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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：
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
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/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

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.

项目总结