1. The Contribution of caBIG and In Silico Resources to Glioblastoma Research Daniel J. Brat MD, PhD Department of Pathology and Laboratory Medicine Emory University School of Medicine Atlanta, Georgia

2. Why do gliomas progress biologically and clinically? Classification Biology of Progression caBIG and In Silico Research

3. Diffuse Gliomas: 2007 WHO Classification Astrocytomas Infiltrating Astrocytoma (WHO grade II) Anaplastic Astrocytoma (WHO grade III) Glioblastoma (WHO grade IV) Oligodendrogliomas Oligodendroglioma (WHO grade II) Anaplastic Oligodendroglioma (WHO grade III) Mixed Oligoastrocytomas Oligoastrocytoma (WHO grade II) Anaplastic Oligoastrocytoma (WHO grade III)*

4. OligodendrogliomaAstrocytoma Nuclear Qualities

5. Distinguishing Among the Gliomas “There are also many cells which appear to be transitions between gigantic oligodendroglia and astrocytes. It is impossible to classify them as belonging in either group” Bailey P, Bucy PC. Oligodendrogliomas of the brain. J Pathol Bacteriol 1929: 32:735

6. Progression to Glioblastoma GBM is the highest grade astrocytoma (WHO grade IV). Mean survival is 60 weeks following standard treatment. Survival = 14 weeks following resection alone. Lower grade astrocytomas (i.e. WHO grade II and III) are also ultimately fatal, but have slower growth rates.

7. Progression to GBM Anaplastic Astrocytoma (WHO grade III) Glioblastoma (WHO grade IV)

8. Anaplastic Astrocytoma, WHO grade III

9. Glioblastoma Pseudopalisading necrosisMicrovascular hyperplasia

10. In Silico Center for Translational Neuro-oncology Informatics Joel Saltz, MD, PhD : Director Daniel Brat, MD, PhD : PI Adam Flanders, MD : Radiology Lead Tahsin Kurc, PhD: Chief Architect Ashish Sharma, PhD: Imaging Informatics Lead

11. Biomedical Informatics Requirements Radiology Imaging Patient Outcome Pathologic Features “Omic” Data Incorporates Digital Pathology, Radiology, “Omics” data, patient outcome Exploits synergies between initiatives to improve ability to forecast survival & treatment response.

12. Key Data Sets REMBRANDT: Gene expression and genomics data set of all glioma subtypes The Cancer Genome Atlas (TCGA): Rich “omics” set of GBM, digitized Pathology and Radiology Vasari Feature Set: Standardized annotation of gliomas of all subtypes In Silico Center for Translational Neuro-oncology Informatics

13. Leverage caBIG Imaging Infrastructure Free, Open Source Components for Management and Integration of Imaging in Clinical Research Visualization Annotations And Markup Transfer Standards eXtensible Imaging Platform (XIP): easily extensible open source platform providing development and implementation support. The National Biomedical Imaging Repository (NBIA): Searchable repository of in vivo cancer images Archive AIM Data Service at Emory Grid –based management of Annotation and Image Markup for Pathology and Radiology Images Annotations and Imaging Markup Developer (AIM): XML standard for medical image annotation and markup Imaging Core Middleware: a set of tools, libraries and applications that bridge unique medical model of DICOM, caGrid and other non-specific radiology software and systems.

14. caMicroscope Analytical service that pulls images from the image server and executes MATLAB programs on a cluster caGrid based with IVI imaging middleware bulk data transport The Client allows multi- resolution browsing and markup of the images, invoke MATLAB programs and examine the MATLAB output images Successor to the Maryland/Hopkins Virtual Microscope (c 1997); Active Proxy- G (c 2000)

15. The Cancer Genome Atlas (TCGA) “Catalog and discover major cancer-causing genome alterations in large cohorts of human tumors through integrated multidimensional analysis.” Gene expression, copy number, sequencing, promoter methylation, miRNA Whole slide digitized images for 120 cases. Target of 500 GBMs, not yet completed.

16. TCGA Research Network Digital Pathology Neuroimaging

17. TCGA Neuropathology Criteria Attributes that Relate to Entire Specimen Roughly 120 TCGA specimens; 3 Reviewers Presence and Degree of: Microvascular hyperplasia elements  Complex/glomeruloid  Circumferential endothelial hyperplasia Necrosis  Pseudopalisading pattern  Zonal necrosis Inflammation  Macrophages/histiocytes  Lymphocytes  Neutrophils Differentiation:  Small cell component  Gemistocytes  “Oligodendroglioma-like”  Multi-nucleated/giant cells  Epithelial metaplasia  Mesenchymal metaplasia Other Features  Perineuronal and/or perivascular satellitosis  Entrapped gray matter  Entrapped white matter  Micro-mineralization

18. 3 Gene Families are Altered in GBM: RTK, p53 and RB

19. TCGA Gene Expression Families

20. Somatic mutations within Expression Families

21. In Silico Center for Translational Neuro-oncology Informatics Specific Aims: 1)Influence of necrosis-hypoxia on gene expression profiling and genetic classification. 2)Molecular correlates of high resolution nuclear morphometry. 3)Gene expression profiles of low grade gliomas that progress to GBM 4)Molecular correlates of MRI enhancement patterns.

22. In Silico Center for Translational Neuro-oncology Informatics: Aims Aim 1) Determine the influence of tumor micro- environment on gene expression profiling and genetic classification using TCGA data. Necrosis = Severe Hypoxia Microvascular Hyperplasia GBM

23. GBM: necrosis, hypoxia, angiogenesis and gene expression Does the presence or degree of necrosis within digitized frozen section slides correlate with specific gene expression patterns or determine algorithm- based unsupervised clustering of GBMs gene expression categories? Does the presence or degree of necrosis influence the type of angiogenesis or pro-angiogenic gene expression patterns within human gliomas?

24. Correlate nuclear shape and texture features of gliomas to genetics and gene expression defined by Rembrandt and TCGA data sets. Define specific features that carry genetic and prognostic weight. Aim 2) Determine molecular correlates of high resolution nuclear morphometry of gliomas using Rembrandt and TCGA datasets.

25. Nuclear Segmentation Feature Extraction TCGA Whole Slide Images Class Assignment

26. In Silico Research of Glial Neoplasms Astrocytoma Oligodendroglioma

27. Whole slide scans from 15 TCGA GBMS (69 slides) 8 pure astrocytomas; 7 with oligo components 399,233 nuclei analyzed for astro/oligo features Cases were categorized based on ratio of oligo/astro cells Machine-based Classification of TCGA GBMs (J Kong) TCGA Gene Expression Query: c-Met overexpression Separation: p =1.4 X 10 -22

28. Nuclear Qualities Which features carry most prognostic significance? Which features correlate with genetic alterations?

29. Aim 3) Examine gene expression profiles of low grade gliomas that progress to GBM for predictive clustering and correlates with pathologic and radiologic features. Time to progression 0.5 – 8 yrs

30. Imaging Pathology Molecular Time

31. Predicting Recurrence Matched sets of low grade gliomas and GBMs that progressed that progressed from them Henry Ford cases in Rembrandt data set Examine the gene expression profile of low grade gliomas for predictive clustering, prognostic significance and correlates with pathologic and radiologic features.

32. Aim 4. Identify correlates of MRI enhancement patterns in astrocytic neoplasms with underlying vascular changes and gene expression profiles. No enhancement Normal Vessels Stable lesion ? Rim-enhancement Vascular Changes Rapid progression

33. Neuroimaging Correlates Define the precise relationship between imaging characteristics related to contrast-enhancement and those seen by advanced imaging sequences with the underlying histopathologic features, especially those associated with vascular changes Correlate specific MR characteristics defined by the Vasari Feature Set with pathologic grade, vascular morphology and underlying gene expression profiles

34. Use of caBIG Tools at Emory In Silico Brain Tumor Research Center Radiology Workstations (Osirix iPAD, XIP/AVT, ClearCanvas) NBIA TCGA Portal Pathology Workstations (caMicroscope, Aperio ImageScope) Extended AIME Service Enterprise Architecture 2.0 caB2B caIntegrator 2 CBIIT Research Team Carl Schaefer Jinghui Zhang ICR VCDE/ ARCH CIP: Research & Clinical Trials In vivo Imaging TBPT Pathology Image Archive Science TCGA Portal

35. In Silico Center for Translational Neuro-oncology Informatics Thank You! Funding Support: caBIG TCGA NINDS NCI