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。虽然在不同疾病类型的大量患者中观察到了良好的反应，但相当多的患者从免疫治疗中获得的好处很少或没有。这种不同的结果归因于肿瘤内和肿瘤周围高度不同的物理和生理特征，抑制了免疫系统的反应。目前正在研究各种物理、化学和生物治疗方法，以改变肿瘤环境，从抑制免疫效应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，这些方法受到了阻碍。能够持续监测肿瘤免疫状态的技术，从而指导精确提供干预措施，将肿瘤推向免疫刺激状态，为释放免疫疗法的全部潜力提供了希望。计算机物理系统(CPS)的观点特别适合于解决这一挑战，它将肿瘤视为“体内CPS”，开发了用于纵向评估肿瘤的传感器和分析技术，并采用共同定位的方法提供物理/化学治疗，以将肿瘤内的环境调节到免疫刺激状态。如果成功开发和翻译，本项目中研究的免疫调节CPS框架可能最终指导免疫治疗前启动干预措施的选择和最佳提供，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，并用于评估治疗反应。研究团队将开发一门关于生物医学网络物理系统的研究生级课程，以及本科课程中关于植入式生物医学传感器的模块。此外，该项目将通过通向STEM的途径计划，为来自代表性不足群体的学生提供暑期研究机会。本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，包括用于纵向传感和控制TME内物理和生理参数的传感/致动平台；(2)新颖的基于模型信息的机器学习技术，用于根据TME的物理/生理特征确定肿瘤免疫状态；以及(3)通过MIST设备进行模型引导治疗，以将TME驱动到免疫刺激状态。先进的3D制造技术将提供可植入的微机械多模式传感设备，以实现对组织氧合、pH、压力和新陈代谢等TME参数的体内纵向传感，并在单个设备上进行协同治疗。从可植入传感器收集的数据将与生物物理参数的计算模型融合在一起，这些计算模型由使用图形神经张量完成方法的肿瘤特定血管图提供信息。这种新的用于数据填充和融合的混合机器学习方法将系统地纳入不确定性，并提供基础来推断肿瘤的免疫状态，并在实验小动物的免疫状态的金标准分子生物标记物上进行验证。基于图的聚类方法与递归神经网络相结合将被用于预测肿瘤状态的变化。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转变为有利于免疫原性的状态，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既定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。

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.