1. PD: Joel Saltz, MD, PhD PI: Daniel J. Brat MD, PhD Emory University School of Medicine In Silico Center for Brain Tumor Research

2. Mechanisms Underlying Glioma Progression 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. 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

5. OligodendrogliomaAstrocytoma Nuclear Qualities

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

7. In Silico Center for Brain Tumor Research 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

8. 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.

9. 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 Brain Tumor Research

10. Leveraging caBIG Imaging Infrastructure Management and Integration of Imaging Data 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: tools, libraries and applications that bridge unique medical model of DICOM, caGrid and other non-specific radiology software and systems. Tony Pan Tahsin Kurc

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

12. 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.

13. TCGA Research Network Digital Pathology Neuroimaging

14. 3 Gene Families are Altered in GBM: RTK, p53 and RB

15. TCGA Gene Expression Families

16. Genetic Alterations within Expression Families

17. TCGA Neuropathology Attributes 120 TCGA specimens; 3 Reviewers Presence and Degree of: Microvascular hyperplasia  Complex/glomeruloid  Endothelial hyperplasia Necrosis  Pseudopalisading pattern  Zonal necrosis Inflammation  Macrophages/histiocytes  Lymphocytes  Neutrophils Differentiation:  Small cell component  Gemistocytes  Oligodendroglial  Multi-nucleated/giant cells  Epithelial metaplasia  Mesenchymal metaplasia Other Features  Perineuronal/perivascular satellitosis  Entrapped gray or white matter  Micro-mineralization

18. Specific Aims: 1.Influence of necrosis/ hypoxia on gene expression and genetic classification. 2. Molecular correlates of high resolution nuclear morphometry. 3.Gene expression profiles that predict glioma progression. 4. Molecular correlates of MRI enhancement patterns. In Silico Center for Brain Tumor Research

19. Web based data portal Available at : portal.insilicoscience.org Allows research team to efficiently find all data for a given subject David Gutman

20. Current Data Summary Distinct Samples Total samples Data type 334505AgilentG4502AAgilent Human Genome Microarray 244A _1 and _2' 26 H-miRNA_8x15KAgilent Human miRNA Microarray 3302225ht_hg_u133aht_hg_u133a gene chip 261816OMA002Illumina DNA Methylation OMA002 Cancer Panel 1 246542OMA003Illumina DNA Methylation OMA002 Cancer Panel 1 121122path_annotation A Neuropathologist has manually annotated 18 features from this series 289 rbt_U133P2_dataRembrandt U133P2 Chip Data 135411svs_filesWhole Slide TCGA Aperio Slide Images 73 tcga_MRI_dataMRI Data for TCGA project from NBIA Portal

21. 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

22. 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?

23. GBM: % Necrosis

24. TCGA: GBM Frozen Sections 179 cases were assessed for % necrosis on frozen section slides for TCGA quality assurance. Cox-based regression analysis for % necrosis vs. gene expression (795 probe sets; 647 distinct genes)

25. Network Analysis based on % Necrosis Carlos Moreno David Gutman

26. Network Analysis based on % Necrosis

27. 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.

28. Nuclear Segmentation TCGA Whole Slide Images Jun Kong

29. Oligodendroglioma Astrocytoma Nuclear Qualities Class Assignment 110

32. Feature Extraction TCGA Whole Slide Images Jun Kong

33. In Silico Research of Glial Neoplasms Astrocytoma Oligodendroglioma

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

35. Whole slide scans from 14 TCGA GBMS (69 slides) 7 purely astrocytic in morphology; 7 with 2+ oligo component 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

36. Nuclear Feature Analysis: TCGA Using the parallel computation infrastructure of Sun Grid Engine, we analyzed image tiles of 4096x4096 of 213 whole- slide TCGA images of permanent tissue sections. Approximately 90 million nuclei segmented. 79 patients:57 are diagnosed as GBM (‘oligo 0’) 17 are classified as GBM with ‘oligo 1’, 5 as GBM with ‘oligo 2+’. With each data file including all nuclear features from one patient, all nuclei were classified with color blue, green, and red representing nuclei scored as 1~3, 4~6, and 7~10, respectively.

37. The ratio of nuclei classified ≤ 5 to >5 was computed for each 79 patients. Ratios associated with ‘oligo 0’ and ‘oligo 2+’ patient populations were compared with two-sample t-test (p=0.0145) Nuclear Feature Analysis: TCGA

38. Discriminating Features (Grade 1 vs. Grade 7-10)

41. Imaging Pathology Molecular Time 1 – 8 yrs Aim 3) Examine gene expression profiles of low grade gliomas that progress to GBM for predictive clustering and correlates with pathologic and radiologic features.

42. ClassicalProneuralNeuralMesenchymal Hierarchical clustering of 176 Rembrandt samples using TCGA classification genes defines four major subtypes. Lee Cooper and Carlos Moreno

43. 75 lower-grade gliomas in REMBRANDT (p < 0.0003). Lee Cooper Carlos Moreno Predicting Recurrence/Survival 43 oligodendrogliomas in REMBRANDT (p < 0.0002).

44. 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

45. Neuroimaging Correlates Define relationship between contrast-enhancement, perfusion and permeability with vascular changes Correlate MR characteristics defined by the Vasari Feature Set with pathologic grade, vascular morphology and gene expression profiles

46. Angiogenesis Segmentation H&E Image Color Deconvolution Hematoxylin Image Eosin Image Eosin Image Spatial Norm. Density Image Density Calculation Boundary Smoothing Density Image Object ID Segmented Vessels Eosin intensity image Angiogenic Segmentation

47. States of Angiogenesis Endothelial Hypertrophy Complex Microvascular Hyperplasia Endothelial Hyperplasia Lee Cooper Sharath Cholleti

48. Vessel Characterization Bifurcation detection

49. 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

50. Thank You! Funding Support: caBIG In Silico Center for Brain Tumor Research