Rational design of drug combinations to target chemotherapy resistance in high-grade serous ovarian cancer via metabolomic profiling of patient-derived organoids

通过患者来源类器官的代谢组学分析，合理设计药物组合，以针对高级别浆液性卵巢癌的化疗耐药性

• 批准号: 10401850
• 负责人: Jan Krumsiek
• 金额: $ 8.48万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10401850
• 关键词: Affect; Algorithms; Biochemical Pathway; Cancer Etiology; Cancer Model; Cancer Patient; Carboplatin; Cells; Cessation of life; Chemoresistance; Clinical; Clinical Trials; Collaborations; Combined Modality Therapy; Computational algorithm; Computer Models; Data; Disease; Disease Resistance; Drug Combinations; Drug resistance; Failure; Fresh Tissue; Future; Genes; In Vitro; Intervention; Link; Malignant neoplasm of ovary; Maps; Metabolic; Metabolic Pathway; Metabolism; Modeling; Molecular; Mutation; Nature; Organoids; Paclitaxel; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phase; Pilot Projects; Primary Neoplasm; Process; Refractory; Regulator Genes; Relapse; Resistance; Resistance profile; Running; Sampling; Serous; Signal Pathway; Signaling Protein; Statistical Data Interpretation; Technology; Testing; Therapeutic; Tissue Sample; Tissues; Translations; Tumor Burden; Tumor Tissue; Woman; base; cancer cell; cancer subtypes; cancer therapy; chemotherapeutic agent; chemotherapy; cohort; design; drug candidate; drug testing; improved; in silico; in vitro Model; in vivo evaluation; metabolic profile; metabolomics; multidisciplinary; neoplastic cell; novel; patient response; rational design; resistance mechanism; response; tumor

Project Summary/Abstract This resistant disease rates women, metabolomics resistance, of algorithm To regulatory of organoids. identified therapies project aims to develop a novel, interdisciplinary framework for the designed reprogramming of drug- tumors towards a responsive state. This pilot will focus on high-grade serous ovarian cancer, a with up to 20% o patients being refractory to primary treatment with carboplatin, and 80% relapse of initially responsive patients. We will obtain primary tumor tissue rom 12 refractory and 12 responsive grow patient-derived organoids as a personalized in vitro model of the patients' tumors, and perform profiling on both tissue samples and organoids. This will allow us to identify metabolic states of which will pinpoint the molecular escape mechanisms resistant tumors employ to evade the action the chemotherapeutic agent. In the second phase of the project, we wil l develop a novel computational that predicts which other drug is most likely to reprogram the tumor into a carboplatin-sensitive state. this end, we will map the metabolic profiles of resistance onto an integrated metabolic, signaling and pathway map to then identify drugs whose targets are upstream of the desired effect. In the last part the project, we will choose the highest-scoring predictions and validate them experimentally on the Specifically, we expect to render resistant organoids responsive by pretreating them with the drugs. If successful, this project will establish a new paradigm for the rational design of combination in refractory tumors. f f

项目 总结/摘要 这 耐 疾病 率 女人， 代谢组 电阻， 的 算法 到 监管 的 类器官 识别 疗法 该项目旨在开发一种新型的跨学科框架，用于药物的设计性重新编程- 肿瘤进入反应状态。该试点将重点关注高级别浆液性卵巢癌， 高达20%的患者对卡铂的初级治疗难治，80%的患者复发 最初有反应的病人。我们将从12例难治性和12例反应性患者中获得原发性肿瘤组织 培养患者来源的类器官作为患者肿瘤的个性化体外模型， 对组织样本和类器官进行分析。这将使我们能够识别代谢状态的 这将精确定位耐药肿瘤逃避作用的分子逃逸机制， 化疗剂。在项目的第二阶段，我们将开发一种新的计算 预测哪种药物最有可能将肿瘤重新编程到卡铂敏感状态。 为此，我们将把耐药的代谢谱映射到一个整合的代谢、信号和 然后识别其靶点位于所需效果上游的药物。最后一部分 在该项目中，我们将选择得分最高的预测，并在实验上验证它们。 具体地说，我们希望通过用以下物质预处理它们来使抗性类器官产生反应： 毒品如果成功的话，这个项目将为组合的合理设计建立一个新的范例 在难治性肿瘤中。 F F