Center for Comprehensive Informatics

Atlanta, GA, United States

Center for Comprehensive Informatics

Atlanta, GA, United States

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Caleb Rutledge W.,Emory University | Kong J.,Center for Comprehensive Informatics | Gao J.,Center for Comprehensive Informatics | Gutman D.A.,Center for Comprehensive Informatics | And 19 more authors.
Clinical Cancer Research | Year: 2013

Purpose: Tumor-infiltrating lymphocytes (TIL) have prognostic significance in many cancers, yet their roles in glioblastoma have not been fully defined. We hypothesized that TILs in glioblastoma are associated with molecular alterations, histologies, and survival. Experimental Design: We used data from The Cancer Genome Atlas (TCGA) to investigate molecular, histologic, and clinical correlates of TILs in glioblastomas. Lymphocytes were categorized as absent, present, or abundant in histopathologic images from 171 TCGA glioblastomas. Associations were examined between lymphocytes and histologic features, mutations, copy number alterations, CpG island methylator phenotype, transcriptional class, and survival. Wevalidated histologic findings usingCD3Ggene expression. Results: Wefound a positive correlation between TILs and glioblastomas with gemistocytes, sarcomatous cells, epithelioid cells, and giant cells. Lymphocytes were enriched in the mesenchymal transcriptional class and strongly associated with mutations in NF1 and RB1. These mutations are frequent in the mesenchymal class and characteristic of gemistocytic, sarcomatous, epithelioid, and giant cell histologies. Conversely, TILs were rare in glioblastomas with small cells and oligodendroglioma components. Lymphocytes were depleted in the classical transcriptional class and in EGF receptor (EGFR)-amplified and homozygous PTEN-deleted glioblastomas. These alterations are characteristic of glioblastomas with small cells and glioblastomas of the classical transcriptional class. No association with survival was shown. Conclusions: TILs were enriched in glioblastomas of the mesenchymal class, strongly associated with mutations in NF1 and RB1 and typical of histologies characterized by these mutations. Conversely, TILs were depleted in the classical class, EGFR-amplified, and homozygous PTEN-deleted tumors and rare in histologies characterized by these alterations. © 2013 American Association for Cancer Research.


Cooper L.A.D.,Center for Comprehensive Informatics | Cooper L.A.D.,Emory University | Kong J.,Emory University | Gutman D.A.,Emory University | And 13 more authors.
IEEE Transactions on Biomedical Engineering | Year: 2010

The integration of imaging and genomic data is critical to forming a better understanding of disease. Large public datasets, such as The Cancer Genome Atlas, present a unique opportunity to integrate these complementary data types for in silico scientific research. In this letter, we focus on the aspect of pathology image analysis and illustrate the challenges associated with analyzing and integrating large-scale image datasets with molecular characterizations. We present an example study of diffuse glioma brain tumors,where themorphometric analysis of 81 million nuclei is integrated with clinically relevant transcriptomic and genomic characterizations of glioblastoma tumors. The preliminary results demonstrate the potential of combining morphometric and molecular characterizations for in silico research. © 2010 IEEE.


PubMed | Center for Comprehensive Informatics
Type: Journal Article | Journal: IEEE transactions on bio-medical engineering | Year: 2010

The integration of imaging and genomic data is critical to forming a better understanding of disease. Large public datasets, such as The Cancer Genome Atlas, present a unique opportunity to integrate these complementary data types for in silico scientific research. In this letter, we focus on the aspect of pathology image analysis and illustrate the challenges associated with analyzing and integrating large-scale image datasets with molecular characterizations. We present an example study of diffuse glioma brain tumors, where the morphometric analysis of 81 million nuclei is integrated with clinically relevant transcriptomic and genomic characterizations of glioblastoma tumors. The preliminary results demonstrate the potential of combining morphometric and molecular characterizations for in silico research.

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