Multi-scale modeling of glioma for the prediction of treatment response, treatment monitoring and treatment allocation

用于预测治疗反应、治疗监测和治疗分配的神经胶质瘤多尺度建模

• 批准号: 10184938
• 负责人: Olivier Gevaert
• 金额: $ 61.2万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10184938
• 关键词: Adult; Algorithms; Area; Basic Science; Biological Assay; Biological Markers; Brain Neoplasms; Cancer Patient; Clinical; Clinical Trials; Computer Models; Computing Methodologies; DNA Methylation; DNA Repair Enzymes; Data; Data Set; Data Sources; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Diagnostic radiologic examination; Drug Targeting; Epidermal Growth Factor Receptor; Evaluation; Event; Eye; Gene Expression; Gene Expression Profile; Genome; Glioma; High-Throughput Nucleotide Sequencing; Human; Image; Informatics; Link; MGMT gene; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Medical center; Modality; Modeling; Molecular; Molecular Profiling; Monitor; Outcome; Pathway interactions; Patient imaging; Patients; Pattern; Pharmaceutical Preparations; Prediction of Response to Therapy; Prognosis; Prognostic Marker; Property; Research; Resistance; Role; Somatic Mutation; Technology; Therapeutic; Time; Translating; Translations; Treatment outcome; Tumor Subtype; Tumor Tissue; Work; base; cancer site; clinical application; clinical care; clinical practice; cohort; data framework; data fusion; digital pathology; epigenetic silencing; follow-up; genome sequencing; imaging biomarker; imaging modality; imaging study; in vivo; methylation pattern; molecular imaging; multi-scale modeling; multimodality; multiple omics; multiscale data; mutational status; neuro-oncology; novel; novel strategies; novel therapeutics; pathology imaging; patient biomarkers; precision medicine; predict clinical outcome; predictive marker; prospective; prospective test; quantitative imaging; radiological imaging; radiologist; recruit; response; survival outcome; survival prediction; synergism; temozolomide; treatment response; treatment strategy; tumor; whole slide imaging

Project summary Computational multi-scale modeling is a growing area of research that aims to link whole slide images and radiographic iamges with multi-omics molecular profiles of the same patients. Multi-scale modeling has shown its potential through its ability to predict clinical outcomes e.g. prognosis, and through predicting actionable molecular properties of tumors, e.g. the activity of EGFR, a major drug target in many cancers. Current applications are limited to study associations between imaging and molecular data, and predicting long term outcomes. No actionable information can be gained from multi-scale biomarkers yet. We propose to develop a multi-scale modeling framework to support treatment response, treatment monitoring and treatment allocation for patients with brain tumors, focusing on the most aggressive subtype of glioma, IDH wild-type high grade glioma. In Aim 1, we will develop informatics algorithms that integrate multi-scale data for treatment response. We will use our expertise in data fusion and develop novel approaches to integrate multi- scale data to predict first line treatment response. In Aim 2, we will develop algorithms that allow combining multi-scale data at diagnosis with multi-modal MR imaging data during treatment follow-up. We will focus on predicting treatment response and progression and whether we can predict these events earlier than radiologists can. In Aim 3, we will develop algorithms that use the multi-scale data to predict drug target activities and also suggest novel drugs for patients that become resistant to first line treatment. We will use a mixture of publicly available glioma multi-scale data sets totaling more than 1000 patients, and also 1600 retrospective and 150 prospective brain tumor patients from Stanford Medical Center. Combining these complementary data sources in a multi-scale framework for data fusion can have profound contributions toward predicting treatment outcomes by uncovering unknown synergies and relationships. More specifically, developing computational models integrating quantitative image features and molecular data to develop multi-scale signatures, holds the potential to translate in benefit to brain tumor patients by investigating biomarkers that accurately predict treatment response. Readily, because whole slide images and radiographic imaging is part of the routine diagnostic work-up of cancer patients and molecular data of brain tumors is increasingly being used in clinical workflows, therefore if reliable multi-scale signatures can be found reflecting treatment response, translation to clinical applications is feasible, including optimizing recruitment for clinical trials.

项目摘要 计算多尺度建模是一个越来越多的研究领域，旨在将整个幻灯片图像和 同一患者的多摩学分子谱的放射线图。多尺度建模已显示 通过预测临床结果的能力，它的潜力例如预后，通过预测可行的 肿瘤的分子特性，例如EGFR的活性是许多癌症的主要药物靶标。当前的 应用仅限于研究成像和分子数据之间的关联，并预测长期 结果。尚未从多尺度生物标志物中获得可行的信息。 我们建议开发一个多尺度建模框架来支持治疗反应，治疗监测 和针对脑肿瘤患者的治疗分配，重点是胶质瘤最具侵略性的亚型IDH 野生型高级神经胶质瘤。在AIM 1中，我们将开发信息学算法，以集成多尺度数据 治疗反应。我们将在数据融合中使用我们的专业知识，并开发新颖的方法来整合多种 扩展数据以预测一线治疗响应。在AIM 2中，我们将开发允许合并的算法 在治疗随访期间，具有多模式MR成像数据诊断时的多尺度数据。我们将重点关注 预测治疗反应和进展，以及我们是否可以比 放射科医生可以。在AIM 3中，我们将开发使用多尺度数据预测药物目标的算法 活动，还为对一线治疗具有抗药性的患者提供了新的药物。我们将使用一个 公开可用的神经胶质瘤多尺度数据集的混合物，总计1000多名患者，还有1600例 来自斯坦福医学中心的回顾性和150名前瞻性脑肿瘤患者。 将这些互补数据源组合到多尺度的数据融合框架中，可以具有深刻的 通过发现未知的协同作用和关系来预测治疗结果的贡献。更多的 具体而言，开发计算模型将定量图像特征和分子数据集成到 开发多尺度特征，具有通过研究来转化为脑肿瘤患者受益的潜力 准确预测治疗反应的生物标志物。很容易，因为整个幻灯片图像和影像学 成像是癌症患者常规诊断检查的一部分，脑肿瘤的分子数据是 因此，越来越多地用于临床工作流程，因此，如果可以发现可靠的多尺度标志 治疗响应，转化为临床应用是可行的，包括优化临床招募 试验。