An integrative approach for in silico glioma research.
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.
Cooper
Lee A D
LA
Center for Comprehensive Informatics, Atlanta, GA 30322, USA. lee.cooper@emory.edu
Kong
Jun
J
Gutman
David A
DA
Wang
Fusheng
F
Cholleti
Sharath R
SR
Pan
Tony C
TC
Widener
Patrick M
PM
Sharma
Ashish
A
Mikkelsen
Tom
T
Flanders
Adam E
AE
Rubin
Daniel L
DL
Van Meir
Erwin G
EG
Kurc
Tahsin M
TM
Moreno
Carlos S
CS
Brat
Daniel J
DJ
Saltz
Joel H
JH
D002467:Cell Nucleus / Q000473:pathology; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D005910:Glioma / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007150:Immunohistochemistry