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

项目摘要--项目2 我们假设患者衍生的模型可以用来确定更有效的治疗转移性乳癌的方法。 可以使用分子生物标记物预测的癌症，这些模型可以用来准确预测 并询问治疗抵抗力。我们将使用我们的大量PDX、有机体和组织切片培养 从不同治疗阶段的患者建立的模型(包括纵向样本和 初级：来自同一患者的转移对)，以研究新的治疗方法。我们将利用我们的新的，非常 从PDX分离的肿瘤有机物(PDxOS)中NCI-IND药物反应的广泛数据集：我们已经筛选 包括所有乳腺癌亚型的近100个PDxO模型，每个模型包含40-50种药物。有效的“铅”疗法 已经在PDX模型中得到了活体验证；有机物和PDX数据之间有高度的一致性。组学 所有模型上都有数据，并用于识别与应答者或 无应答者，对于每种铅治疗。我们将对这个丰富的数据集应用新的计算算法 优先考虑可以与标准疗法或相互结合使用的药物。这个 这项研究的主要成果将是转移性乳腺癌的新的NCI-IND治疗方案，使用 患者衍生模型作为临床前工具用于(1)评估多种药物和药物组合的疗效 通过大量不同患者的肿瘤模型，以及(2)确定有反应和无反应的生物标志物 回应乳腺癌的新疗法，以便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.