权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
10614974
负责人：
Olivier Gevaert
金额：
$ 57.63万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
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/10614974
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 cancer site clinical application clinical care clinical practice clinical trial recruitment cohort data framework data fusion digital pathology epigenetic silencing follow-up genome sequencing imaging biomarker imaging modality in vivo methylation pattern 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 response survival outcome survival prediction synergism temozolomide treatment response treatment strategy tumor whole slide imaging

项目摘要

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.

项目概要计算多尺度建模是一个不断发展的研究领域，旨在将整个幻灯片图像与具有同一患者的多组学分子特征的放射线图像。多尺度建模表明它的潜力在于它能够预测临床结果，例如预测，并通过预测可采取行动肿瘤的分子特性，例如EGFR 的活性，EGFR 是许多癌症的主要药物靶点。当前的应用仅限于研究成像和分子数据之间的关联以及预测长期结果。目前还无法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反应、治疗监测脑肿瘤患者的治疗分配，重点关注最具侵袭性的胶质瘤亚型 IDH 野生型高级别神经胶质瘤。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缩放数据来预测一线治疗反应。在目标 2 中，我们将开发允许组合的算法诊断时的多尺度数据与治疗随访期间的多模态 MR 成像数据。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耐药性的患者推荐新药。我们将使用一个公开的神经胶质瘤多尺度数据集的混合物，总计超过 1000 名患者，还有 1600 来自斯坦福医学中心的 150 名脑肿瘤患者的回顾性和前瞻性研究。将这些互补的数据源组合在多尺度数据融合框架中可以产生深远的影响通过发现未知的协同作用和关系来预测治疗结果。更多的具体来说，开发集成定量图像特征和分子数据的计算模型开发多尺度特征，通过研究有可能为脑肿瘤患者带来益处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相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越来越多地在临床工作流程中使用，因此，如果可以找到反映可靠的多尺度特征治疗反应，转化为临床应用是可行的，包括优化临床招募试验。

项目成果

期刊论文数量（23）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Whole Slide Imaging-Based Prediction of TP53 Mutations Identifies an Aggressive Disease Phenotype in Prostate Cancer.

DOI：
10.1158/0008-5472.can-22-3113
发表时间：
2023-09-01
期刊：
CANCER RESEARCH
影响因子：
11.2
作者：
Pizurica, Marija;Larmuseau, Maarten;Van der Eecken, Kim;de Schaetzen van Brienen, Louise;Carrillo-Perez, Francisco;Isphording, Simon;Lumen, Nicolaas;Van Dorpe, Jo;Ost, Piet;Verbeke, Sofie;Gevaert, Olivier;Marchal, Kathleen
通讯作者：
Marchal, Kathleen

GeNNius: an ultrafast drug-target interaction inference method based on graph neural networks.