Strategies for discerning chemotherapy response and resistance in ovarian cancer

辨别卵巢癌化疗反应和耐药的策略

• 批准号: 10512982
• 负责人: Wei Wei
• 金额: $ 51.04万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10512982
• 关键词: Aftercare; Agreement; Apoptosis; Bioenergetics; Biological Assay; Cancer Diagnostics; Cancer Etiology; Cancer Patient; Cells; Cessation of life; Chemoresistance; Clinic; Clinical; Clinical Trials; Cytometry; DNA Damage; Data; Dependence; Development; Drug Exposure; Drug Targeting; Drug Tolerance; Drug resistance; Epigenetic Process; Exposure to; Female; Genes; Genetic; Genome; Genomic medicine; Genomics; Goals; Guidelines; Histology; Immunofluorescence Immunologic; In Situ; Knowledge; Machine Learning; Malignant neoplasm of ovary; Measurement; Metabolic; Methods; Modeling; Molecular; Molecular Genetics; Neoplasm Metastasis; Operative Surgical Procedures; Patients; Pharmaceutical Preparations; Pharmacotherapy; Platinum; Platinum Compounds; Predisposition; Proliferation Marker; Recurrence; Refractory; Resistance; Resistance development; Sampling; Series; Serous; Signal Transduction; Slice; Stains; Therapeutic; Time; Tissues; Toxic effect; Treatment-related toxicity; Work; base; biomarker discovery; cancer genomics; cancer type; chemotherapy; clinical decision-making; diagnostic assay; drug development; experience; high risk; innovation; machine learning algorithm; machine learning model; metabolic phenotype; molecular marker; mortality; neoplastic cell; novel; novel therapeutics; patient response; precision medicine; predictive modeling; predictive signature; programs; prospective; responders and non-responders; response; single-cell RNA sequencing; standard care; transcriptome; transcriptomics; treatment response; tumor

Project Summary/Abstract Ovarian cancer is the fifth leading cause of cancer death among females. Most cases of ovarian cancer are high- grade serous ovarian cancer (HGSOC), which accounts for most ovarian cancer mortality. In spite of the increasing knowledge of this tumor type, the use of “old” DNA-damaging chemotherapeutic drugs remains the first option for ovarian cancer patients. The standard treatment of HGSOC involves de-bulking surgery followed by platinum-based chemotherapy with the aim to eliminate all the remaining micro-metastases. Unfortunately, a significant number of HGSOC patients do not respond or only partially respond to these treatments. A patient who does not respond to platinum chemotherapies has to go through the same treatment and experience the toxicity without meaningful benefit. Despite the rapid advances in cancer genomics and precision medicine in the last decade, what remains surprising is that no robust molecular biomarker that can foretell patient response to platinum agents has been identified in ovarian cancer, pointing to the need for the incorporation of functional metrics and early-stage molecular changes into the predictive framework. We hypothesize that early-stage molecular and functional alterations upon drug exposure that underlie how tumor cells orchestrate a response to the drug treatment are dictating longer-term therapy responses and thus critical to the development of predictive models. We further hypothesize that the drug susceptibility and bioenergetic dependency of the patient tumors are important functional readouts for such a prediction. We propose to integrate early-stage temporal genome, transcriptome, metabolic phenotypes, drug susceptibility, and histology information into machine learning models to discern a set of metrics most predictive for patient-specific responses to platinum chemotherapy in HGSOC. This is enabled by a strategic integration of a freshly prepared, precision-cut tumor slice culture model, an innovative single-cell on-chip metabolic cytometry assay, and a novel in situ spatial metabolic profiling assay on live tissues. Upon successful completion, this study could deliver a predictive diagnostic assay for identifying HGSOC patients with a higher risk of resistance to platinum chemotherapy, as well as patients who may benefit from such treatments, prior to the onset of therapy. These results will provide innovative molecular and functional information complementary to tumor genetics and other clinical factors for more informative clinical decision-making and help re-direct platinum-refractory patients to other therapeutic approaches or clinical trials of novel therapies before the treatment.

项目概要/摘要 卵巢癌是女性癌症死亡的第五大原因。大多数卵巢癌病例的发病率都很高 级别浆液性卵巢癌（HGSOC），占卵巢癌死亡率的大部分。尽管 随着人们对这种肿瘤类型的了解不断增加，使用“旧的”DNA 损伤性化疗药物仍然是最有效的治疗方法。 卵巢癌患者的首选。 HGSOC 的标准治疗包括随后进行减瘤手术 通过铂类化疗，旨在消除所有剩余的微转移。不幸的是，一个 大量 HGSOC 患者对这些治疗没有反应或仅部分反应。一位病人 对铂类化疗没有反应的人必须接受相同的治疗并经历 没有有意义的益处的毒性。尽管癌症基因组学和精准医学取得了快速进展 在过去的十年中，令人惊讶的是没有强大的分子生物标志物可以预测患者的反应 铂类药物已在卵巢癌中得到证实，表明需要结合功能性药物 指标和早期分子变化纳入预测框架。我们假设早期 药物暴露后分子和功能的改变是肿瘤细胞如何协调反应的基础 药物治疗决定了长期治疗反应，因此对于药物治疗的发展至关重要 预测模型。我们进一步假设患者的药物敏感性和生物能依赖性 肿瘤是此类预测的重要功能读数。我们建议整合早期时间 将基因组、转录组、代谢表型、药物敏感性和组织学信息输入机器 学习模型来辨别一组最能预测患者对铂类药物特定反应的指标 HGSOC 化疗。这是通过对新鲜准备的、精确切割的肿瘤进行战略整合来实现的 切片培养模型、创新的单细胞片上代谢细胞术测定以及新颖的原位空间分析 活体组织的代谢谱分析。成功完成后，这项研究可以提供预测 用于识别对铂类化疗耐药风险较高的 HGSOC 患者的诊断测定，如 以及在治疗开始前可能受益于此类治疗的患者。这些结果将提供 与肿瘤遗传学和其他临床因素互补的创新分子和功能信息 提供更多信息的临床决策，并帮助将铂类难治性患者重新引导至其他治疗 治疗前新疗法的方法或临床试验。