Engineering personalized micro-tumor ecosystems

设计个性化微肿瘤生态系统

基本信息

批准号：
9981696
负责人：
Ali Khademhosseini
金额：
$ 74.21万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
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/9981696
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 Drug Combinations Ecosystem Engineering Future 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 genetic testing humanized mouse improved in vivo large scale data machine learning algorithm malignant breast neoplasm miniaturize novel outcome prediction patient response personalized medicine personalized screening precision medicine prevent response side effect survival outcome triple-negative invasive breast carcinoma tumor tumor heterogeneity tumor microenvironment tumor xenograft

项目摘要

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.

抽象的癌症是美国的主要原因之一，占死亡人数的近四分之一。然而，尽管最近基于以下成就开发了亚型特异性个性化治疗：在分子和基因分析领域，许多癌症治疗方法仍然疗效较低，这主要是由于由于预测患者肿瘤对治疗药物的反应的能力有限。由于肿瘤复杂，通常无法转化为成功的治疗临床结果微环境和异质性限制了生物标志物指导策略的预测能力因此，化疗是个性化三维（3D）肿瘤生态系统的成功工程强烈希望能够准确地概括体外肿瘤微环境和异质性预测患者对抗癌药物的反应，从而进一步改善患者的治疗结果。开发个性化乳腺癌生态系统芯片平台，用于癌症的个性化筛查利用患者来源的肿瘤外植体进行高精度化疗，确定肿瘤等级匹配生物材料基质和自体患者血清来模拟患者特异性肿瘤标志。在这个项目中，芯片上的癌症生态系统也将受到生理流体动力学的严格调控。已经接受了这一点：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）定义的肿瘤分级匹配基质、自体患者的组合血清和微流体生物反应器将防止肿瘤外植体的表型改变，3）机器学习算法与癌症生态系统芯片平台的集成将提供更多根据收集的数据准确、公正地预测患者对化疗的反应我们的初步结果表明，肿瘤外植体培养与我们初步预测了肿瘤来源的基质成分的治疗反应灵敏度为 100%。结果显示，对包括乳腺癌、结直肠癌和头颅癌在内的一系列癌症具有高度特异性和颈部鳞状细胞癌，因此这些发现可以具有广泛的应用，并可以作为癌症治疗的范式转变。