Molecular profiles of tumors are nowadays used to determine prognosis and to guide therapy. For example the presence of a mutation in the EGFR gene will most likely lead to anti-EGFR therapy. Recently an image phenotype was discovered that acts as a biomarker of EGFR mutation. This is a precursor of the possibilities of a new emerging field called radiogenomics defined as directly linking imaging features to underlying molecular properties. Research in radiogenomics is rapidly gaining recognition as a powerful new field that has several promising applications, such as non-invasive molecular lesion assessment. When image surrogates can be identified that mirror relevant molecular aberrations (e.g. a mutation in the EGFR gene) they can be readily translated in clinical care. The value added by radiogenomics can be readily translated, as medical imaging is part of routine management in oncology. However, these early applications have not taken full advantage of the opportunities. First, they limit the correlation to either a handful of manually annotated image features and a pre-selected set of molecular parameters. Secondly, the initial applications are limited to a single omics by focusing on gene expression, without taking into account DNA mutations, DNA copy number changes or DNA methylation changes. We will develop a radiogenomics framework to identify non-invasive biomarkers mirroring relevant molecular tumor properties that impact treatment and clinical outcome of human brain tumors. Our objective is not to mimic a radiologist's expertise through computational means, but to empower radiologists and clinicians with new biomarkers. We will offer innovative new algorithms to represent medical images. Once such a representation is computed (e.g., in the form of a large data matrix), we will identify univariate and multivariate image signatures predictive of clinical outcome. Next, we will use sophisticated methods for integration with molecular data to interrogate different views of the data with respect to a clinically relevant outcome. The end result is a radiogenomics map where image signatures of molecular properties and tumor heterogeneity can be hypothesized and validated. We will have image signatures that are prognostic and image signatures reflecting actionable molecular properties of a tumor such as drug target activity or drug signatures.

Public Health Relevance

Our proposed radiogenomics framework will identify non-invasive biomarkers that predict prognosis and image signatures that mirror relevant molecular tumor properties that impact treatment and clinical outcome of human brain tumors. Moreover, we will have image signatures that are prognostic and image signatures reflecting actionable molecular properties of a tumor such as drug target activity or drug signatures.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB020527-01A1
Application #
8837360
Study Section
Special Emphasis Panel (ZRG1-SBIB-Q (80))
Program Officer
Pai, Vinay Manjunath
Project Start
2015-02-15
Project End
2019-01-31
Budget Start
2015-02-15
Budget End
2016-01-31
Support Year
1
Fiscal Year
2015
Total Cost
$515,525
Indirect Cost
$190,729
Name
Stanford University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
Bulens, Philippe; Couwenberg, Alice; Haustermans, Karin et al. (2018) Development and validation of an MRI-based model to predict response to chemoradiotherapy for rectal cancer. Radiother Oncol 126:437-442
Bakr, Shaimaa; Gevaert, Olivier; Echegaray, Sebastian et al. (2018) A radiogenomic dataset of non-small cell lung cancer. Sci Data 5:180202
Malta, Tathiane M; Sokolov, Artem; Gentles, Andrew J et al. (2018) Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell 173:338-354.e15
Champion, Magali; Brennan, Kevin; Croonenborghs, Tom et al. (2018) Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response. EBioMedicine 27:156-166
Zhou, Mu; Leung, Ann; Echegaray, Sebastian et al. (2018) Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications. Radiology 286:307-315
Iv, M; Zhou, M; Shpanskaya, K et al. (2018) MR Imaging-Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma. AJNR Am J Neuroradiol :
Zhou, M; Scott, J; Chaudhury, B et al. (2018) Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. AJNR Am J Neuroradiol 39:208-216
Wang, Shuo; Zhou, Mu; Liu, Zaiyi et al. (2017) Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Med Image Anal 40:172-183
Yan, Kelley S; Gevaert, Olivier; Zheng, Grace X Y et al. (2017) Intestinal Enteroendocrine Lineage Cells Possess Homeostatic and Injury-Inducible Stem Cell Activity. Cell Stem Cell 21:78-90.e6
Panahiazar, Maryam; Dumontier, Michel; Gevaert, Olivier (2017) Predicting biomedical metadata in CEDAR: A study of Gene Expression Omnibus (GEO). J Biomed Inform 72:132-139

Showing the most recent 10 out of 24 publications