Development of personalized ex vivo predictive technology for rapidly matching patient tumors with chemotherapy regimens before treatment.

项目摘要/摘要 胰腺导管腺癌(PDAC)是最致命的癌症之一，5年生存率为9%， 据估计，到2030年，每年将有6万人死亡。PDAC通常在晚期诊断，从而排除 大多数患者需要手术切除。虽然新的系统治疗方案提高了存活率，但可用性 在没有工具从这些选项中选择最佳方案(在个性化的基础上)的情况下，创建了 临床决策中令人沮丧的悖论。由于缺乏个性化的预测工具，目前的标准 护理治疗策略的基础是预后因素，如年龄、分期、表现状况、血清白蛋白、 等。有一个关键的、迫切的和尚未得到满足的需求，即开发能够识别最佳系统的预测工具 在个体化的基础上，制定治疗方案并排除无效的选择，以改善 提高生活质量，减少过度治疗。CerFlux，Inc.正在开发这种低成本的预测技术 和快速个性化肿瘤学疗效测试(POET)，将每个患者与正确的治疗相匹配-之前 治疗-在短期内改变胰腺癌的治疗方法，并改变 世界各地的患者和提供者。我们的个性化医疗方法是独一无二的，并得到进一步增强 由CerFlux，Inc.和詹姆斯综合癌症中心之间的商业-学术合作 在俄亥俄州立大学。拟议的项目将建立在我们团队最近的工作基础上，包括一项专利 (US 10,114,010B1)药物转运和胰腺微组织肿瘤的仿生体外平台 模特们。这项建议的商业目标是确定在个性化治疗中使用PEET的最佳实践。 我们的假设是，在PEAT中观察到的对治疗的反应将与在 相应的病人。我们的目标是事先预测每个患者的有效和无效治疗。 开始治疗。我们提出以下目标，以达致我们的目标： 目的1：使用人PDAC细胞系异种移植来校准和优化PEET用于评价治疗 随后对患者组织进行测试。 目的2：在个体化的基础上评估各种系统治疗药物在PET中的疗效，以建立 在个性化治疗中使用PEET的协议和最佳实践。 我们预计CerFlux，Inc.和James之间将继续进行实质性的商业和学术合作 俄亥俄州立大学的综合癌症中心，包括将机器学习与 根据多种因素得出一个“诗人分数”--一种个性化的量化效能分数。数据 来自PEAT和PEET的分数将帮助临床团队将个别患者的治疗排在第一位 药物输注。如果成功，这项由SBIR驱动的研究有可能改变胰腺癌的治疗 并在短期内在世界各地产生积极影响。

Project Summary/Abstract Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest cancers with <9% five-year survival rate and an estimated 60,000 deaths/year by 2030. PDAC is often diagnosed at an advanced stage thereby precluding surgical resection for most patients. While new systemic therapy regimens have improved survival, availability of multiple options, without tools to select an optimal regimen from these (on an individualized basis), has created a frustrating paradox in clinical decision-making. Due to a lack of personalized predictive tools, current standard of care treatment strategy is based on prognostic factors such as age, stage, performance status, serum albumin, etc. There is a critical, urgent and unmet need to develop predictive tools that can identify optimal systemic therapy regimens and eliminate from consideration ineffective options, on an individualized basis, to improve quality of life and reduce overtreatment. CerFlux, Inc. is developing such predictive technology with its low-cost and rapid Personalized Oncology Efficacy Test (POET) to match each patient with the right treatment – before treatment – to transform pancreatic cancer treatment in the near-term and make a difference in the lives of patients and providers around the world. Our personalized medicine approach is unique and further enhanced by a commercial-academic collaboration between CerFlux, Inc. and the James Comprehensive Cancer Center at the Ohio State University. The proposed project will build on recent work by our team including a patented (US 10,114,010B1) biomimetic in vitro platform for pharmacological transport and pancreatic microtissue tumor models. The commercial goal of this proposal is to identify best practices for using POET in personalized therapy. Our hypothesis is that response to treatment observed in POET will approximate the response in the corresponding patient. Our objective is to predict both effective and ineffective treatments for each patient prior to initiating treatment. We propose the following aims to achieve our objective: Aim 1: Calibrate and optimize POET for evaluating therapeutics using human PDAC cell-line xenografts for subsequent testing with patient tissue. Aim 2: Evaluate efficacy of various systemic therapy agents in POET on an individualized basis to establish protocols and best practices for using POET in personalized therapy. We envision substantial continuing commercial-academic collaboration between CerFlux, Inc. and the James Comprehensive Cancer Center at the Ohio State University including the integration of machine learning to derive a “POET Score” – a personalized quantitative efficacy score – based on a combination of factors. Data from POET and the POET Score will help clinical teams rank treatments for individual patients before the first drug infusion. If successful, this SBIR-driven study has the potential to transform pancreatic cancer treatment in the near-term and make a positive impact around the world.