Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data

Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data

1. Yitan Zhu*, 
2. Yanxun Xu*, 
3. Donald L. Helseth Jr*, 
4. Kamalakar Gulukota, 
5. Shengjie Yang,
6. Lorenzo L. Pesce, 
7. Riten Mitra, 
8. Peter Müller, 
9. Subhajit Sengupta, 
10. Wentian Guo,
11. Jonathan C. Silverstein, 
12. Ian Foster, 
13. Nigel Parsad, 
14. Kevin P. White and 
15. Yuan Ji

+Author Affiliations

1. Affiliations of authors: Program of Computational Genomics & Medicine (YZ, SY, SS, YJ), Center for Molecular Medicine (DLH Jr, KG), and Center for Biomedical Research Informatics (JCS, NP), NorthShore University HealthSystem, Evanston, IL; Department of Mathematics, The University of Texas at Austin, Austin, TX (YX, PM); Computation Institute (LLP, IF) and Institute for Genomics and Systems Biology (KPW), The University of Chicago and Argonne National Laboratory, Chicago IL; Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY (RM); School of Public Health, Fudan University, Shanghai, P. R. China (WG); Department of Human Genetics and Department of Ecology & Evolution (KPW) and Department of Public Health Sciences (YJ), The University of Chicago, Chicago, IL.

1. ↵ *Authors contributed equally to this work.
2. Correspondence to: Yuan Ji, PhD, Program of Computational Genomics & Medicine, NorthShore University HealthSystem, 1001 University Pl, Evanston, IL 60201 (e-mail:koaeraser@gmail.com).

• Received August 4, 2014.
• Revision received January 13, 2015.
• Accepted April 10, 2015.

Abstract

Background: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer.

Methods: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood model derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes “Prior interaction map + TCGA data → Posterior interaction map.”

Results: Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available atwww.compgenome.org/ZODIAC.

Conclusions: Zodiac recapitulates and extends existing knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer.

• © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com