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

项目摘要 乳腺癌是全世界妇女中最常见的癌症， 美国在其一生中现有的乳腺癌患者衍生模型，包括细胞和 类器官培养物和患者来源的异种移植物不能概括乳腺肿瘤发生的重要方面。 值得注意的是，这些模型来源于晚期肿瘤，在培养中经历遗传漂变和选择，并且是 与高通量遗传和化学筛选不相容。显然需要更强大的 乳腺癌的临床前模型，增强了基础癌症研究转化为有意义的 改善患者的预后。人诱导多能干细胞（hiPSC）是一种有吸引力的工具， 因为它们提供了可扩展的患者来源的细胞来源， 潜在癌细胞的特征，包括突变负担和药物敏感性。此外，作为hiPSC 克隆来源于单细胞，来自单个肿瘤的多个hiPSC系提供了检查 亚克隆特异性对肿瘤发生和治疗反应的贡献。然而，迄今为止，还没有研究 hiPSC模拟乳腺肿瘤发生的潜力。本研究旨在开发一种新的临床前模型 通过从人乳腺肿瘤中产生hiPSC系来研究乳腺癌，并使用该模型来检查 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 抑制剂靶向肿瘤细胞中固有的DNA损伤修复缺陷，是一种有前途的治疗肿瘤的方法。 高度侵袭性BRCA突变型三阴性乳腺癌的亚群。在目标1中，我们将重新编程初级 将乳腺癌细胞分化为hiPSC状态，然后分化为乳腺上皮细胞（MEC），用于体外培养。 疾病建模我们将使用深度测序来确定遗传异质性的程度 hiPSC系的克隆异质性概括了原发性肿瘤的克隆异质性。在目标2中，我们将使用遗传上不同的 源自BRCA突变乳腺肿瘤的hiPSC-MEC系，以检查DNA损伤的亚克隆差异 修复能力、对PARP抑制剂的内在敏感性和获得PARP抑制剂抗性的倾向。 PARP抑制剂抗性的亚克隆特异性机制将使用基因编辑进行机械验证 in this model模型.这些目标的实现将满足对新型乳腺癌体外模型的迫切需求， 准确地捕捉人类乳腺肿瘤的基因组异质性，并可用于模拟患者 特异性亚克隆肿瘤结构影响对治疗的反应。此外，该项目还提供 申请人的博士前培训平台，重点是发展癌症疾病的专业知识 建模和精确肿瘤学，与实验设计和分析相关的技能，熟练掌握计算 药物基因组学，以及从事转化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.