Identification of noninvasive imaging surrogates for brain tumor gene-expression modules

Abstract

Glioblastoma multiforme (GBM) is the most common and lethal primary brain tumor in adults. We combined neuroimaging and DNA microarray analysis to create a multidimensional map of gene-expression patterns in GBM that provided clinically relevant insights into tumor biology. Tumor contrast enhancement and mass effect predicted activation of specific hypoxia and proliferation gene-expression programs, respectively. Overexpression of EGFR, a receptor tyrosine kinase and potential therapeutic target, was also directly inferred by neuroimaging and was validated in an independent set of tumors by immunohistochemistry. Furthermore, imaging provided insights into the intratumoral distribution of gene-expression patterns within GBM. Most notably, an ''infiltrative'' imaging phenotype was identified that predicted patient outcome. Patients with this imaging phenotype had a greater tendency toward having multiple tumor foci and demonstrated significantly shorter survival than their counterparts. Our findings provide an in vivo portrait of genome-wide gene expression in GBM and offer a potential strategy for noninvasively selecting patients who may be candidates for individualized therapies.cancer ͉ genomics ͉ glioblastoma multiforme ͉ radiogenomics R ecent advances in the molecular analysis of brain tumors have led to an improved understanding of gliablastoma multiforme (GBM) tumor biology and the genomic heterogeneity that typifies the disease (1-7). However, the diagnosis and treatment of GBM is still largely guided by histopathology and immunohistochemistry, approaches that group histologically similar tumors that can often demonstrate markedly distinct clinical behaviors. Overall survival remains poor, with most patients succumbing to their disease within 15 months of diagnosis. Methods that assess molecular differences between GBMs hold promise for improving outcome by potentially allowing for individualized patient management.Magnetic resonance imaging (MRI) is routinely used in the diagnosis, characterization, and clinical management of GBM (8). It is a powerful and noninvasive diagnostic imaging tool that allows global assessment of GBMs and their interaction with their local environment. In its ability to extract structural, compositional, physiological, and functional information, MRI captures multidimensional, in vivo portraits of GBMs. Interestingly, histologically similar tumors often demonstrate highly distinct imaging profiles on MRI (9). Recently, several studies have attempted to correlate imaging findings with molecular markers, but no consistent associations have emerged. and many of the imaging features that characterize tumors currently lack biological or molecular correlates (10-15). Much of the information encoded within neuroimaging studies therefore remains unaccounted for and incompletely characterized at the molecular level. We reasoned that the phenotypic diversity of GBM captured by neuroimaging reflects underlying inter-and intratumoral gene-expression differences and that these relationships ...

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

“…Specific hypotheses to be tested based on other tumor types included 1) positive correlations between VEGF expression and enhancement 10,11 ; 2) EGFR expression with necrosis, tumor size, and presence of nodal metastasis 9,21 ; and 3) a negative correlation with cyclin D1 expression and tumor size and invasion. 24 We also hypothesized that because EGFR relates to growth and proliferation, it would have a positive correlation to mass effect, 8 …”

“…[7][8][9][10][11][12][13][14][15] Advantages of imaging include a larger area of coverage than pathologic specimens, which extends evaluation to tissue beyond the surgical resection margins, and less expense than combinations of multiple genomic tests, especially if obtained anyway as routine practice. 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.…”

Radiogenomics and Radioproteomics

“…This rationale has been explored in research but no attempt to investigate a clinical application has been initiated. A small amount of work has already been initiated looking more specifically at radiogenomics/radioproteomics in tumors [2][3][4][5][6][7][8][9], as well as prognosis and therapy modulation. Fukuda et al showed a significant negative correlation between the metabolic rate of oxygen and Fluorodeoxyglucose (FDG) standardized uptake value (SUV) in human liver tumors [10].…”

“…Strauss et al correlated compartmental parameters of FDG uptake and the gene expression values of cyclin-A [3]. Furthermore, Diehn et al showed that a high Contrast to Necrosis ratio on MRI was a surrogate imaging phenotype for EGFR expression in Glioblastomas [6]. They also found that an infiltrative "radiographic signature" predicted a worse prognosis [6].…”

“…Furthermore, Diehn et al showed that a high Contrast to Necrosis ratio on MRI was a surrogate imaging phenotype for EGFR expression in Glioblastomas [6]. They also found that an infiltrative "radiographic signature" predicted a worse prognosis [6]. Zander et al [16] and Sohn et al [17] found that FDG and Fluorothymidine (FLT), respectively, early in the course of treatment could predict outcomes to EGFR-TKI treatment.…”