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Methodology for Imaging Genomics of Gliomas
Rivka R. Colen, M.D.1, Bhanu Mahajan, M.B.B.S.1, Arpad Kovacs, M.D.1, Pascal O. Zinn, M.D.2, Ferenc A. Jolesz, M.D.1
1 Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.2 M.D. Anderson Cancer Center, Houston, TX, USA.
• Quantitative image analysis results in more precise and reproducible results and can then be correlated with genomics.
• In imaging genomics, tumor volumetrics has the potential to uncover genomic targets for subsequent development into cancer therapeutics.
• This was first described by Zinn et al in 2011 where an invasive gene and microRNA was uncovered using the FLAIR volume as an MRI biomarker (radiophenotype).
• Here, we demonstrate the methodology and feasibility for the first comprehensive radiogenomic analysis using quantitative MRI volumetrics and large-scale gene- and microRNA expression profiling in Glioblastoma Multiforme (GBM).
• Retrospective study of 78 treatment naïve GBM patients
• Image data used in this research were obtained from The Cancer Imaging Archive (http://cancerimagingarchive.net/) sponsored by the Cancer Imaging Program, DCTD/NCI/NIH.
• Imaging was correlated with Gene- and micoRNA- expression profiles obtained from The Cancer Genome Atlas (TCGA)
Tumor Segmentation. 65 year old male patient with a right temporal GBM. Segmentation of tumor edema (blue), enhancement (yellow) and necrosis (red) was performed and edema volume was obtained.
Fluid-attenuated inversion recovery (FLAIR) was used for segmentation of the edema
MRI-FLAIR radiophenotype readily and noninvasively detected key cancer genomic components responsible for cellular migration and invasion in GBM.
• Quantitative imaging genomic analysis is simple.
• Imaging surrogates/ biomarkers using conventional MRI radiophenotypes of necrosis, enhancing tumor and edema/tumor infiltration reflect specific genomic composition in GBM.
• Quantitative imaging genomic analysis allow for predictions of key genetic events.
• Quantitative imaging genomic analysis can screen for novel genomic targets for possible subsequent development of targeted therapeutics.
• Stratify patients based on their genetic/molecular makeup into responders versus non-responders to a particular treatment.
• The ability to determine uncover genomic targets from routine clinical MRI examinations and thus subsequently determine if a patient is a candidate for that genomic therapy is a major step towards personalized medicine.