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Author Notes:

Address correspondence to: Lee A.D. Cooper, Department of Biomedical Informatics, Psychology and Interdisciplinary Sciences Building, 36 Eagle Row, 5th Floor South Atlanta, GA 30322, Phone: 404-712-0110; Fax: 404-727-4992, lee.cooper@emory.edu

The authors declare no conflicts of interest.


Research Funding:

Support: This work was supported by US Public Health Service National Institutes of Health (NIH) grants K22LM011576 (LADC), R01CA176659 (DJB) and the Georgia Research Alliance (DJB).


  • Science & Technology
  • Life Sciences & Biomedicine
  • Medicine, Research & Experimental
  • Pathology
  • Research & Experimental Medicine

Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images


Journal Title:

Laboratory Investigation


Volume 95, Number 4


, Pages 366-376

Type of Work:

Article | Post-print: After Peer Review


Technological advances in computing, imaging, and genomics have created new opportunities for exploring relationships between histology, molecular events, and clinical outcomes using quantitative methods. Slide scanning devices are now capable of rapidly producing massive digital image archives that capture histological details in high resolution. Commensurate advances in computing and image analysis algorithms enable mining of archives to extract descriptions of histology, ranging from basic human annotations to automatic and precisely quantitative morphometric characterization of hundreds of millions of cells. These imaging capabilities represent a new dimension in tissue-based studies, and when combined with genomic and clinical endpoints, can be used to explore biologic characteristics of the tumor microenvironment and to discover new morphologic biomarkers of genetic alterations and patient outcomes. In this paper, we review developments in quantitative imaging technology and illustrate how image features can be integrated with clinical and genomic data to investigate fundamental problems in cancer. Using motivating examples from the study of glioblastomas (GBMs), we demonstrate how public data from The Cancer Genome Atlas (TCGA) can serve as an open platform to conduct in silico tissue-based studies that integrate existing data resources. We show how these approaches can be used to explore the relation of the tumor microenvironment to genomic alterations and gene expression patterns and to define nuclear morphometric features that are predictive of genetic alterations and clinical outcomes. Challenges, limitations, and emerging opportunities in the area of quantitative imaging and integrative analyses are also discussed.

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