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 ...

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Changes in blood epigenetic age have been associated with several pathological conditions and have recently been described to anticipate cancer development. In this work, we analyze a publicly available leukocytes methylation dataset to evaluate the relation between DNA methylation age and the prospective development of specific types of cancer. We calculated DNA methylation age acceleration using five state-of-the-art estimators (three multi-site: Horvath, Hannum, Weidner; and two CpG specific: ELOV2 and FHL2) in a cohort including 845 subjects from the EPIC-Italy project and we compared 424 samples that remained cancer-free over the approximately ten years of follow-up with 235 and 166 subjects who developed breast and colorectal cancer, respectively. We show that the epigenetic age estimated from blood DNA methylation data is statistically significantly associated to future breast and male colorectal cancer development. These results are corroborated by survival analysis that shows significant association between age acceleration and cancer incidence suggesting that the chance of developing age-related diseases may be predicted by circulating epigenetic markers, with a dependence upon tumor type, sex and age estimator. These are encouraging results towards the non-invasive and perspective usage of epigenetic biomarkers.