PFI-RP: Precision diagnostics for personalized cancer care: development of new drugs and selection of treatment

这一创新-研究伙伴关系(PFI-RP)项目的更广泛影响/商业潜力是通过使用创新的人工智能(AI)技术来选择为每个患者量身定做的最佳治疗方案，从而延长患者的生命，从而使癌症患者受益。尽管出现了新的治疗方法，癌症仍然是美国第二大死亡原因。目前缺乏临床工具来选择适合个别癌症患者的最佳治疗方案。为了满足这些未得到满足的临床需求，该项目将采用成熟的尖端人工智能/大数据技术，为个别患者确定最佳治疗方案和治疗方案，以改善癌症预后。在成功完成这项技术的开发后，该解决方案将通过避免最初的治疗失败来降低医疗成本。该项目还将促进跨学科的研究生研究，并在创新和创业方面教育未来的领导者。这项工作如果成功，将在阿巴拉契亚地区带来产业投资和劳动力发展，缩小癌症发病率和死亡率较高的西弗吉尼亚州的健康差距。拟议的项目基于尖端人工智能技术，在计算效率、可扩展性和准确性方面具有竞争优势，与现有的其他诊断基因测试和新药开发方法相比。利用建议的技术，可以开发新的原型基因分析并为其申请专利，用于乳腺癌、卵巢癌、结肠癌和肺癌患者的诊断、预后和治疗效益预测。这些基因分析将有能力识别肿瘤复发和转移的高危个体，并选择对患者最有利的药物治疗。拟议的人工智能管道最初将专注于发现治疗肺癌的新药选择，但可以扩大到其他类型的癌症。该项目有三个目标：1)建立与多个创新网络/中心的联系，加强学术界和产业界的合作，2)加快以市场为导向的原型产品开发，3)开发一个新的跨计算机科学、肿瘤学、生物医学研究和创业的多学科研究生培养计划。为了克服商业化中的技术障碍，该项目将利用多模式患者数据，使用几家精密制药公司的制造平台来1)验证原型基因分析，以实现最佳治疗选择和更好的癌症结果，2)建立基于人工智能的技术，为先前治疗失败的癌症患者全面发现新药和/或重新定位食品和药物管理局(FDA)批准的药物。该项目由创新伙伴计划和已建立的刺激竞争研究计划(EPSCoR)联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Partnerships for Innovation – Research Partnerships (PFI-RP) project is to benefit cancer patients by using AN innovative artificial intelligence (AI) technology to select optimal treatment options tailored to each individual patient with the goal of prolonging patient life. Despite emerging new therapies, cancer remains the second leading cause of death in the United States. Clinical tools to select optimal treatment options suitable for individual cancer patients are currently lacking. To meet these unmet clinical needs, this project will employ an established cutting-edge AI/big data technology to determine the best treatments and therapeutic options for individual patients to improve cancer outcomes. Upon successful completion of the development of this technology, the solution will reduce healthcare costs by avoiding initial treatment failures. This project will also foster transdisciplinary graduate research and educate future leaders in innovation and entrepreneurship. This work, if success, will result in industrial investments and workforce development in Appalachia, reducing health disparities in West Virginia, which has high cancer incidence and mortality rates.The proposed project is based on leading-edge AI technology with competitive advantages in computational efficiency, scalability, and accuracy over existing other methods for developing diagnostic gene tests and new drugs. Using the proposed technology, new prototype gene assays can be developed and patented for diagnosis, prognosis, and prediction of therapeutic benefits in breast, ovarian, colon, and lung cancer patients. These gene assays will have the ability to identify individuals who are at high-risk for tumor recurrence and metastasis and select the drug treatments that will benefit the patients the most. The proposed AI pipeline will initially focus on the discovery of new drug options for treating lung cancer but can be expanded to other types of cancers. This project has three goals: 1) to establish the connection with multiple innovation networks/hubs, enhancing academia-industry collaboration, 2) to accelerate market-oriented development of prototype products, and 3) to develop a novel multidisciplinary graduate training program across computer science, oncology, biomedical research, and entrepreneurship. To overcome the technical hurdles in commercialization, this project will leverage multi-modal patient data using several precision medicine companies' manufacturing platforms to 1) validate the prototype gene assays for optimal treatment selection and better cancer outcomes, and 2) establish an AI-based technology for comprehensively discovering new drugs and/or repositioning Food and Drug Administration (FDA)-approved drugs for cancer patients with failed prior therapy.This project is jointly funded by Partnerships for Innovation Program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.