Collaborative Research: CPS: Medium: A CPS approach to tumor immunomodulation; sensing, analysis, and control to prime tumors to immunotherapy

合作研究：CPS：中：肿瘤免疫调节的 CPS 方法；

• 批准号: 2038851
• 负责人: Rahul Sheth
• 金额: $ 44.84万
• 依托单位: University of Texas, M.D. Anderson Cancer Center
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038851&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; approach

Cancer remains the second leading cause of death in the US. Immunotherapy is a cancer treatment that aims to help the body’s immune system fight cancer. While excellent responses have been observed for a large number of patients with varying disease types, a considerably larger number of patients have received little to no benefit from immunotherapy. This varied outcome has been attributed to the highly heterogeneous physical and physiological profile within and around tumors that suppress the immune system’s response. Various physical, chemical, and biological treatment modalities are under investigation for altering the tumor environment from a state where immune effects are suppressed, to one supportive of an anti-tumor immune response. However, these approaches are hampered by the lack of techniques for monitoring the tumor state in response to candidate treatments. Technologies that enable continuous monitoring of the tumor’s immune state, and thereby guide precise delivery of interventions to drive tumors to an immunostimulatory state, offer the promise of unlocking the full potential of immunotherapies. A cyber-physical systems (CPS) perspective is uniquely suited to addressing this challenge, treating the tumor as an “in body CPS” with the development of sensors and analytical techniques for longitudinal assessment of the tumor, coupled with co-located methods for delivering physical/chemical treatments for modulating the environment within the tumor towards an immunostimulatory state. If successfully developed and translated, the CPS framework for immunomodulation investigated in this project may ultimately guide selection and optimal delivery of priming interventions prior to immunotherapy delivery, determine when priming interventions have successfully modulated the tumor to an immunogenically favorable state, and for assessing treatment response. The investigator team will develop a graduate-level course on biomedical cyber-physical systems along with modules on implantable biomedical sensors for undergraduate courses. Further, this project will provide summer research opportunities for students from under-represented groups via the Pathways to STEM program. This project will investigate a CPS framework for immunomodulation of the tumor microenvironment (TME), integrating: (1) a unique 3D micro-array sensor and treatment (MIST) device consisting of a sensing/actuation platform for longitudinal sensing and control of physical and physiological parameters within the TME; (2) novel model-informed machine learning techniques for determining tumor immune state from TME physical/physiologic characteristics; and (3) model-guided therapy via the MIST device for driving the TME to an immunostimulatory state. Advanced 3D fabrication technology will provide implantable micromachined multimodal sensing devices to enable longitudinal in vivo sensing of TME parameters such as tissue oxygenation, pH, pressure, and metabolism, and co-located treatment on a single device. Data gathered from implantable sensors will be fused with computational models of biophysical parameters informed by tumor-specific vasculature maps using a graph neural tensor completion approach. The novel hybrid machine learning approach for data imputation and fusion will systematically incorporate uncertainties and provide the basis to infer the immune state of a tumor, validated against gold-standard molecular biomarkers of immune state in experimental small animals. A graph-based clustering approach integrated with a recurrent neural network will be used for the prediction of tumor state changes. Finally, we will evaluate the efficacy of model-guided delivery of energy-based interventions to transform the TME to a pro-immunogenic state and the impact of these interventions on immunotherapy outcomes in small animals.This project is jointly funded by the Cyber-Physical 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.

癌症仍然是美国第二大死亡原因。免疫疗法是一种癌症治疗方法，旨在帮助人体的免疫系统对抗癌症。虽然在大量患有不同疾病类型的患者中观察到了良好的反应，但相当多的患者从免疫疗法中几乎没有获益或没有获益。这种不同的结果归因于肿瘤内部和周围高度异质的物理和生理特征，抑制了免疫系统的反应。正在研究各种物理、化学和生物治疗方式，以将肿瘤环境从免疫效应受到抑制的状态改变为支持抗肿瘤免疫反应的状态。然而，这些方法因缺乏监测候选治疗的肿瘤状态的技术而受到阻碍。能够持续监测肿瘤免疫状态，从而指导精确实施干预措施以驱动肿瘤进入免疫刺激状态的技术，有望释放免疫疗法的全部潜力。网络物理系统（CPS）视角特别适合应对这一挑战，通过开发用于纵向评估肿瘤的传感器和分析技术，结合用于提供物理/化学治疗以将肿瘤内的环境调节到免疫刺激状态的协同定位方法，将肿瘤视为“体内CPS”。如果成功开发和翻译，本项目研究的免疫调节 CPS 框架可能最终指导免疫治疗实施之前启动干预措施的选择和最佳实施，确定启动干预措施何时成功地将肿瘤调节到免疫原性有利状态，并评估治疗反应。研究团队将开发生物医学网络物理系统的研究生课程以及本科生课程的植入式生物医学传感器模块。此外，该项目还将通过“STEM 之路”项目为代表性不足群体的学生提供夏季研究机会。该项目将研究用于肿瘤微环境（TME）免疫调节的 CPS 框架，集成：（1）独特的 3D 微阵列传感器和治疗（MIST）设备，由传感/驱动平台组成，用于纵向传感和控制 TME 内的物理和生理参数； （2）基于模型的新型机器学习技术，用于根据 TME 物理/生理特征确定肿瘤免疫状态； (3) 通过 MIST 装置进行模型引导治疗，将 TME 驱动至免疫刺激状态。先进的 3D 制造技术将提供可植入的微机械多模态传感设备，以实现组织氧合、pH、压力和新陈代谢等 TME 参数的纵向体内传感，并在单个设备上进行协同定位治疗。从植入式传感器收集的数据将与使用图神经张量完成方法的肿瘤特异性脉管系统图提供的生物物理参数的计算模型融合。用于数据插补和融合的新型混合机器学习方法将系统地纳入不确定性，并为推断肿瘤的免疫状态提供基础，并根据实验小动物免疫状态的金标准分子生物标志物进行验证。与循环神经网络集成的基于图的聚类方法将用于预测肿瘤状态变化。最后，我们将评估模型引导的能量干预措施的有效性，以将 TME 转变为促免疫状态，以及这些干预措施对小动物免疫治疗结果的影响。该项目由网络物理计划和刺激竞争性研究既定计划 (EPSCoR) 共同资助。该奖项反映了 NSF 的法定使命，并通过评估认为值得支持 利用基金会的智力优势和更广泛的影响审查标准。