I-Corps: Implementation of Genetic Algorithms for Personalized Chemosensitivity Testing for Cancer Patients

I-Corps：实施遗传算法对癌症患者进行个性化化学敏感性测试

• 批准号: 1265265
• 负责人: M. Umit Uyar
• 金额: $ 5万
• 依托单位: CUNY City College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1265265&HistoricalAwards=false
• 关键词: Corps; Implementation; Genetic; Algorithms; Personalized

Researchers are investigating a direct approach to evaluate chemotherapy effectiveness, where tumor tissue that has been extracted from a patient is exposed to clinically relevant treatments in an environment that closely mimics the human body. Through monitoring and analysis methods, researchers are able to determine which chemotherapies appear to be most effective in treating a patient?s cancer. These results can be used synergistically with current gene expression methods to improve predicting patient response to all therapies including chemotherapy. In the proposed process, researchers provide a mechanism for determining the efficacy of a cancer treatment directly on living biopsied tissue. This method is not dependent on historical statistical correlations to predict patient treatment and outcome. Researchers propose to predict therapeutic patient response more accurately by precisely measuring drug efficacy directly on cultured tumor tissue. This method is designed to be a high throughput real-time assay using genetic algorithms for automated data capture. Currently, clinicians choose chemotherapy treatment options for their patients according to the National Comprehensive Cancer Network (NCCN) guidelines and historical clinical outcome results for patients with similar types of breast cancer. Historically this approach has had limited success since less than 10% of breast cancer patients receiving chemotherapy are thought to exhibit absolute improvement. Researchers aim to provide oncologists and pathologists with information to better determine the best FDA approved chemotherapeutic option for their patients. The proposed method, if successful, will be provided as a service requested by an oncologist and pathologist, similar to other current forms of clinical diagnostic testing, such as medical imaging, blood work, etc. This method has the potential to be expanded to all other solid tumor cancers as well as being adapted to testing novel therapeutic agents.

研究人员正在研究一种直接评估化疗效果的方法，即从患者身上提取的肿瘤组织在接近模拟人体的环境中接受临床相关的治疗。通过监测和分析方法，研究人员能够确定哪些化疗方法似乎对治疗患者最有效？S癌症。这些结果可以与目前的基因表达方法协同使用，以提高对包括化疗在内的所有治疗的患者反应的预测。在拟议的过程中，研究人员提供了一种机制，用于直接在活组织上确定癌症治疗的有效性。这种方法不依赖于历史的统计相关性来预测患者的治疗和结果。研究人员提出，通过直接在培养的肿瘤组织上精确测量药物疗效，可以更准确地预测治疗患者的反应。该方法是一种使用遗传算法进行自动数据捕获的高通量实时分析方法。目前，临床医生根据国家综合癌症网络(NCCN)指南和类似类型乳腺癌患者的历史临床结果为他们的患者选择化疗方案。从历史上看，这种方法的成功是有限的，因为只有不到10%的接受化疗的乳腺癌患者被认为表现出绝对的改善。研究人员的目标是为肿瘤学家和病理学家提供信息，以更好地为他们的患者确定FDA批准的最佳化疗方案。如果成功，建议的方法将作为肿瘤学家和病理学家的要求提供服务，类似于其他当前形式的临床诊断测试，如医学成像、血液检查等。该方法有可能扩展到所有其他实体肿瘤癌症，并适用于测试新的治疗药物。