An Integrative Approach for In Silico Glioma Research

Abstract

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.

A Methodological Approach to the Characterization of Brain Gliomas, by Means of Semi-Automatic Morphometric Analysis

“…In other words, these astrocytic cells are more non-circular than pure oligodendrioma (O) ones. These findings are consistent with previous histopathological observations (Dymecki and Kulczycki 2005;Cooper et al, 2010) and with other morphometric studies of this family of neoplasmas, providing further confirmation of the good quality of the results obtained (Nafe et al, 2000;Sallinen et al, 2000). It is interesting that astrocytoma tumors show an increase in AR with increasing malignancy grade (from II-nd [i.e., ADm] to III-rd [i.e.…”

“…As follows from Fig. 9 the cell nuclei of astrocytic tumors are more elongated and non-circular than those of oligodendrioma tumors (Dymecki and Kulczycki 2005;Cooper et al, 2010). Three types of astrocytic tumors were analyzed -anaplastic astrocytoma (AA, grade III), gemistocytic astrocytoma (AG, grade II) and diffuse astrocytoma (ADm, grade II).…”

“…To date, only for glioblastoma multiforme have four putative sub-types been defined as having significant histological features. These are the proneural, neural, mesenchymal, and classical types (Cooper et al, 2010). To meet the need for a robust and efficient morphometric means of distinguishing between them, systems enabling large-scale data processing have recently been designed (Cooper et al, 2010;Verhaak et al, 2010).…”

“…These are the proneural, neural, mesenchymal, and classical types (Cooper et al, 2010). To meet the need for a robust and efficient morphometric means of distinguishing between them, systems enabling large-scale data processing have recently been designed (Cooper et al, 2010;Verhaak et al, 2010). These systems make it possible to analyze a great variety of morphologic, densitometric and topometric parameters using largescale data sets (e.g., The Cancer Genome Atlas (TCGA) and the Repository for Molecular Brain Neoplasia Data (REMBRANDT)) (TCGA Consortium, 2008;Madhavan et al, 2009).…”

Correlation of Perfusion Parameters with Genes Related to Angiogenesis Regulation in Glioblastoma: A Feasibility Study

“…[1][2][3][4] The TCGA and Repository for Molecular Brain Neoplasia are extensive multidimensional datasets, which present a unique opportunity to integrate imaging and genomic data, which, in turn, provides unique opportunities for developing a more sophisticated understanding of gliomas. 5 Considering the increasing focus on the use of quantitative imaging biomarkers for patient survival and treatment response, it is critical to understand the molecular basis of these imaging features. Recent literature has tried to correlate morphologic imaging features with gene expression in GBMs [6][7][8][9] ; however, there has been little emphasis on correlating metabolic or physiologic imaging biomarkers with gene expression.…”