Research Project 2: Identify and validate efficacious therapies for metastatic breast cancer

项目概要-项目2 我们假设，患者来源的模型可用于确定更有效的治疗转移性乳腺癌， 可以使用分子生物标志物预测的癌症，并且这些模型可以用于准确预测 并询问治疗抗性。我们将使用大量的PDX、类器官和组织切片培养 根据不同治疗阶段的患者建立的模型（包括纵向样本和 原发性：来自同一患者的转移性对）以研究新疗法。我们将利用我们新的，非常 从PDX（PDxO）分离的肿瘤类器官中NCI-IND药物反应的广泛数据集：我们筛选了 近100个PDxO模型，包括所有乳腺癌亚型，每个模型有40-50种药物。有效的“先导”疗法 已经在PDX模型中进行了体内验证;类器官和PDX数据之间具有高度一致性。组学 所有模型的数据都是可用的，并被用于识别与应答者相关的生物标志物， 每种电极导线治疗的无应答者。我们将把新的计算算法应用于这个丰富的数据集， 优先考虑可以与标准治疗联合使用或相互联合使用的药物。的 本研究的主要结果将是转移性乳腺癌的新NCI-IND治疗方案， 患者衍生模型作为临床前工具，用于（1）评估许多药物和药物组合的疗效 在大量不同患者的肿瘤模型中，和（2）鉴定应答和非应答的生物标志物。 为新的治疗方法，以更好地为临床试验设计提供信息。

PROJECT SUMMARY – PROJECT 2 We hypothesize that patient-derived models can be used to identify more effective treatments for metastatic breast cancer that can be predicted using molecular biomarkers, and that these models can be used to accurately predict and interrogate treatment resistance. We will use our large collection of PDX, organoid, and tissue slice culture models established from patients at different stages of treatment (including longitudinal samples and primary:metastatic pairs from the same patient) to investigate new therapies. We will capitalize on our new, very extensive dataset of NCI-IND drug responses in tumor organoids isolated from PDX (PDxOs): We have screened nearly 100 PDxO models comprising all breast cancer subtypes, each with 40-50 drugs. Effective “lead” therapies have been validated in vivo in PDX models; there is high concordance between organoid and PDX data. Omics data are available on all of the models, and are being used to identify biomarkers associated with responders or non-responders, for each lead therapy. We will apply new computational algorithms to this rich dataset to prioritize drugs that can be used in combination with standard therapy, or in combination with each other. The primary outcome of this study will be new NCI-IND treatment regimens for metastatic breast cancer, using patient-derived models as a preclinical tool to (1) evaluate the efficacy of many drugs and drug combinations across a large number of different patients’ tumor models, and (2) identify the biomarkers of responding and non- responding breast cancers for new therapies, in order to better inform clinical trial design.