SMARTCORE Technology: Using AI and Patient Tissue to Identify Potential Cancer Therapies for Ultra-rare Cancers

SMARTCORE 技术：利用人工智能和患者组织来识别极罕见癌症的潜在癌症疗法

• 批准号: 10796286
• 负责人: Taran Singh Gujral
• 金额: $ 250万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10796286
• 关键词: SMARTCORE; Technology; Using; AI; Patient

Project Summary Our proposal presents a new approach, called SmartCore, to tackle the challenge of finding practical solutions for ultra-rare cancers. We aim to use an AI-driven drug screening platform designed to test primary human tumor tissue. Our strategy is to identify and repurpose drugs that exhibit activity against the cancer of interest. We circumvent the latency of developing organoids or patient-derived xenograft models for drug screening by utilizing intact tumors as organotypic cultures. Using a machine-learning algorithm, we can predict tumor sensitivity to a panel of ~4,000 drugs, including ~1800 FDA-approved drugs, by inputting responses to a set of computationally selected 254 compounds. Our long-term goal is to create a robust drug and target discovery method for individual cancers, irrespective of rarity, that directly links to clinical application, thereby addressing a critical ‘bench-to-bedside’ gap in personalized and precision oncology. To realize our goal, we intend to expand the capability of our platform to make use of clinical needle biopsies. This will vastly increase the utility of our SmartCore technology to analyze biopsy samples from any tumor that can be shipped to our processing facility within 24 hrs. This proposal will demonstrate the proof-of-concept of our SmartCore platform in an ultra-rare condition, fibrolamellar cancer of the liver, with two specific aims. Aim 1 is to discover therapeutics for fibrolamellar cancer using AI-based chemical screening. Here, we will establish technical parameters for optimal performance of our AI-based screening platform using independent FLC cohorts obtained from multiple sources, validate the top ‘hits’ using established patient-derived xenograft models from three independent human FLCs, and deduce signaling networks and proteins targeted by the candidate drugs, highlighting molecular pathways important for the survival of FLC. Aim 2 is to develop an AI-based chemical screening approach using needle biopsies. To broaden the impact of our technology, we will modify our AI-based screening to accommodate 18- gauge core needle biopsies routinely performed in clinical practice. We will curate a set of <40 drugs for FLC- specific testing as the basis for drug prediction using our deep neural network algorithm, test the accuracy of this approach by comparing needle biopsies with larger tissue slices in our FLC population and optimize pre-analytic conditions to yield reproducible results. If successful, our technology will make use of needle core biopsies, be agnostic to the underlying molecular derangement, screen a large collection of compounds using deep neural network algorithms, and require a short turnaround time of one week from start to finish; all of which are attributes of an ideal test that overcomes the Achilles heel of personalized oncology.

项目摘要 我们的提案提出了一种称为SmartCore的新方法，以应对寻找实际解决方案的挑战 治疗罕见癌症我们的目标是使用人工智能驱动的药物筛选平台，旨在测试原发性人类肿瘤 组织.我们的策略是识别和重新利用对感兴趣的癌症表现出活性的药物。我们 通过以下方式规避开发用于药物筛选的类器官或患者来源的异种移植模型的潜伏期： 利用完整的肿瘤作为器官型培养物。使用机器学习算法，我们可以预测肿瘤 通过输入对一组约4，000种药物（包括约1800种FDA批准的药物）的反应， 计算选择的254种化合物。我们的长期目标是创造一个强大的药物和目标发现 针对个体癌症的方法，无论罕见与否，都直接与临床应用联系起来，从而解决 个性化和精确肿瘤学中的关键“实验室到床边”差距。为了实现我们的目标，我们打算扩大 我们的平台能够利用临床穿刺活检。这将大大提高我们的实用性。 SmartCore技术用于分析任何肿瘤的活检样本，这些样本可以运送到我们的处理设施 24小时内。该提案将以一种极其罕见的方式展示我们SmartCore平台的概念验证。 条件，肝纤维板层癌，有两个具体的目标。目的1是发现治疗方法， 纤维板层癌使用人工智能为基础的化学筛选。在这里，我们将建立最佳的技术参数 我们基于AI的筛查平台使用从多个来源获得的独立FLC队列的性能， 使用来自三个独立的人FLC的已建立的患者来源的异种移植物模型验证最高“命中”， 并推导出候选药物靶向的信号网络和蛋白质，突出分子途径 这对刚果解放阵线的生存至关重要。目标2是开发一种基于人工智能的化学筛选方法， 活组织检查为了扩大我们技术的影响，我们将修改我们基于人工智能的筛选，以适应18- 在临床实践中常规进行的标准芯针活检。我们将为FLC策划一套<40种药物- 具体测试作为药物预测的基础，使用我们的深度神经网络算法，测试这种准确性。 在我们的FLC人群中，通过比较针吸活检与较大组织切片的方法， 条件以产生可再现的结果。如果成功，我们的技术将利用针芯活检， 不可知的潜在的分子紊乱，筛选大量的化合物，使用深层神经网络 网络算法，并且从开始到结束需要一周的短周转时间;所有这些都是属性 一个理想的测试，克服了个性化肿瘤学的致命弱点。