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The field of neuro-oncology would benefit tremendously from a refined molecular classification of diffuse gliomas that allows therapies to be directed at those signaling networks that drive tumor progression. Diffuse gliomas are uniformly fatal diseases that are treated in a largely unsuccessful manner with blunt instruments that target cell division (radiation therapy and chemotherapies) nonspecifically, rather than attacking the underlying mechanisms of disease onset and progression. It is becoming increasingly clear that diffuse gliomas should be recognized as a number of molecularly discrete diseases, each with its own tumor-promoting signaling networks that could be exploited. However, signaling networks are complex and need to be understood in the setting of histopathology, spatial characteristics, microenvironment, genetics, and gene expression patterns. An integrated analysis of these multi-scale data (micro-anatomic morphology, genomics, molecular networks, and clinical outcome) can lead to a better classification of glioma subtypes with the ultimate goal of therapeutic targeting of underlying mechanisms of tumor progression for each subtype.

The In Silico Brain Tumor Research Center (ISBTRC) was established to study brain tumors using innovative in silico experiments and explore novel ideas in brain tumor translational research. It is an integrated effort of four institutions: Emory University Woodruff Health Sciences Center, Henry Ford Hospital, Stanford University Center for Biomedical Ontology, and Thomas Jefferson University. The ISBTRC focuses on discovery through a series of hypothesis-driven research projects using publicly available data sources. The integrative in silico experiments at the ISBTRC are designed to leverage complementary molecular, pathology and radiology brain tumor data obtained from The Cancer Genome Atlas (TCGA) and Rembrandt studies as well as data obtained from partner institutions. The experiments involve workflows consisting of novel image analysis algorithms and bioinformatics analyses to extract imaging characteristics defined by features associated with vascular morphology and pathologic grade, and to correlate these characteristics with underlying gene expression profiles. The current work has led to results that demonstrate morphological subtypes of glioblastoma not previously recognized by pathologists. Using image analysis supported by high performance computing and data management middleware, the appearance of each of the hundreds of thousands of cells in a patient sample was described using 74 features representing shape and texture. By calculating summary statistics of appearance over a patient’s cells, a description or morphological signature of the patient’s tumor was developed. In a series of experiments using 480 slides from 167 patients, it was discovered that the signatures self-aggregate into distinct clusters. The survival characteristics of this morphology-driven stratification are statistically significant, suggesting that computationally extracted morphology descriptors can be significant predictors of prognosis.

Selected Publications

Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines, G Teodoro, T Pan, TM Kurc, J Kong, LAD Cooper, JH Saltz.  Parallel Computing, in press, 2013. 

A high-performance spatial database approach for pathology imaging algorithm evaluation,F Wang, J Kong, J Gao, LA Cooper, T Kurc, Z Zhou, D Adler, C Vergara-Niedermayr, B Katigbak, D Brat, J Saltz.  Journal of Pathology Informatics 4 (1), 5, 2013.

Accelerating Pathology Image Data Cross Comparison on CPU/GPU Hybrid Systems, K. Wang, Y. Huai, R. Lee, F. Wang, X. Zhang, and Joel H. Saltz.  accepted to the 38th International Conference on Very Large Databases (VLDB 2012), Istanbul, Turkey, August 27 - 31.

High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms, G Teodoro, T Pan, T Kurc, J Kong, L Cooper, N Podhorszki, S Klasky, and J Saltz.  27th IEEE International Parallel & Distributed Processing Symposium (IPDPS), Boston, USA, accepted for publication, 2013. 

Feature-based Analysis of Large-scale Spatio-Temporal Sensor Data on Hybrid Architectures, J Saltz, G Teodoro, T Pan, L Cooper, J Kong, S Klasky, T Kurc. International Journal of High Performance Computing, in press, 2013. 

Enabling ontology based semantic queries in biomedical database systems, S Zheng, F Wang, J Lu, J Saltz.  Proceedings of the 21st ACM international conference on Information and knowledge management, pp. 2651-2654, 2012. 

The Tumor Microenvironment Strongly Impacts Master Transcriptional Regulators and Gene Expression Class of Glioblastoma, Lee A.D. Cooper, David A. Gutman, Candace Chisolm, Christina Appin,Jun Kong, Yuan Rong, Tahsin Kurc, Erwin G. Van Meir, Joel H. Saltz, Carlos S. Moreno,and Daniel J. Brat, American Journal of Pathology, Vol. 180, No. 5, pp 2108-2119,   DOI: 10.1016/j.ajpath.2012.01.040 (See related Commentary on page 1768)

Integrated morphologic analysis for the identification and characterization of disease subtypes, Lee A D Cooper, Jun Kong, David A Gutman, Fusheng Wang, Jingjing Gao, Christina Appin, Sharath Cholleti,Tony Pan, Ashish Sharma, Lisa Scarpace, Tom Mikkelsen, Tahsin Kurc, Carlos S Moreno, Daniel J Brat, Joel H Saltz, J Am Med Inform Assoc 19(2), 317-323, 2012. doi:10.1136/amiajnl-2011-000700

A proprotein convertase/MMP-14 proteolytic cascade releases a novel 40 kDa vasculostatin from tumor suppressor BAI1, SM Cork, B Kaur, NS Devi, L Cooper, JH Saltz, EM Sandberg, S Kaluz, and EG Van Meir,  Oncogene, pp. 1-9, 2012.

A Data Model and Database for High-resolution Pathology Analytical Image Informatics Fusheng Wang, Jun Kong, Lee Cooper, Tony Pan, Tahsin Kurc, Wenjin Chen, Ashish Sharma, Cristobal Niedermayr, Tae W. Oh, Daniel Brat, Alton B. Farris, David Foran, Joel Saltz, Journal of Pathology Informatics, Vol. 2, Issue 1, pp. 32-40, 2011.

Posters and Abstracts:

MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set, D Gutman, L Cooper, S Hwang, C Holder, J Gao, T Aurora, et al.. Radiology, Radiological Society of North America, 2013. 

Machine-Based Classification of Oligodendroglioma component in Glioblastoma using large-scale microscopic analysis uncovers Oligodendroglial molecular signatures, Jun Kong, et .al..  Presented at The Cancer Genome Atlas' 2nd Annual Scientific Symposium: Enabling Cancer Research Through TCGA, 2012

Machine-Based Classification of Oligodendroglioma component in Glioblastoma using large-scale microscopic analysis uncovers Oligodendroglial molecular signatures, David  Gutman, et al.. Presented at The Cancer Genome Atlas' 2nd Annual Scientific Symposium: Enabling Cancer Research Through TCGA, 2012

The Cancer Digital Slide Archive: An online resource for integrative TCGA pathology, David Gutman and  Lee Cooper. Presented at The Cancer Genome Atlas' 2nd Annual Scientific Symposium: Enabling Cancer Research Through TCGA, 2012

Tumor-Infiltrating Lymphocytes in Glioblastoma are associated with mutations in NF1, RB1, and TP53 and Enriched in the mesenchymal transcriptional class, Caleb Rutledge, et al... Presented at The Cancer Genome Atlas' 2nd Annual Scientific Symposium: Enabling Cancer Research Through TCGA, 2012

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