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 Copy number DNA Methylation Agilent & Affy microarray RNA sequencing Copy number Affy SNP 6.0 DNA Methylation Agilent Infinium (27k) Mutation DNA sequencing Medical Images (MRI)

Method Two step algorithm Generating the List of Candidate Drivers Associating Candidate Drivers with their Downstream Targets Step 2: Using an idea that was developed at Stanford by Daphne called module networks

Step 1 Generating the List of Candidate Drivers If gene expression can be explained by genomic events Copy Nr Methylation Mutation Gene candidate driver gene Expression Rationale: Genes driven by multiple genomic events in a significant subset of samples are unlikely to be randomly deregulated. De redenering hier is dat het heel onwaarschijnlijk is dat als een gen significant wordt beinvloed door zowel copy number, methylatie als mutatie, dat dit toevallig gebeurd.

Step 1 Generating the List of Candidate Drivers Gene expression – DNA methylation correlation Gene expression – Copy number correlation Model gene expression as a function of Copy number DNA methylation Mutation data Incorporating prior knowledge β1 has to be positive β2 has to be negative Pairwise Spearman correlation

Step 2: Associating Candidate Drivers with their Downstream Targets genes patients Driver genes from Step 1 Known transcription factors clustering clusters Potential Regulators Ri Module FOXM1 Cluster 37 E2F8 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 RBMS1, PLAGL1, MAML2, PNRC1 are part of module 74 and inhibiting module 18 Gevaert O and Plevritis S. PSB 2013

Results Step 2: Module network GBM Gevaert O and Plevritis S. PSB 2013

GBM Network Top DNA repair module DNMT1 PARP1 CHAF1B 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 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 downstream targets allow to check for enrichment analysis and annotate the regulator genes 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 … Microarrary allows to measure the expression in human genome. (25000 genes)

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

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 Computational features iPAD

Quantitative image features Texture Features Shape Features Edge Sharpness Features Used curvature along the boundary difference in intensities of tissue surrounding the tumor and the tumoritself Window (W) Scale (S) Gabor Filter Bank 150 Computational features Jiajing Xu, Sandy Napel

Quantitative image features Computational features iPAD 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 Enhancement 0.0030232 1.6809 1.1925 2.3692 Edge sharpness window kurtosis Necrosis 0.010502 1.4889 1.0976 2.0196 Compactness 0.018057 1.4306 1.0632 1.925 Edge sharpness window skewness 0.023249 1.4155 1.0485 1.9111 LAII std-5R 0.023539 1.4792 1.0541 2.0757 LAII std-8R 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 0.025448 1.4692 1.0484 2.059 0.026997 1.4695 1.0448 2.0669 RDS mean 0.034709 1.4878 1.029 2.1514 0.037068 1.4827 1.0239 2.147 0.037589 1.4493 1.0215 2.0562

Creating a 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