Langworthy Diversity Supplement: Image-based models of tumor-immune dynamics in glioblastoma

Langworthy Diversity Supplement：基于图像的胶质母细胞瘤肿瘤免疫动力学模型

• 批准号: 10381307
• 负责人: Peter Canoll
• 金额: $ 4.61万
• 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/10381307
• 关键词: Advanced Malignant Neoplasm; Artificial Intelligence; Biopsy; Brain Neoplasms; Computer Models; Environment; Evolution; Genomics; Glioblastoma; Glioma; Image; Image Guided Biopsy; Immune; Immunologics; Immunotherapeutic agent; Immunotherapy; Link; Magnetic Resonance Imaging; Malignant Neoplasms; Maps; Methods; Microglia; Modeling; Patients; Phenotype; Population; Treatment Protocols; Uncertainty; aggressive therapy; anticancer research; base; biological heterogeneity; cancer care; individual patient; macrophage; neuro-oncology; non-invasive imaging; parent grant; radiomics; response; tumor; tumor heterogeneity

ABSTRACT Glioblastoma Multiforme (GBM) is the most common of all gliomas with a median survival 14-18 months, despite aggressive treatment regimens. Immunotherapy is emerging as a promising method to treat cancer; however, we are not able to identify early response or predict who will respond. These uncertainties pose serious challenges to being able to effectively apply immunotherapeutic approaches. While biopsies are the most reliable way to assess the immunological landscape within the tumor, we are limited both spatially and temporally in the number of biopsies we can obtain, particularly for brain tumor patients. The heterogeneity of the tumor-immune landscape across patients suggests that a patient-specific approach will be required to accurately assess each patient’s individual tumor-immune environment and the evolution thereof. As part of the Parent Grant, we will use non-invasive imaging, image-guided biopsies, computational modeling, and artificial intelligence to bridge spatial and temporal scales and predict the abundance of glioma associated microglia/macrophages (GAMMs) comprising each magnetic resonance image (MRI) at the voxel level. Linking the MRI to the biological heterogeneity using radiomics approaches provides an opportunity to individualize our understanding of the tumor-immune environment. Specifically, for this supplement, we will use the predictive tumor-immune maps to develop an immunotherapy response metric termed GAMMs Days Gained (GDG), which is based on the existing Days Gained metric. GDG will be used to evaluate the GAMM population changes with therapy as depicted by the predictive map. We expect that the GDG will aid in understanding who will respond based on early predictive map changes. Additionally, the GDG metric will be compared to results from other immunotherapy response metrics, including the standard immunotherapy response assessment in neuro-oncology (iRANO) criteria.

摘要 多形性胶质母细胞瘤（GBM）是所有胶质瘤中最常见的，尽管存在严重的并发症，但中位生存期为14-18个月。 积极的治疗方案。免疫疗法正在成为治疗癌症的一种有前途的方法;然而， 我们无法确定早期响应或预测谁会响应。这些不确定性造成了严重的 能够有效应用免疫方法的挑战。虽然活检是最可靠的 为了评估肿瘤内的免疫景观，我们在空间和时间上都受到限制， 我们可以获得的活组织检查的数量，特别是对于脑肿瘤患者。肿瘤免疫的异质性 患者的总体情况表明，需要采用患者特异性方法来准确评估每个 患者的个体肿瘤免疫环境及其演变。作为家长补助金的一部分，我们将 使用非侵入性成像、图像引导活检、计算建模和人工智能， 空间和时间尺度，并预测胶质瘤相关的小胶质细胞/巨噬细胞（GAMM）的丰度 包括体素级的每个磁共振图像（MRI）。将核磁共振成像与生物学 使用放射组学方法的异质性提供了一个机会，个性化我们的理解， 肿瘤免疫环境具体来说，对于这种补充，我们将使用预测性肿瘤免疫图， 开发一种称为GAMMs获得天数（GDG）的免疫治疗反应指标，该指标基于现有的 获得的天数指标。GDG将用于评价GAMM人群随治疗的变化，如 预测地图我们希望GDG将有助于了解谁将根据早期预测做出响应， 地图改变。此外，将GDG指标与其他免疫治疗应答的结果进行比较 指标，包括神经肿瘤学标准免疫治疗反应评估（iRANO）标准。