Quantifying Multiscale Competitive Landscapes of Clonal Diversity in Glioblastoma

量化胶质母细胞瘤克隆多样性的多尺度竞争格局

• 批准号: 9389124
• 负责人: Leland Hu
• 金额: $ 71.65万
• 依托单位: MAYO CLINIC ARIZONA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-12 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9389124
• 关键词: Address; Behavior; Biopsy; Brain; Cell Line; Cell Survival; Cells; Clinical; Communication; Computer Simulation; Cultured Cells; Data; Dependency; Diagnosis; Diagnostic; Drug resistance; Epidermal Growth Factor Receptor; Excision; Exhibits; Future; Gene Mutation; Genomics; Glioblastoma; Heterogeneity; Human; Hypoxia; Image; Image Guided Biopsy; Imaging Techniques; Individual; Injectable; Location; Machine Learning; Magnetic Resonance Imaging; Maps; Minority; Modeling; Molecular; Molecular Profiling; Mus; Mutation; Nature; Operative Surgical Procedures; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Primary Neoplasm; Recruitment Activity; Recurrence; Recurrent tumor; Residual state; Resistance; Risk; System; Therapeutic; Tissue Sample; Tissues; Tumorigenicity; Work; Xenograft Model; Xenograft procedure; angiogenesis; base; cell growth; cohort; contrast enhanced; epidermal growth factor receptor VIII; exome; follow-up; genomic data; image guided; imaging informatics; improved; in vitro Assay; mathematical model; molecular dynamics; novel strategies; oncology; patient stratification; predicting response; predictive modeling; serial imaging; therapeutic target; therapy resistant; transcriptome; transcriptome sequencing; translational impact; treatment response; tumor; tumor growth; tumor heterogeneity; tumor progression

ABSTRACT Glioblastoma (GBM) exhibits profound intratumoral molecular heterogeneity that contributes to treatment resistance and poor survival. Specifically, each tumor comprises multiple molecularly-distinct subpopulations with different treatment sensitivities. This heterogeneity not only portends the pre-existence of resistant molecular subpopulations, but also the communications between neighboring subpopulations that further modulate tumorigenicity and resistance. In fact, a minority tumor subpopulation with EGFRvIII mutation has been shown to potentiate a majority subpopulation with wild-type EGFR to increase tumor growth, cell survival, and drug resistance. This type of cooperativity presents clear implications for improving GBM treatment. Yet compared to other tumor types, the interactions in GBM remain critically understudied. A significant barrier to studying the interactions between molecularly-distinct subpopulations is the challenge of tissue sampling in GBM. In particular, contrast-enhanced MRI (CE-MRI) routinely guides surgical biopsy and resection of the MRI enhancing core, but fails to address the diverse subpopulations of the surrounding non-enhancing parenchyma (so called “brain around tumor” or BAT). These unresected residual subpopulations in BAT represent the main contributors to tumor recurrence, which can exhibit different therapeutic targets (and interactions) compared with enhancing biopsies. To address the limitations of tissue sampling, imaging techniques can help quantitatively characterize tumors in their entirety, including unresected BAT regions. Our group has used multi-parametric MRI and image-guided biopsies to develop and validate machine-learning (ML) models of intratumoral genomic heterogeneity, with particular focus on the BAT zone. In Aim 1, will we collect and molecularly profile a large set of image-recorded stereotactic biopsies in primary GBM patients to quantify the diversity of molecularly-distinct subpopulations, as well as their phenotypic niches, throughout the BAT zone. We will assess local heterogeneity at the biopsy level and also co-localize regional patterns and rates of recurrence on serial MRI. In Aim 2, we will use these biopsies and spatially matched MRI metrics to refine our existing ML predictive models. We will use these ML models to co- localize spatial patterns of molecularly-distinct subpopulations (and their phenotypic niches) to quantify their risk of regional recurrence. In Aim 3, we will functionally validate the subpopulation interactions observed in Aims 1 and 2 using patient derived xenograft (PDX) models. We will also validate these interactions in human GBM using a subset of spatially matched biopsies from primary and recurrent tumors in the same patients. This proposal leverages our unique expertise in image-guided tissue analysis and MRI-based computational modeling to study the diversity of molecularly-distinct subpopulations and the evolving competitive landscapes in human GBM. This work will help risk stratify patients in future targeted clinical drug trials and should also facilitate new strategies (e.g., adaptive therapy) to exploit subpopulation co-dependency for therapeutic benefit.

摘要 胶质母细胞瘤（GBM）表现出深刻的肿瘤内分子异质性，有助于治疗 抵抗力和生存能力差。具体地，每个肿瘤包括多个分子上不同的亚群 不同的治疗敏感性。这种异质性不仅预示着抗性的预先存在， 分子亚群，而且相邻亚群之间的通信， 调节致瘤性和抗性。事实上，少数具有EGFRvIII突变的肿瘤亚群 已显示增强野生型EGFR的大多数亚群以增加肿瘤生长，细胞存活， 和耐药性。这种类型的协同性为改善GBM治疗提供了明确的意义。然而 与其他肿瘤类型相比，GBM中的相互作用仍然严重不足。 研究不同分子亚群之间相互作用的一个重要障碍是 GBM中组织取样的挑战。特别是，对比增强MRI（CE-MRI）常规指导手术 活检和切除MRI增强核心，但未能解决不同的亚群的 周围非增强实质（所谓的“肿瘤周围脑”或BAT）。这些未切除的残留物 BAT中的亚群代表肿瘤复发的主要贡献者，其可以表现出不同的 与增强活检相比，治疗靶点（和相互作用）。为了解决组织的局限性， 采样，成像技术可以帮助定量表征肿瘤的整体，包括未切除的 最佳可得技术区域。我们的团队已经使用多参数MRI和图像引导活检来开发和验证 肿瘤内基因组异质性的机器学习（ML）模型，特别关注BAT区域。 在目标1中，我们是否会收集大量的图像记录的立体定向活检标本，并对其进行分子分析， 原发性GBM患者，以量化分子上不同的亚群的多样性，以及他们的 表型生态位，在整个BAT区。我们将在活检水平评估局部异质性， 共定位区域模式和复发率的系列MRI。在目标2中，我们将使用这些活检， 空间匹配的MRI指标来完善我们现有的ML预测模型。我们将使用这些ML模型来共同- 定位分子不同亚群（及其表型生态位）的空间模式，以量化其 区域复发风险。在目标3中，我们将从功能上验证在 目的1和2使用患者来源的异种移植物（PDX）模型。我们还将在人类中验证这些相互作用 GBM使用同一患者原发性和复发性肿瘤的空间匹配活检样本子集。 该提案利用了我们在图像引导组织分析和基于MRI的计算 建模研究分子不同亚群的多样性和不断变化的竞争格局 在人类GBM中。这项工作将有助于在未来的靶向临床药物试验中对患者进行风险分层， 促进新的战略（例如，适应性治疗），以利用亚群相互依赖性来获得治疗益处。