剂抗性的亚克隆特异性机制 在这个模型中。这些目标的完成将满足对新的乳腺癌体外模型的迫切需求 准确捕获人类乳腺肿瘤的基因组异质性，并可用于模拟患者如何- 特定的亚克隆性肿瘤构筑影响治疗反应。此外，拟议的项目还提供 为申请者提供博士前培训平台，重点发展癌症方面的专业知识 建模和精确肿瘤学，与实验设计和分析相关的技能，精通计算 药物基因组学，以及从事转译癌症研究所需的专业技能。

PROJECT SUMMARY Breast cancer is the most common cancer among women worldwide, affecting one in eight women in the United States during their lifetime. Existing patient-derived models for breast cancer, including cell and organoid cultures and patient-derived xenografts, fail to recapitulate important aspects of breast tumorigenesis. Notably, these models are derived from late-stage tumors, undergo genetic drift and selection in culture, and are incompatible with high-throughput genetic and chemical screening. There is a clear need for more robust preclinical models of breast cancer that enhance translation of basic cancer research into meaningful improvements in patient outcomes. Human induced pluripotent stem cells (hiPSCs) are an appealing tool for modeling breast cancer because they provide a scalable source of patient-derived cells that retain molecular features of underlying cancer cells, including mutational burdens and drug sensitives. Additionally, as hiPSCs are clonally derived from single cells, multiple hiPSC lines from an individual tumor provide a tool to examine subclone-specific contributions to tumorigenesis and therapy response. However, no study to-date has examined the potential of hiPSCs to model breast tumorigenesis. This study aims to develop a novel preclinical model of breast cancer by generating hiPSC lines from human breast tumors and to use this model to examine patient and subclone-specific responses to poly (ADP-ribose) polymerase (PARP) inhibitors. PARP inhibitors target intrinsic DNA damage repair deficiencies in tumor cells and are a promising therapeutic for subsets of highly aggressive, BRCA-mutant triple negative breast cancers. In Aim 1, we will reprogram primary breast cancer cells to an hiPSC state, followed by differentiation into mammary epithelial cells (MECs) for in vitro disease modeling. We will use in-depth sequencing to determine the extent to which the genetic heterogeneity of hiPSC lines recapitulates the clonal heterogeneity of primary tumors. In Aim 2, we will use genetically distinct hiPSC-MEC lines derived from a BRCA mutant breast tumor to examine subclonal differences in DNA damage repair proficiency, intrinsic sensitivity to PARP inhibitors, and propensity to acquire PARP inhibitor resistance. Subclone-specific mechanisms of PARP inhibitor resistance will be mechanistically validated using gene editing in this model. Completion of these aims will fill a critical need for novel in vitro models of breast cancer that accurately capture the genomic heterogeneity of human breast tumors and can be used to model how patient- specific subclonal tumor architecture influences response to therapy. Additionally, the proposed project provides a platform for the applicant’s predoctoral training, with a focus on developing expertise in cancer disease modeling and precision oncology, skills related to experimental design and analysis, proficiency in computational pharmacogenomics, and the professional skills required to pursue a career in translational cancer research.