Physicians

Department / Division
Surgery / Surgical Oncology

Address
DUMC 2945
Durham, NC 27710

Office Telephone
919-684-6849

Fax Telephone
919-668-4777

Training

• MD, Duke University School of Medicine, 2003

Residency

• General Surgery, Duke University Medical Center, 2003-2008

Other Training

• PhD, Genetics, Duke University Medical Center, 2002

Clinical Interests
Breast cancer; research interest in solid-tumor gene-expression analysis; oncogenic pathway analysis; computational and systems biology

Research Interests
Quantitative Oncology Research

My interest is in developing and testing quantitative models of oncogenic pathway activation. This extends previous work where I demonstrated that ectopic Myc and Ras activity in cell culture generate transcriptional changes that allow one to model their activity in in vivo. Gene expression-based models accurately recapitulate the activity of Myc-activated and Ras-activated pathways in the cell cycle, demonstrating kinetics that match their known biochemical activity and, more importantly, accurately predict the activity of Myc and Ras in tumor tissues from MMTV transgenic mouse models.

Currently, there are two broad thrusts to the work we are doing:

A. Latent Factor Models of Cancer Signaling
Extending on work by colleagues Carvalho, Lucas, West and Chang in the Department of Statisical Sciences and the IGSP, I am interested in using latent factors, particularly Bayesian factor regression models (BFRM, see Carvalho, JASA, 2008) to decompose oncogenic signaling pathways into signaling components. This extends from my previous work, but uses  new techniques that reflect our intuition that the entirety of a signaling cascade is not inflexibly stereotyped irregardless of tissue type or context. There are obviously numerous strategies for tackling this problem. We pursue a paradigm that in vitro perturbations of oncogenic activity elicit patterns that can be traced out in vivo. Further, preclinical in vivo models provide an opportunity to refine these in vitro building blocks.

To test whether these latent factors have the potential to serve as phenotypes, along the lines of Bild, et al. (Nature, 2006), we think of response to targeted cancer therapeutics as a quantitative phenotype. If a targeted agent can attenuate or alter known signaling pathways, then the configuration of a tumor’s molecular machinery should reflect this fact. Our goal is to confirm this concept in vitro and ramp up to in vivo mouse xenograft models, and hopefully, beyond.

A consequence of factor models of oncogenic signaling is that it is trivial to develop models that allow components of multiple signaling pathways to become interleaved depending on the context. Because of this a parallel goal is to create a ‘library’ of pathway factors that can be heterogeneously employed to answer our questions.

B. QUADRA-Genomic Factor
If we want to build a resource consisting of informative factors for elucidating cancer signaling it is important that it is in a framework that is intelligible to (1) ourselves, (2) our colleagues, (3) the scientific community. We therefore are building a resource called QUADRA-Genomic Factor (Quality-Assured Data Repository and Analysis-Geomic Factor) that rigorously version controls our work from inception to publication, providing a transparent, auditable, and reproducible foundation from which to work. Such tools are actually not new: software developers have been using versioning systems for years. Being able to rewind your analysis, or branch out new ideas without overwriting your previous work, or merge collaborative projects are all things that programmers do everyday. We should do the same, both to make it easier on ourselves and for others. The aim is to version all our analyses, all our custom functions, and our libraries of factors. In combination with this, we are building resources to tag genomic data with metadata that make it simple to perform programmatic reconciliations that minimize the chance that errors do get propagated throughout a complex genomic analysis.

Version
commit 8f0f233e791fe54588d0a27675240dc065a05cd7
Author: Erich S. Huang
Date:   Tue Sep 7 15:05:56 2010 -0400

Industry Relationships and Collaborations (What's this?)

This physician has no reported relationships with industry.

Representative Publications
Cheng SH, Horng CF, West M, Huang E, Pittman J, Tsou MH, Dressman H, Chen CM, Tsai SY, Jian JJ, Liu MC, Nevins JR, Huang AT. Genomic prediction of locoregional recurrence after mastectomy in breast cancer. J Clin Oncol. 2006 Oct 1;24(28):4594-602. (2006) Abstract

Huang ES, Nevins JR, West M, Kuo PC. An overview of genomic data analysis. Surgery. 2004 Sep;136(3):497-9. (2004) Abstract

Pittman J, Huang E, Nevins J, Wang Q, West M. Bayesian analysis of binary prediction tree models for retrospectively sampled outcomes. Biostatistics. 2004 Oct;5(4):587-601. (2004) Abstract

Black EP, Huang E, Dressman H, Rempel R, Laakso N, Asa SL, Ishida S, West M, Nevins JR. Distinct gene expression phenotypes of cells lacking Rb and Rb family members. Cancer Res. 2003 Jul 1;63(13):3716-23. (2003) Abstract

Huang E, Cheng SH, Dressman H, Pittman J, Tsou MH, Horng CF, Bild A, Iversen ES, Liao M, Chen CM, West M, Nevins JR, Huang AT. Gene expression predictors of breast cancer outcomes. Lancet. 2003 May 10;361(9369):1590-6. (2003) Abstract

Huang E, Ishida S, Pittman J, Dressman H, Bild A, Kloos M, D'Amico M, Pestell RG, West M, Nevins JR. Gene expression phenotypic models that predict the activity of oncogenic pathways. Nat Genet. 2003 Jun;34(2):226-30. (2003) Abstract

Huang E, West M, Nevins JR. Gene expression profiling for prediction of clinical characteristics of breast cancer. Recent Prog Horm Res. 2003;58:55-73. (2003) Abstract

Nevins JR, Huang ES, Dressman H, Pittman J, Huang AT, West M. Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction. Hum Mol Genet. 2003 Oct 15;12 Spec No 2:R153-7. (2003) Abstract

Ishida S, Huang E, Zuzan H, Spang R, Leone G, West M, Nevins JR. Role for E2F in control of both DNA replication and mitotic functions as revealed from DNA microarray analysis. Mol Cell Biol. 2001 Jul;21(14):4684-99. (2001) Abstract

Leone G, Sears R, Huang E, Rempel R, Nuckolls F, Park CH, Giangrande P, Wu L, Saavedra HI, Field SJ, Thompson MA, Yang H, Fujiwara Y, Greenberg ME, Orkin S, Smith C, Nevins JR. Myc requires distinct E2F activities to induce S phase and apoptosis. Mol Cell. 2001 Jul;8(1):105-13. (2001) Abstract

West M, Blanchette C, Dressman H, Huang E, Ishida S, Spang R, Zuzan H, Olson JA Jr, Marks JR, Nevins JR. Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci U S A. 2001 Sep 25;98(20):11462-7. (2001) Abstract