Cancer onset and progression involves disruptions of complex networks of biomolecular interactions in cells, as a result of genomic and epigenomic aberrations. To achieve a deeper understanding of the genetic basis of cancer and to identify novel translational directions requires moving beyond detecting simple associations between genomic aberrations and clinical endpoints. However, little is known about how the states of disease-perturbed regulatory networks are altered by such aberrations, or how aberrations ultimately lead to cellular dysfunction. The goal of the Center for Systems Analysis of the Cancer Regulome is to address this challenge by developing and applying advanced algorithms and model-based approaches to enable the interpretation of TCGA data at the level of regulatory mechanisms;and to use such knowledge for enabling principled and systematic approaches for drug target discovery and therapeutic intervention in a clinically relevant manner. The Center will build three computational pipelines that will carry out integrated analyses of TCGA-derived high-throughput experimental data sets. These pipelines will: 1) identify potential mechanisms of genomic-transcriptomic regulation associated with specific cancers and clinical parameters;2) infer networks that explain cancer type-specific transcriptional profiles, and identify molecules that may be important control nodes in these networks as a means to prioritize drug targets for therapeutic intervention;and 3) compare cancer-associated features across all cancer types within TCGA to gain insight into the regulatory basis for cancer progression in different cancer types. The results of these pipelines will be rapidly disseminated through TCGA, together with visualization tools that facilitate the exploration and evaluation of each derived regulatory mechanism.

Public Health Relevance

Cancer is a major cause of mortality and morbidity in the United States. Developing new therapies requires a deep understanding of the molecular basis of this complex genetic disease. Our integrated systems-level analysis of cancer onset and progression will map the molecular processes undertying cancer and identify potential novel drug targets for therapeutic intervention.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1-SRLB-U (O1))
Program Officer
Yang, Liming
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Institute for Systems Biology
United States
Zip Code
Plaisier, Christopher L; O'Brien, Sofie; Bernard, Brady et al. (2016) Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis. Cell Syst 3:172-86
Camargo, M Constanza; Bowlby, Reanne; Chu, Andy et al. (2016) Validation and calibration of next-generation sequencing to identify Epstein-Barr virus-positive gastric cancer in The Cancer Genome Atlas. Gastric Cancer 19:676-81
Sun, Yan; Ji, Ping; Chen, Tao et al. (2016) MIIP haploinsufficiency induces chromosomal instability and promotes tumour progression in colorectal cancer. J Pathol :
Ceccarelli, Michele; Barthel, Floris P; Malta, Tathiane M et al. (2016) Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma. Cell 164:550-63
Phillips, Lynette M; Zhou, Xinhui; Cogdell, David E et al. (2016) Glioma progression is mediated by an addiction to aberrant IGFBP2 expression and can be blocked using anti-IGFBP2 strategies. J Pathol 239:355-64
Zhang, Min; Liu, Guoyan; Xue, Fengxia et al. (2016) Copy number deletion of RAD50 as predictive marker of BRCAness and PARP inhibitor response in BRCA wild type ovarian cancer. Gynecol Oncol 141:57-64
Chua, C Y; Liu, Y; Granberg, K J et al. (2016) IGFBP2 potentiates nuclear EGFR-STAT3 signaling. Oncogene 35:738-47
Poole, William; Gibbs, David L; Shmulevich, Ilya et al. (2016) Combining dependent P-values with an empirical adaptation of Brown's method. Bioinformatics 32:i430-i436
Cancer Genome Atlas Research Network; Linehan, W Marston; Spellman, Paul T et al. (2016) Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma. N Engl J Med 374:135-45
Wu, Sherry Y; Rupaimoole, Rajesha; Shen, Fangrong et al. (2016) A miR-192-EGR1-HOXB9 regulatory network controls the angiogenic switch in cancer. Nat Commun 7:11169

Showing the most recent 10 out of 75 publications