Engineering personalized micro-tumor ecosystems

设计个性化微肿瘤生态系统

• 批准号: 9756346
• 负责人: Ali Khademhosseini
• 金额: $ 77.55万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-21 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9756346
• 关键词: 3-Dimensional; Accounting; Achievement; Address; Algorithms; Antibiotics; Antineoplastic Agents; Area; Autologous; Biocompatible Materials; Biological; Biological Assay; Biological Markers; Biomimetics; Biopsy; Bioreactors; Cancer Patient; Cause of Death; Cessation of life; Cetuximab; Characteristics; Clinical; Clinical Data; Colorectal Cancer; Complex; Consensus; Data; Development; Dimensions; Drug Combinations; Ecosystem; Engineering; Future; Genetic screening method; Glucose; Head and Neck Squamous Cell Carcinoma; Heterogeneity; Human; Hydrogels; In Vitro; Individual; KRAS2 gene; Lab-On-A-Chips; Liquid substance; Malignant Neoplasms; Metabolic; Microfluidics; Miniaturization; Modeling; Molecular Profiling; Nature; Needle biopsy procedure; Neoadjuvant Therapy; Outcome; Parents; Pathologic; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Phenotype; Physiological; Prediction of Response to Therapy; Proteins; Publishing; Quality of life; Regimen; Rest; Screening for cancer; Serum; Specificity; Supplementation; System; Testing; Therapeutic; Therapeutic Agents; Training; Translating; Treatment Cost; Treatment Protocols; Tumor Tissue; Tumor stage; Tumor-Derived; United States; Variant; Work; Xenograft Model; base; cancer therapy; chemotherapy; cytokine; design; effective therapy; fluid flow; genetic profiling; humanized mouse; improved; in vivo; machine learning algorithm; malignant breast neoplasm; miniaturize; novel; outcome prediction; patient response; personalized medicine; personalized screening; precision medicine; prevent; protein B; response; side effect; survival outcome; triple-negative invasive breast carcinoma; tumor; tumor heterogeneity; tumor microenvironment; tumor xenograft

Abstract Cancer is one of the leading causes of death in the United States, accounting for near 1 in every 4 deaths. However, despite the recent development of subtype-specific personalized therapy based on achievements in the fields of molecular and genetic profiling, many cancer treatments still have low efficacy which mostly arise from the limited ability to predict the patient tumor responses to therapeutic agents. The major reason that current therapeutics often cannot translate into a successful clinical outcomes is because of the complex tumor microenvironment and heterogeneity that limit the predictive power of the biomarker-guided strategies for chemotherapy. Therefore, the successful engineering of personalized three-dimensional (3D) tumor ecosystem that can recapitulate the tumor microenvironment and heterogeneity in vitro is strongly desired to accurately predict patients’ responses to anti-cancer drugs and thus further improve patient outcome. Here we propose to develop a personalized breast-cancer-ecosystem-on-a-chip platform for personalized screening of cancer chemotherapeutics with high accuracy by utilizing patient-derived tumor explant, defined tumor grade-matched biomaterial matrices and autologous patient serum to mimic patient-specific tumor hallmarks. The proposed cancer-ecosystem-on-a-chip will also be tightly regulated under physiological fluid dynamics. In this project, we have hypothesized that 1) the use of tumor explant will embrace the critical components of the tumor heterogeneity of the patient, 2) the combination of defined tumor grade-matched matrix, autologous patient serum, and a microfluidic bioreactor will prevent the phenotype alteration of the tumor explant, and 3) the integration of a machine-learning algorithm with the cancer-ecosystem-on-a-chip platform will provide more accurate, unbiased prediction of the patient responses to chemotherapeutics based on the data gathered from the engineered tumor model. Our preliminary results show that the combination of tumor explant culture and tumor-derived matrix constituents had predicted therapeutic responses with 100% sensitivity. Our preliminary results show high specificity throughout a range of cancers including breast cancer, colorectal cancer, and head and neck squamous cell carcinoma, and thus the findings can have broad applications, and can emerge as a paradigm shift in the management of cancer.

摘要 癌症是美国的主要死亡原因之一，占死亡人数的近1/4。 然而，尽管最近亚型特异性个体化治疗的发展基于 在分子和遗传学领域，许多癌症治疗方法的疗效仍然很低，主要是由于 从有限的能力来预测患者肿瘤对治疗剂的反应。当前的主要原因是 治疗往往不能转化为成功的临床结果是因为复杂的肿瘤 限制生物标记物导向策略预测能力的微环境和异质性 化疗。因此，个性化三维(3D)肿瘤生态系统的成功工程 能够准确反映肿瘤微环境和体外异质性的研究是人们迫切需要的。 预测患者对抗癌药物的反应，从而进一步改善患者的预后。在此，我们建议 开发个人化乳腺癌芯片生态系统平台，用于个人化癌症筛查 利用患者来源的肿瘤组织块进行高精度的化疗，确定的肿瘤分级匹配 生物材料基质和自体患者血清，以模拟患者特定的肿瘤特征。建议数 芯片上的癌症生态系统也将在生理流体动力学下受到严格调控。在这个项目中，我们 假设1)使用肿瘤外植体将包含肿瘤的关键成分 患者的异质性，2)确定的肿瘤分级匹配矩阵、自体患者的组合 血清和微流控生物反应器将防止肿瘤外植体的表型改变，以及3) 机器学习算法与芯片上癌症生态系统平台的集成将提供更多 根据从以下来源收集的数据，准确、公正地预测患者对化疗药物的反应 经过改造的肿瘤模型。我们的初步结果表明，肿瘤外植体培养和 肿瘤衍生基质成分预测治疗反应的敏感度为100%。我们的预赛 结果显示，在包括乳腺癌、结直肠癌和头部在内的一系列癌症中具有高度的特异性。 和颈部鳞状细胞癌，因此这一发现可以有广泛的应用，并可以作为一种 癌症管理的范式转变。