MRI-based mapping of regional genomic diversity in Glioblastoma

描述（由申请人提供）：我们建议开发一种基于图像的胶质母细胞瘤（GBM）诊断系统，该系统可识别肿瘤几乎总是复发的区域内治疗抗性的潜在遗传基础。这将有助于为GBM患者提供个性化护理。目前的治疗选择是公式化的，对所有患者都是统一的，没有考虑到广泛的遗传多样性，导致治疗抵抗和预后不良。具体而言，每个患者的GBM是独特的异质性，并且由对治疗具有不同亲和性的多个不同亚克隆群体组成。这种多样性导致肿瘤对靶向治疗的反应不均匀，并允许抗性克隆作为复发性疾病重新繁殖。此外，常规MRI常规指导增强肿瘤核心的手术切除，但在相邻的非增强实质或肿瘤周围脑（BAT）内留下肿瘤群。BAT是辅助治疗的主要靶点，因为它含有几乎普遍复发的残留肿瘤群体。在BAT中处理基因组多样性应该为治疗选择提供信息，但该区域几乎从未进行过活检，因为它在常规MRI上的评价很差。目前，还没有系统的方法，解决肿瘤内异质性的特点BAT内的区域基因组多样性。为了满足这一关键需求，该探索性提案将开发和测试一种新型映射系统，该系统将多参数MRI与图像引导的组织分析和机器学习（ML）算法相结合，以描绘GBM中的区域基因组变异。该系统使用常规MRI来识别主要的肿瘤亚组分：增强核心、BAT和中央坏死。在这些子组件中，高级MRI（灌注、扩散、纹理）将进一步表征局部肿瘤特性（即，血管生成、渗透性、侵袭和增殖），其代表潜在基因组状态的表型表达。这些MRI特征将指导来自不同肿瘤亚区域的立体定向活检，以生成匹配的MRI和基因组数据对。ML算法将结合这些数据来估计每个肿瘤的区域基因组多样性，包括尚未手术采样的BAT区域。我们已经统一了一个多学科的研究人员团队，这些研究人员来自具有实质性和长期合作关系的机构。我们的团队在多个研究领域提供专业知识，这些领域是实现研究目标所必需的，包括：1）图像处理和分析; 2）图像引导立体定向手术和配准; 3）机器学习（ML）方法的开发;以及4）BAT中肿瘤的全面基因组询问和细胞生物学。如果成功的话，这里提出的工作应该会显著影响GBM患者的诊断和治疗。这可能通过实现从“一种治疗适合所有人”到基于突变的方法的范式转变来改善临床结果，该方法选择靶向个体肿瘤群体的组合疗法。

DESCRIPTION (provided by applicant): We propose to develop an image-based diagnostic system for Glioblastoma (GBM) that identifies the potential genetic underpinnings of treatment resistance within the zone where tumor almost always recurs. This should facilitate the delivery of individualized care for patients with GBM. Current therapy selection is formulaic and uniform for all patients and does not account for broad genetic diversity that contributes to treatment resistance and dismal prognosis. Specifically, each patient's GBM is uniquely heterogeneous and comprised of multiple distinct subclonal populations with differing susceptibilities to therapy This diversity causes tumors to respond non-uniformly to targeted therapy and allows resistant clones to repopulate as recurrent disease. Additionally, conventional MRI routinely guides surgical resection of enhancing tumor core, but leaves behind tumor populations within adjacent non-enhancing parenchyma, or brain around tumor (BAT). The BAT represents the primary target of adjuvant therapy because it harbors the residual tumor populations that nearly universally recur. Curating the genomic diversity within BAT should inform treatment selection, but this region is almost never biopsied because it is poorly evaluated on conventional MRI. Currently, there is no systematic method that addresses intratumoral heterogeneity to characterize the regional genomic diversity within BAT. To address this critical need, this exploratory proposal will develop and test a novel mapping system that integrates multi-parametric MRI with image-guided tissue analysis and machine learning (ML) algorithms to delineate regional genomic variations in GBM. This system uses conventional MRI to identify major tumoral subcomponents: enhancing core, BAT, and central necrosis. Within these subcomponents, advanced MRI (perfusion, diffusion, texture) will further characterize regional tumor properties (i.e., angiogenesis, permeability, invasion, and proliferation) that represent phenotypic expression of underlying genomic status. These MRI traits will guide stereotactic biopsies from distinct tumoral subregions to generate matched pairs of MRI and genomic data. An ML algorithm will incorporate these data to estimate regional genomic diversity throughout each tumor, including BAT areas that have not been surgically sampled. We have unified a multi-disciplinary team of investigators from institutions that have substantial and long-standing collaborations. Our group offers expertise in multiple fields of study that are necessary to accomplish the research aims, including: 1) image processing and analytics; 2) image-guided stereotactic surgery and coregistration; 3) development of machine learning (ML) methodology; and 4) comprehensive genomic interrogation and cell biology of tumor within BAT. If successful, the work proposed here should significantly impact how GBM patients are diagnosed and treated. This potentially improves clinical outcomes by enabling a paradigm shift from "one treatment fits all" to a mutations-based approach that selects combinatorial therapies targeting individual tumor populations.