Image-based models of tumor-immune dynamics in glioblastoma

胶质母细胞瘤肿瘤免疫动力学的基于图像的模型

• 批准号: 10737767
• 负责人: Peter Canoll
• 金额: $ 1.06万
• 依托单位: MAYO CLINIC ARIZONA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10737767
• 关键词: Advanced Malignant Neoplasm; Anti-Inflammatory Agents; Area; Artificial Intelligence; Biological; Biophysics; Biopsy; Cancerous; Cells; Classification; Clinical; Complex; Computer Models; Data; Decision Making; Diagnostic; Early identification; Edema; Environment; Evolution; Future; Glioblastoma; Glioma; Goals; Heterogeneity; Image; Immune; Immune system; Immunologics; Immunology; Immunosuppression; Immunotherapy; In Situ; Inflammation; Inflammatory; Link; Location; Machine Learning; Macrophage; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of brain; Maps; Measures; Microglia; Modeling; Molecular; Neuroglia; Oncology; Operative Surgical Procedures; Outcome; Pathway interactions; Patient-Focused Outcomes; Patients; Phenotype; Physics; Population; Predisposition; Prognosis; Recurrence; Resources; Role; Scientist; Sex Differences; Signal Transduction; Spatial Distribution; Specimen; System; Testing; Therapeutic; Therapeutic Intervention; Time; Tumor Promotion; Variant; anticancer research; biological heterogeneity; cancer care; cell type; clinical decision-making; clinical imaging; cohort; cytotoxic; effective therapy; immune cell infiltrate; in vivo; insight; machine learning model; molecular phenotype; neoplastic cell; novel strategies; outcome prediction; personalized approach; predictive modeling; quantitative imaging; radiomics; response; serial imaging; sex; spatiotemporal; standard of care; synergism; tool; tumor; tumor behavior; tumor growth; tumor microenvironment; tumor progression; tumor-immune system interactions

ABSTRACT The use of immunotherapy to treat cancer continues to generate hope and excitement among those involved in cancer care and research. However, our inability to explain why some patients do not respond to immunotherapy, combined with our inability to identify early response or predict the responders, poses serious challenges in this field. Currently, biopsies serve as the most informative way to assess the immunological activity within a cancerous area, but we are spatially and temporally limited in the number of biopsies we can obtain from patients, especially in cases of brain cancer. Clear evidence of tumor-immune environment heterogeneity across patients suggests that we will have to use an individualized approach in order to accurately assess patient tumor’s specific immune environment and the evolution of these complex systems. We propose to use computational modeling and artificial intelligence to bridge the spatial scales of the cellular content comprising each MRI at the voxel level, but also to bridge the temporal scales. We will focus on the most cellular immune population in glioblastoma, microglia/macrophages, that constitute as much as 50% of the cellular content of tumor specimens. By fusing MRI with the biological heterogeneity found in image- localized biopsies through such radiomics approaches provides an opportunity to individualize our understanding of the the tumor-immune environment, broadly benefiting scientists across the fields of oncology and immunology. In addition to providing a deeper understanding of the tumor at every imaging time point, the radiomics maps can also be used to parameterize dynamic mechanistic models of tumor growth to allow for prediction of future dynamics. These spatio-temporal models allow us to test hypotheses about causal relationships between different cell types and microenvironmental factors, as well as to verify whether the radiomics maps provide early dynamic insights into tumor response that can impact clinical decision making.

摘要 使用免疫疗法治疗癌症继续在参与治疗的人中产生希望和兴奋。 癌症治疗和研究。然而，我们无法解释为什么有些患者对 免疫疗法，加上我们无法识别早期反应或预测反应者， 这个领域的挑战。目前，活组织检查是评估免疫学指标的最具信息性的方法。 活动在癌区域内，但我们在空间和时间上有限的活检，我们可以 从病人身上获得，尤其是在脑癌的情况下。肿瘤免疫环境的明确证据 患者之间的异质性表明，我们必须使用个性化的方法， 准确评估患者肿瘤的特定免疫环境和这些复杂系统的演变。 我们建议使用计算建模和人工智能来桥接细胞的空间尺度 在体素水平上，包括每个MRI的内容，但也要桥接时间尺度。重点抓好 胶质母细胞瘤中的大多数细胞免疫群体，小胶质细胞/巨噬细胞，构成了多达50%的 肿瘤标本的细胞成分通过融合MRI与图像中发现的生物异质性- 通过这种放射组学方法进行的局部活检提供了一个机会， 了解肿瘤免疫环境，使肿瘤学领域的科学家广泛受益 和免疫学。除了在每个成像时间点提供对肿瘤的更深入了解外， 放射组学图谱也可用于对肿瘤生长的动态机制模型进行参数化 预测未来动态。这些时空模型使我们能够检验关于因果关系的假设 不同细胞类型和微环境因素之间的关系，以及验证 放射组学图谱提供了对肿瘤反应的早期动态洞察，可以影响临床决策。