Olivier Gevaert, PhD
Stanford University School of Medicine
Center for Cancer Systems Biology (CCSB) & Department of Radiology
Thursday, August 2, 2012 at 10:00 AM Room 3167 Graves Hall
Radiogenomics of lung cancer and beyond
Vast amounts of molecular data characterizing the genome, epi-genome and transcriptome are becoming available for a wide range of cancers. In addition, new computational tools for quantitatively analyzing medical images are creating new types of phenotypic data. This presents the opportunity to integrate these heterogeneous data and create a more comprehensive view of key biological processes underlying image features. Moreover, this integration can have profound contributions toward predicting diagnosis and treatment. I will present our work focusing on integrating transcriptome and CT image data in non-small cell lung cancer. We developed methods extending the idea of a radiogenomics map beyond the early applications1, 2. More specifically, we built a radiogenomics strategy and identified a prognostic signature of image biomarkers, composed of imaging features that are expressed in terms of their predictive gene expression signature, by leveraging publicly available microarray data with survival outcomes. We applied this on both CT imaging (Gevaert O. et al. Radiology, In Press, June 21, 2012)3 and PET imaging (Nair V*, Gevaert O.*, Cancer Research, In Press, June 18)4 non small cell lung cancer patients. Moreover, we are extending these ideas to MRI imaging in glioblastoma multiforme (Gevaert O., et al. AACR 2012). These methods allow studying how form follows function by studying the molecular biology behind image features. Secondly, there is the potential to predict activity of molecular pathways based on image features, creating enormous possibilities of non-invasive diagnosis. Finally, our radiogenomics strategy for identifying imaging biomarkers may enable a more rapid evaluation of novel imaging modalities, thereby accelerating their translation to personalized medicine.
1. Segal, E. et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 25, 675-680 (2007).
2. Diehn, M. et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A 105, 5213-5218 (2008).
3. Gevaert, O. et al. Non-Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data--Methods and Preliminary Results. Radiology (2012).
4. Nair, V.S. et al. Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer. Cancer Res (2012).