- Chang JT. (2012) Deriving transcriptional programs and functional processes from gene expression databases. Bioinformatics 28(8), 1122-9.
- Cheng Q, Chang JT, Geradts J, Neckers LM, Haystead T, Spector NL, Lyerly HK. (2012) Amplification and high‐level expression of HSP90 marks aggressive phenotypes of HER2 negative breast cancer. Breast Can Res 14(2), R62.
- Chang JT, Gatza ML, Lucas JE, Barry WT, Nevins JR. (2011) A Software Platform for Gene Expression Signature Analysis. BMC Bioinformatics 12(443).
- Cohen AL, Soldi R, Zhang H, Gustafson AM, Wilcox R, Welm BE, Chang JT, Johnson E, Spira A, Jeffrey SS, Bild AH. (2011) A pharmacogenomic method for individualized prediction of drug sensitivity. Mol Syst Biology 7(513).
- Shats I, Gatza ML, Chang JT, Mori S, Freedman JA, Wang J, Potti A, Rich J, Nevins JR. (2011) Development of an Expression Signature-Based Approach to Quantify and Target a Stem-Like Phenotype in Cancer. Cancer Res 71(5), 1772-80.
- Freedman JA, Chang JT, Jakoi L, and Nevins JR. (2009) A Combinatorial Basis for E2F Transcription Factor Specificity. Oncogene 28(32): 2873-81.
- Chang JT, Carvalho C, Mori S, Bild AH, Gatza M, Wang Q, Lucas J, Potti A, Febbo P, West M, and Nevins JR. (2009) A Genomic Strategy to Elucidate Modules of Oncogenic Pathway Signaling Networks. Molecular Cell 34(1): 104-114.
- Carvalho C, Chang J, Lucas J, Nevins JR, Wang Q, and West M. (2008) High - Dimensional Sparse Factor Modelling: Applications in Gene Expression Genomics. Jour of the Amer Stat Assoc 103:1438-1456.
- Chang JT and Nevins JR. (2006) GATHER: a systems approach to interpreting genomic signatures. Bioinformatics 22(23): 2926-2933.
- Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, Joshi MB, Harpole D, Lancaster JM, Berchuck A, Olson JA, Marks JR, Dressman HK, West M, and Nevins JR. (2005) Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439(7074): 353-357.
Jeffrey Chang, Ph.D.
CPRIT Scholar in Cancer Research
UTHSC, Medical School, (713) 500-7558
Ph.D.: Stanford University, 2004
- Institute for Genome Sciences & Policy,
Duke University, 2010
Cell Signaling Programs in Cancer
Cell Signaling, Transcriptional Regulation, Genomics, Bioinformatics
Our lab deciphers cell signaling programs. Briefly, receptors in the cell membrane initiate cascades of reactions (pathways) that ultimately change the expression of genes. While cellular pathways are often thought of as independent and linear entities, the reality is that there is significant crosstalk among them. Indeed, the dense interconnections among signaling molecules exhibit a network structure.
The complexity of the cell signaling network provides it the capacity to produce organisms like ourselves (a good thing) as well as diseases that are difficult to manage (a bad thing). Therefore, a challenge is to explain how the network operates in normal circumstances, and how it is rewired in disease. Specifically, we wish to understand how the propagation of cell cycle signals becomes altered in cancer.
Our research program can be grouped into three cores:
- We are dissecting the structure of signaling cascades, focusing on the Ras network. Ras controls numerous tumorigenic processes through multiple downstream effectors. To better understand the structure of Ras signaling, we are developing strategies to dissect Ras activities into discrete sub-components called modules, represented by gene expression profiles. We have previously shown that these modules link to disease. We now wish to identify the genes that drive each module, and investigate how they may form the basis of a rational strategy for selecting clinical treatments.
- We are also decoding combinatorial transcriptional regulatory programs. Here, we focus on E2F, a family of transcription factors that regulate a range of activities through interactions with cofactors. E2F1 has a unique ability to regulate both cell cycle progression and apoptosis, processes whose decoupling is a fundamental step in the development of cancer. To better understand this, we are investigating the combinatorial interactions that underlie this transcriptional program, and how alterations can lead to the uncontrolled proliferation seen in cancer.
- Lastly, we are developing infrastructure to distribute our computational algorithms. Each of our projects contains a computational component, and an important aspect of our work is to make our methods available. We have previously developed the GATHER website for analysis of gene sets, and are now developing a platform SIGNATURE for the analysis of oncogenic pathways.
Across our investigations, we use genomics to reveal the simple fundamental units that constitute complex biological phenotypes (such as the workings of a cancer cell). We use human cell culture as a model and leverage a range of techniques including bioinformatics, molecular biology, and biochemistry.