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

癌症仍然是美国第二大死因。免疫疗法是一种旨在帮助人体免疫系统对抗癌症的癌症治疗方法。虽然对许多不同疾病类型的患者已经观察到良好的反应，但相当多的患者从免疫治疗中几乎没有获益。这种不同的结果归因于肿瘤内部和周围高度异质性的物理和生理特征，这些特征抑制了免疫系统的反应。目前正在研究各种物理、化学和生物治疗方式，以改变肿瘤环境，使其从免疫效应被抑制的状态转变为支持抗肿瘤免疫反应的状态。然而，由于缺乏监测肿瘤状态以响应候选治疗的技术，这些方法受到阻碍。能够持续监测肿瘤免疫状态的技术，从而指导精确的干预措施，将肿瘤驱动到免疫刺激状态，为释放免疫疗法的全部潜力提供了希望。网络物理系统（CPS）的观点非常适合解决这一挑战，通过传感器和肿瘤纵向评估分析技术的发展，将肿瘤视为“体内CPS”，再加上提供物理/化学治疗的共同定位方法，以调节肿瘤内的环境，使其达到免疫刺激状态。如果成功开发和翻译，该项目中研究的免疫调节CPS框架可能最终指导在免疫治疗递送之前启动干预的选择和最佳递送，确定启动干预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。研究小组将开发一门生物医学网络物理系统的研究生课程，并为本科课程开发植入式生物医学传感器模块。此外，该项目将通过STEM项目为来自弱势群体的学生提供暑期研究机会。该项目将研究肿瘤微环境（TME）免疫调节的CPS框架，整合：(1)一个独特的3D微阵列传感器和治疗（MIST）设备，该设备由一个传感/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，用于从TME物理/生理特征中确定肿瘤免疫状态；(3)通过MIST装置进行模型引导治疗，使TME进入免疫刺激状态。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，以实现对TME参数（如组织氧合、pH值、压力和代谢）的纵向体内传感，并在单个设备上进行同步治疗。从植入式传感器收集的数据将与生物物理参数的计算模型融合，这些参数由肿瘤特异性血管图提供，使用图形神经张量补全方法。这种用于数据输入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。结合递归神经网络的基于图的聚类方法将用于肿瘤状态变化的预测。最后，我们将评估以模型为导向的能量干预将TME转化为免疫原性状态的有效性，以及这些干预对小动物免疫治疗结果的影响。该项目由网络物理计划和促进竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.