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: 10397589
• 负责人: Olivier Gevaert
• 金额: $ 56.86万
• 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/10397589
• 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; 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.

项目总结 计算多尺度建模是一个不断发展的研究领域，其目标是将整个幻灯片图像和 同一患者的多组学分子图谱的放射图像。多尺度模型显示 它的潜力在于它能够预测临床结果，例如预后，并通过预测可操作 肿瘤的分子特性，例如EGFR的活性，这是许多癌症的主要药物靶点。当前 应用仅限于研究成像和分子数据之间的联系，以及预测长期 结果。目前还不能从多尺度生物标志物中获得可操作的信息。 我们建议开发一个多尺度建模框架来支持治疗响应、治疗监测 和脑肿瘤患者的治疗分配，重点是最具侵袭性的胶质瘤亚型IDH 野生型高级别胶质瘤。在目标1中，我们将开发集成多尺度数据的信息学算法 治疗反应。我们将利用我们在数据融合方面的专业知识，开发新的方法来集成多个 衡量数据以预测一线治疗反应。在目标2中，我们将开发允许组合的算法 在治疗随访期间使用多模式磁共振成像数据对诊断时的多尺度数据进行分析。我们将重点关注 预测治疗反应和进展，以及我们是否可以更早地预测这些事件 放射科医生可以。在目标3中，我们将开发使用多尺度数据来预测药物靶点的算法 活动，并为对一线治疗产生抗药性的患者提出新的药物建议。我们将使用 公开可用的胶质瘤多尺度数据集的混合，总计超过1000名患者，还有1600名 对来自斯坦福医学中心的150名脑瘤患者进行回顾性研究。 在数据融合的多尺度框架中组合这些互补的数据源可能会产生深远的影响 通过揭示未知的协同效应和关系，为预测治疗结果做出贡献。更多 具体地说，开发集成定量图像特征和分子数据的计算模型以 开发多尺度签名，通过调查持有翻译造福脑瘤患者的潜力 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射线照片 成像是癌症患者常规诊断工作的一部分，脑肿瘤的分子数据是 因此，在临床工作流程中越来越多地使用，因此如果能够找到可靠的多尺度签名来反映 治疗反应，转化为临床应用是可行的，包括优化临床招募 审判。