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.

Fig. 1

Spectrum of nuclear morphologies in glioma tumors varies between the pure morphologies of oligodendroglial and astrocytic nuclei.

Fig. 2

Overview of the nuclear analysis workflow is presented. Each nucleus is characterized by a set of feature descriptors that are stored in a relational database for further analysis.

Fig. 3

Pathology analytical imaging standards schema supports storage and retrieval of human markup and annotation as well as algorithmic results for pathology images. Numbers indicate.

Fig. 4

Separation of proneural (blue) and classical (red) tumor subtypes. (a) Individual summary statistics indicate potential morphological distinction between proneural and classical tumor nuclei populations. (b) “Nuclei microarray” composed of nuclei from different tumor subtypes aids in interpretation of results. The green line separates nuclei from (top) proneural tumors and (bottom) classical tumors.