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

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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.