Locoregional Radiogenomic Models Capture Gene Expression Heterogeneity in Glioblastoma

Radiogenomics mapping noninvasively determines important relationships between the molecular genotype and imaging phenotype of various tumors, allowing advances in both clinical care and cancer research. While early work has shown its technical feasibility, here we extend radiogenomic mapping to a locoregional level that can account for the molecular heterogeneity of tumors. To achieve this, our data processing pipeline relies on three main steps: 1) the use of multi-omics data fusion to generate a set of 100 interpretable gene modules, 2) the use of patch-based image analysis (specifically of contrast-enhanced T1-weighted weighted MR images) combined with Generalized Linear Models (GLM) to establish potential links between module expressions and local MR signal, and 3) the use of expression heatmaps based on GLMs decision values to explore visualization of tumor molecular heterogeneity. The performance of the proposed approach was evaluated using a leave-one-patient-out crossvalidation method as well as a separate validation data set. The top performing models were based on a small set of 20 features and yielded Area Under the receiver operating characteristic Curve (AUC) above 0.65 on the validation cohort for eight modules. Next, we demonstrate the clinical and biological interpretation of four modules using molecular expression heatmaps superimposed on clinical radiographic images, showing the potential for assessing tumor molecular heterogeneity and the utility of this method for precision treatment in clinical decision making and imaging surveillance.

[1]  S. Plevritis,et al.  Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. , 2014, Radiology.

[2]  R. Tibshirani,et al.  Pancancer analysis of DNA methylation-driven genes using MethylMix , 2015, Genome Biology.

[3]  Olivier Gevaert,et al.  Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer. , 2012, Cancer research.

[4]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[5]  Ferenc A. Jolesz,et al.  Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme , 2011, PloS one.

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

[7]  Aedín C. Culhane,et al.  GeneSigDB—a curated database of gene expression signatures , 2009, Nucleic Acids Res..

[8]  C. Kruchko,et al.  CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2005-2009. , 2012, Neuro-oncology.

[9]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

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

[11]  Ningyan Zhang,et al.  HER3/ErbB3, an emerging cancer therapeutic target. , 2015, Acta biochimica et biophysica Sinica.

[12]  Olivier Gevaert,et al.  Non–Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications , 2017, Radiology.

[13]  Leland S. Hu,et al.  Radiogenomics to characterize regional genetic heterogeneity in glioblastoma , 2016, Neuro-oncology.

[14]  Michael Spann,et al.  Image Segmentation and Uncertainty , 1987 .

[15]  J. Barnholtz-Sloan,et al.  CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2007-2011. , 2012, Neuro-oncology.

[16]  Olivier Gevaert,et al.  Identifying Master Regulators of Cancer and Their Downstream Targets by Integrating Genomic and Epigenomic Features , 2012, Pacific Symposium on Biocomputing.

[17]  Michael Unser,et al.  A Unifying Parametric Framework for 2D Steerable Wavelet Transforms , 2013, SIAM J. Imaging Sci..

[18]  Avi Ma'ayan,et al.  ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments , 2010, Bioinform..

[19]  K. Aldape,et al.  Identification of noninvasive imaging surrogates for brain tumor gene-expression modules , 2008, Proceedings of the National Academy of Sciences.

[20]  Subha Madhavan,et al.  Rembrandt: Helping Personalized Medicine Become a Reality through Integrative Translational Research , 2009, Molecular Cancer Research.

[21]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[22]  Andrew P. Stubbs,et al.  Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology. , 2009, Cancer research.

[23]  Chia-Chi Lin,et al.  Abstract 3725: Combined cytotoxic effects of volasertib and cisplatin in urothelial carcinoma cells , 2012 .

[24]  Joshua M. Korn,et al.  Comprehensive genomic characterization defines human glioblastoma genes and core pathways , 2008, Nature.

[25]  Michael Unser,et al.  Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification , 2017, IEEE Transactions on Image Processing.

[26]  Herbert Schulz,et al.  Neural Differentiation of Embryonic Stem Cells In Vitro: A Road Map to Neurogenesis in the Embryo , 2009, PloS one.

[27]  A. Gentles,et al.  Identification of an atypical etiological head and neck squamous carcinoma subtype featuring the CpG island methylator phenotype , 2017, EBioMedicine.

[28]  Graeme P. Penney,et al.  Retrospective Rigid Motion Correction in k-Space for Segmented Radial MRI , 2014, IEEE Transactions on Medical Imaging.

[29]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[30]  Yuri Kotliarov,et al.  High-resolution global genomic survey of 178 gliomas reveals novel regions of copy number alteration and allelic imbalances. , 2006, Cancer research.

[31]  R. Verhaak,et al.  Qki deficiency maintains stemness of glioma stem cells in suboptimal environment by downregulating endolysosomal degradation , 2016, Nature Genetics.

[32]  A. Brenner,et al.  Phase I Dose-Escalation Study of VB-111, an Antiangiogenic Virotherapy, in Patients with Advanced Solid Tumors , 2013, Clinical Cancer Research.

[33]  Olivier Gevaert,et al.  Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. , 2012, Radiology.

[34]  Adrien Depeursinge,et al.  Predicting Visual Semantic Descriptive Terms From Radiological Image Data: Preliminary Results With Liver Lesions in CT , 2014, IEEE Transactions on Medical Imaging.

[35]  Howard Y. Chang,et al.  Decoding global gene expression programs in liver cancer by noninvasive imaging , 2007, Nature Biotechnology.

[36]  Prateek Prasanna,et al.  Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma , 2018, Scientific Reports.

[37]  Olivier Gevaert,et al.  MethylMix: an R package for identifying DNA methylation-driven genes , 2015, Bioinform..

[38]  Mitchel S Berger,et al.  Neural stem cells and the origin of gliomas. , 2005, The New England journal of medicine.

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

[40]  Olivier Gevaert,et al.  MethylMix 2.0: an R package for identifying DNA methylation genes , 2018, Bioinform..

[41]  R. Gillies,et al.  Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction , 2016, Journal of magnetic resonance imaging : JMRI.

[42]  S. Plevritis,et al.  Identification of ovarian cancer driver genes by using module network integration of multi-omics data , 2013, Interface Focus.

[43]  J. Kuo,et al.  Activation of multiple ERBB family receptors mediates glioblastoma cancer stem-like cell resistance to EGFR-targeted inhibition. , 2012, Neoplasia.

[44]  P. A. Futreal,et al.  Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. , 2012, The New England journal of medicine.

[45]  P. Lambin,et al.  Defining the biological basis of radiomic phenotypes in lung cancer , 2017, eLife.

[46]  A. Gentles,et al.  NSD1 inactivation defines an immune cold, DNA hypomethylated subtype in squamous cell carcinoma , 2017, Scientific Reports.

[47]  T. Speed,et al.  Summaries of Affymetrix GeneChip probe level data. , 2003, Nucleic acids research.

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

[49]  Stephen M. Moore,et al.  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.

[50]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[51]  Robert J. Gillies,et al.  A microenvironmental model of carcinogenesis , 2008, Nature Reviews Cancer.

[52]  N. Pochet,et al.  Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response , 2017, bioRxiv.

[53]  Bart De Moor,et al.  A Framework for Elucidating Regulatory Networks Based on Prior Information and Expression Data , 2007, Annals of the New York Academy of Sciences.

[54]  A. Goldsmith,et al.  CaMoDi: a new method for cancer module discovery , 2014, BMC Genomics.

[55]  O. Gevaert,et al.  Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis , 2016, PloS one.

[56]  Subha Madhavan,et al.  G-DOC: a systems medicine platform for personalized oncology. , 2011, Neoplasia.