Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

Background Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. Methods We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). Results We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). Conclusion MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.

[1]  Mitchel S Berger,et al.  Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging. , 2012, Neuro-oncology.

[2]  Francisco M. De La Vega,et al.  Genome and Transcriptome Sequencing in Prospective Metastatic Triple-Negative Breast Cancer Uncovers Therapeutic Vulnerabilities , 2012, Molecular Cancer Therapeutics.

[3]  Teresa Wu,et al.  Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma , 2015, PloS one.

[4]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[5]  Paul S Mischel,et al.  Relationship between gene expression and enhancement in glioblastoma multiforme: exploratory DNA microarray analysis. , 2008, Radiology.

[6]  V. P. Collins,et al.  Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics , 2013, Proceedings of the National Academy of Sciences.

[7]  S. Coons,et al.  Regional heterogeneity in the DNA content of human gliomas , 1993, Cancer.

[8]  K. Polyak,et al.  Intra-tumour heterogeneity: a looking glass for cancer? , 2012, Nature Reviews Cancer.

[9]  H. Fine,et al.  Many tumors in one: a daunting therapeutic prospect. , 2011, Cancer cell.

[10]  William D. Dunn,et al.  MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. , 2013, Radiology.

[11]  A. Gupta,et al.  Pretreatment Dynamic Susceptibility Contrast MRI Perfusion in Glioblastoma: Prediction of EGFR Gene Amplification , 2015, Clinical Neuroradiology.

[12]  Eric W. Klee,et al.  Integrated Genomic Characterization Reveals Novel, Therapeutically Relevant Drug Targets in FGFR and EGFR Pathways in Sporadic Intrahepatic Cholangiocarcinoma , 2014, PLoS genetics.

[13]  W. Cavenee,et al.  Heterogeneity maintenance in glioblastoma: a social network. , 2011, Cancer research.

[14]  Ru-Fang Yeh,et al.  Glioblastoma multiforme regional genetic and cellular expression patterns: influence on anatomic and physiologic MR imaging. , 2010, Radiology.

[15]  Sylvia Drabycz,et al.  An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging , 2010, NeuroImage.

[16]  Tej D. Azad,et al.  Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities , 2015, Science Translational Medicine.

[17]  John R. Durkin,et al.  Registration of Magnetic Resonance Image Series for Knee Articular Cartilage Analysis , 2013, Cartilage.

[18]  Catherine Dumur,et al.  Microarray Analysis of MRI-defined Tissue Samples in Glioblastoma Reveals Differences in Regional Expression of Therapeutic Targets , 2006, Diagnostic molecular pathology : the American journal of surgical pathology, part B.

[19]  Jingping Xie,et al.  Assessing tumor cytoarchitecture using multiecho DSC‐MRI derived measures of the transverse relaxivity at tracer equilibrium (TRATE) , 2015, Magnetic resonance in medicine.

[20]  Yonatan Aumann,et al.  Efficient Calculation of Interval Scores for DNA Copy Number Data Analysis , 2005, RECOMB.

[21]  B. Frieden,et al.  Adaptive therapy. , 2009, Cancer research.

[22]  Christopher Nimsky,et al.  Gliomas: histopathologic evaluation of changes in directionality and magnitude of water diffusion at diffusion-tensor MR imaging. , 2006, Radiology.

[23]  Scott N. Hwang,et al.  Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. , 2014, Radiology.

[24]  W. Shapiro,et al.  Isolation, karyotype, and clonal growth of heterogeneous subpopulations of human malignant gliomas. , 1981, Cancer research.

[25]  P. LaViolette,et al.  Validation of functional diffusion maps (fDMs) as a biomarker for human glioma cellularity , 2010, Journal of magnetic resonance imaging : JMRI.

[26]  J. Debbins,et al.  Correlations between Perfusion MR Imaging Cerebral Blood Volume, Microvessel Quantification, and Clinical Outcome Using Stereotactic Analysis in Recurrent High-Grade Glioma , 2012, American Journal of Neuroradiology.

[27]  Rebecca A Betensky,et al.  Mosaic amplification of multiple receptor tyrosine kinase genes in glioblastoma. , 2011, Cancer cell.

[28]  Leland S. Hu,et al.  Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival , 2012, Neuro-oncology.

[29]  S. R. Shepard,et al.  Improved Delineation of Glioma Margins and Regions of Infiltration with the Use of Diffusion Tensor Imaging: An Image-Guided Biopsy Study , 2008 .

[30]  S. Choi,et al.  Cerebral Blood Volume Calculated by Dynamic Susceptibility Contrast-Enhanced Perfusion MR Imaging: Preliminary Correlation Study with Glioblastoma Genetic Profiles , 2013, PloS one.

[31]  D. Haussler,et al.  The Somatic Genomic Landscape of Glioblastoma , 2013, Cell.

[32]  D. Demetrick,et al.  The Use of Magnetic Resonance Imaging to Noninvasively Detect Genetic Signatures in Oligodendroglioma , 2008, Clinical Cancer Research.

[33]  Sanjeev Chawla,et al.  Use of magnetic perfusion-weighted imaging to determine epidermal growth factor receptor variant III expression in glioblastoma. , 2012, Neuro-oncology.

[34]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Andreas Holzinger,et al.  Data Mining with Decision Trees: Theory and Applications , 2015, Online Inf. Rev..

[36]  Tracy T Batchelor,et al.  Magnetic Resonance Imaging Characteristics Predict Epidermal Growth Factor Receptor Amplification Status in Glioblastoma , 2005, Clinical Cancer Research.

[37]  Juan J. Martinez,et al.  Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma. , 2015, Medical physics.

[38]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[39]  K S Panageas,et al.  Statistical issues in analysis of diagnostic imaging experiments with multiple observations per patient. , 2001, Radiology.