Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme

Abstract

BackgroundDespite recent discoveries of new molecular targets and pathways, the search for an effective therapy for Glioblastoma Multiforme (GBM) continues. A newly emerged field, radiogenomics, links gene expression profiles with MRI phenotypes. MRI-FLAIR is a noninvasive diagnostic modality and was previously found to correlate with cellular invasion in GBM. Thus, our radiogenomic screen has the potential to reveal novel molecular determinants of invasion. Here, we present the first comprehensive radiogenomic analysis using quantitative MRI volumetrics and large-scale gene- and microRNA expression profiling in GBM.MethodsBased on The Cancer Genome Atlas (TCGA), discovery and validation sets with gene, microRNA, and quantitative MR-imaging data were created. Top concordant genes and microRNAs correlated with high FLAIR volumes from both sets were further characterized by Kaplan Meier survival statistics, microRNA-gene correlation analyses, and GBM molecular subtype-specific distribution.ResultsThe top upregulated gene in both the discovery (4 fold) and validation (11 fold) sets was PERIOSTIN (POSTN). The top downregulated microRNA in both sets was miR-219, which is predicted to bind to POSTN. Kaplan Meier analysis demonstrated that above median expression of POSTN resulted in significantly decreased survival and shorter time to disease progression (P<0.001). High POSTN and low miR-219 expression were significantly associated with the mesenchymal GBM subtype (P<0.0001).ConclusionHere, we propose a novel diagnostic method to screen for molecular cancer subtypes and genomic correlates of cellular invasion. Our findings also have potential therapeutic significance since successful molecular inhibition of invasion will improve therapy and patient survival in GBM.

Imaging genomics in cancer research: limitations and promises

“…9, These studies were published between 2007 and 2015. 8 studies used data from The Cancer Genome Atlas (TCGA) and/or The Cancer Imaging Archive (TCIA); 1,16,23,27,32,34,38,41 2 studies were multi-institutional; 9,19 and the remaining 17 studies used local institutional data. 15,17,18,[20][21][22][24][25][26]28,30,31,33,[35][36][37]39,40 26 out of 27 studies were retrospective in design.…”

“…8 (30%) studies used a validation data set to verify the association between imaging features and genomic data identified in the initial data set. 20,22,23,27,28,34,37,40 Six types of cancers were studied: glioblastoma multiforme (GBM)/high-grade glioma (n 5 14, 52%), non-small-cell lung cancer (NSCLC) (n 5 3, 11%), hepatocellular carcinoma (HCC) (n 5 3, 11%), breast cancer (n 5 5, 19%), clear-cell renal cell carcinoma (CCRCC) (n 5 1, 4%) and cervical cancer (n 5 1, 4%). The imaging modalities used included fluorine 18 fludeoxyglucose positron emission tomography (PET) (n 5 4, 15%), MRI [n 5 18 (including two perfusion MR), 67%] and CT [n 5 5 (including one perfusion CT), 19%].…”

Probabilistic Radiographic Atlas of Glioblastoma Phenotypes

“…2,3 In support of this hypothesis, region-specific brain tumor cells of origin have been identified in oligodendrogliomas, 4 medulloblastomas, 5 and ependymomas, 6 and compelling evidence has suggested that this is the case for IDH1 mutated gliomas. 7 Further, studies have identified clear links between radiologic features, including the volume and extent of edema and/or invasion 8 as well as contrast enhancement, 9 and -omic signatures. On the basis of these findings, we have constructed a set of radiographic atlases specifying the probability of tumor location and volumetric information for important demographic, -omic, and interventional phenotypes, with the goal of providing new insight into the possible niche locations of cells of origin in glioblastoma.…”

CT Imaging Correlates of Genomic Expression for Oral Cavity Squamous Cell Carcinoma

“…If imaging findings can correlate with clinically useful molecular or genomic biomarkers, then a potential exists for improved tissue sample targeting, 8 clinical decision making, response monitoring, 16 and discovering and validating molecular targets. 17 For example, higher expression of a gene correlated with invasion in glioblastoma by MR imaging and was subsequently found to be a prognostic biomarker of survival. 17 However, imaging-to-genetic cor-…”

“…17 For example, higher expression of a gene correlated with invasion in glioblastoma by MR imaging and was subsequently found to be a prognostic biomarker of survival. 17 However, imaging-to-genetic cor-…”