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Radiogenomics in glioblastoma multiforme Olivier Gevaert, PhD Stanford University Cancer Center for Systems Biology Information Sciences in Imaging Program Overview Multi-omics data integration Quantitative image features in GBM Integrating quantitative image features and module networks Multi-omics data integration Discovering cancer driver genes and their targets Integrative methods • Need to develop statistical methods that allow to integrate multi-omics cancer data • Create mechanistic models of cancer – how is gene expression influenced by genomic events – how to identify cancer drivers and their targets • Public domain data – Gene & miRNA expression • Agilent & Affy microarray • RNA sequencing – Copy number • Affy SNP 6.0 – DNA Methylation • Agilent Infinium (27k) – Mutation • DNA sequencing – Medical Images (MRI) 5 Method • Two step algorithm 1. Generating the List of Candidate Drivers 2. Associating Candidate Drivers with their Downstream Targets Step 1 Generating the List of Candidate Drivers • If gene expression can be explained by genomic events candidate driver gene Rationale: Genes driven by multiple genomic events in a significant subset of samples are unlikely to be randomly deregulated. 7 Copy Nr Methylation Mutation Expression Gene Step 1 Generating the List of Candidate Drivers • Model gene expression as a function of Gene Gene expression expression – DNA – Copy methylation number correlation – Copy number – DNA methylation – Mutation data Ei = F ( b1CNi + b2 Methyli + b3 Muti ) • Incorporating prior knowledge – β1 has to be positive – β2 has to be negative Pairwise Spearman correlation 8 Step 2: Associating Candidate Drivers with their Downstream Targets Driver genes from Step 1 Known transcription factors genes patients clustering clusters Potential Regulators Ri Expressioncluster-i = F (a1R1 + a 2R2 + ...+ a n Rn ) E2F8 FOXM1 Module Cluster 37 Linear Regression + Lasso regularization Lee et al. PLoS Genetics, 2009 Results Step 2: Module network GBM Gevaert O and Plevritis S. PSB 2013 Results Step 2: Module network GBM Gevaert O and Plevritis S. PSB 2013 Results Step 2: Module network GBM 12 Gevaert O and Plevritis S. PSB 2013 GBM Network Top DNA repair module • DNMT1 – Key DNA methylation driver gene • PARP1 – binds DNMT1 – known function in DNA damage • CHAF1B – putative gene involved in DNA repair – cross-cancer • Top regulator in ovary & breast for DNA repair 13 Gevaert O and Plevritis S. PSB 2013 Summary • Integration of multi-omics GBM data incorporating – gene expression – DNA methylation – copy number data • Reduces molecular data dimensions – reduces the multiple testing correction problem • Rich model going beyond clustering Gevaert O and Plevritis S. PSB 2013 Quantitative image features Tissue level data GBM Medical Image Data • 152 TCGA GBM patients have image data • Imaging data is very heterogeneous – different planes – noisy annotation – different pulse sequences • T1 pre gadolinium • T1 post gadolinium •… GBM Medical Image Data GBM Medical Image Data • AXIAL images – T1 pre gadolinium – T1 post gadolinium – T2 FLAIR • Highlight different parts of the tumor – necrosis – enhancement – edema • Manual annotation of ROIs for these concepts – 2D – largest slice for that lesion – for multifocal lesions ROI for each 2; < 8=>?=>355' 23'/ $, -'+) &$(4%41. ' 25'6789: ' B "#*$, 4, ' B "#*$, 4, ' B "#*$, 4, ' @%A) %#". "%-' @&". ) ' GBM Medical Image Data • Results – at least one ROI for 55 patients – ~40 patients have all ROI types • Three ROI types are matched according to location to create a super ROI • If multiple super ROIs, features are combined • Two readers + some redundancy to estimate intra-reader variability Quantitative image features • Create a set of quantitative image features for each ROI • Same feature set as Lung Cancer project Gevaert et al. Radiology 2012 iPAD Computational features Quantitative image features Texture Features Shape Features Edge Sharpness Features Scale (S) Gabor Filter Bank 150 Computational features Window (W) Jiajing Xu, Sandy Napel Quantitative image features iPAD Computational features computational features Texture Feature Shape Feature Edge Sharpness 150-element feature vector Quantitative image features • Focused on 28 highly interpretable quantitative image features – Compactness of ROI – Edge sharpness – Edge Shape (LAII) Explorative analysis Size metrics of necrosis, enhancement and edema • Created ratios of the size of each ROI vs. larger ROI – necrosis/enhancement – necrosis/edema – enhancement/edema Size metrics of necrosis, enhancement and edema • Comparison with the VASARI features Size metrics of necrosis, enhancement and edema • Comparison with the VASARI features Size metrics of necrosis, enhancement and edema • Comparison with the VASARI features Size metrics of necrosis, enhancement and edema • Correlated these with overall & progression free survival – no significant correlation with any survival outcome • Potential problems – small data set, not enough power (<55 samples) – how to combine multi-focal lesions • multi-focal necrosis • multi-focal enhancement – 2D vs. 3D • Interesting – size of edema is weakly correlated with progression free survival (p-value 0.03) Univariate survival analysis Quantitative image feature ROI type Wald Test HR HR lower HR upper RDS std Edge sharpness window kurtosis Enhancement Necrosis 0.0030232 0.010502 1.6809 1.4889 1.1925 1.0976 2.3692 2.0196 Compactness Enhancement 0.018057 1.4306 1.0632 1.925 Edge sharpness window skewness Necrosis 0.023249 1.4155 1.0485 1.9111 LAII std-5R Enhancement 0.023539 1.4792 1.0541 2.0757 LAII std-8R Enhancement 0.024792 1.5828 1.06 2.3636 Edge Sharpness scale max Edema 0.025168 1.49 1.0509 2.1125 Edge sharpness scale mean Edema 0.025448 1.4692 1.0484 2.059 LAII std-5R RDS mean Compactness LAII std-8R Edema Edema Necrosis Edema 0.026997 0.034709 0.037068 0.037589 1.4695 1.4878 1.4827 1.4493 1.0448 1.029 1.0239 1.0215 2.0669 2.1514 2.147 2.0562 Creating a radiogenomic map Radiogenomic map Radiogenomics map Necrosis Radiogenomics map Necrosis • Compactness of Necrosis ROI – high = irregular shape – low = spherical shape • Correlated with Module 64 – P-value 0.0021 (Spearman rho, FDR 4%) – Inverse correlation Low compactness High compactness Radiogenomics map Necrosis • Edge sharpness window necrosis – high = blurry edge – low = sharp edge • Correlated with Module 10 – P-value 0.0178 – Inversely correlated Sharpe edge Blurry edge Overall summary • Developed module network method that integrates/summarizes multi-omics data • Gathered quantitative image features from MRI image data • Correlated quantitative image features with modules Acknowledgements • Sylvia Plevritis • Greg Zaharchuk • Sandy Napel • • • • Lex Mitchell Caroline Yu Jiajing Xu Chris Beaulieu Questions • Pulse sequence annotations • T1 Post is missing in many samples • Readers interested in annotating ROIs