Journal of Neuro-Oncology

, Volume 129, Issue 2, pp 289–300 | Cite as

Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma

  • Guido H. Jajamovich
  • Chandni R. Valiathan
  • Razvan Cristescu
  • Sangeetha Somayajula
Clinical Study


Gene expression profiling from glioblastoma (GBM) patients enables characterization of cancer into subtypes that can be predictive of response to therapy. An integrative analysis of imaging and gene expression data can potentially be used to obtain novel biomarkers that are closely associated with the genetic subtype and gene signatures and thus provide a noninvasive approach to stratify GBM patients. In this retrospective study, we analyzed the expression of 12,042 genes for 558 patients from The Cancer Genome Atlas (TCGA). Among these patients, 50 patients had magnetic resonance imaging (MRI) studies including diffusion weighted (DW) MRI in The Cancer Imaging Archive (TCIA). We identified the contrast enhancing region of the tumors using the pre- and post-contrast T1-weighted MRI images and computed the apparent diffusion coefficient (ADC) histograms from the DW-MRI images. Using the gene expression data, we classified patients into four molecular subtypes, determined the number and composition of genes modules using the gap statistic, and computed gene signature scores. We used logistic regression to find significant predictors of GBM subtypes. We compared the predictors for different subtypes using Mann–Whitney U tests. We assessed detection power using area under the receiver operating characteristic (ROC) analysis. We computed Spearman correlations to determine the associations between ADC and each of the gene signatures. We performed gene enrichment analysis using Ingenuity Pathway Analysis (IPA). We adjusted all p values using the Benjamini and Hochberg method. The mean ADC was a significant predictor for the neural subtype. Neural tumors had a significantly lower mean ADC compared to non-neural tumors (\(p=0.005\)), with mean ADC of \(1.07\pm 0.16 \times 10^{-3}\) and \(1.23\pm 0.16\times 10^{-3}\; \mathrm{{mm^2/s}}\) for neural and non-neural tumors, respectively. Mean ADC showed an area under the ROC of 0.75 for detecting neural tumors. We found eight gene modules in the GBM cohort. The mean ADC was significantly correlated with the gene signature related with dendritic cell maturation (\(\rho =-0.51\), \(p=0.001\)). Mean ADC could be used as a biomarker of a gene signature associated with dendritic cell maturation and to assist in identifying patients with neural GBMs, known to be resistant to aggressive standard of care.


Apparent diffusion coefficient Glioblastoma multiforme Imaging genomics Immunotherapy 



Data used in this research were obtained from The Cancer Imaging Archive (TCIA) sponsored by the Cancer Imaging Program, DCTD/NCI/NIH. The results published here are in part based upon data generated by the The Cancer Genome Atlas (TCGA) Research Network:

Supplementary material

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Guido H. Jajamovich
    • 1
  • Chandni R. Valiathan
    • 1
  • Razvan Cristescu
    • 2
  • Sangeetha Somayajula
    • 1
  1. 1.Scientific InformaticsMerck Research LaboratoriesBostonUSA
  2. 2.Department of Genetics and PharmacogenomicsMerck Research LaboratoriesBostonUSA

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