Department / Division:
Surgery / General Surgery

Address:
DUMC 2945
Durham, NC 27710

Office Telephone:
919-684-6849

Fax Telephone:
919-668-4777

• MD, Duke University School of Medicine, 2003

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

• 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.

I am currently investigating methods to develop pathway predictors that are representative of components or "modules" of pathway activity. The advantage of looking at components is that we can tailor our understanding of pathway activation in tumors to variations in their componentry, allowing us to better understand the heterogeneous behaviors of tumors.

We employ a modeling strategy utilizing sparse ANOVA to identify differentially expressed transcripts in experimentally-controlled in vitro training sets. We then apply Bayesian factor regression models (BFRM) to segment an overarching model of oncogenic pathway activity into a complement of factors or modules that are now essentially “experimentally-annotated”. We can computationally project these modules into new data and interrogate the distribution of all possible models with Shotgun Stochastic Search (SSS). Proof-of-principle experiments demonstrate remarkable accuracy (R=0.93, p-value=7E-12) in predicting response of lung cancer cell lines to Gefitinib using EGFR modules, and breast (R=0.8, p-value=4E-8) and prostate (R=0.83, p-value=8E-5) cell lines to Dasatinib using SRC modules.

An important advantage of this strategy is that we can highlight cross-talk between pathways. For instance, our model of Gefitinib sensitivity incorporates information about downstream Ras-activating mutations. We can demonstrate this by using our complement of EGFR modules to model Ras activation in a dataset of ectopic Ras expression. The EGFR-based modules do so with 100% accuracy, showing that EGFR modules incorporate the Ras-effector arm of EGFR initiated signaling. Further, by investigating the contribution of all EGFR modules to a Ras prediction, we find that module #2 has a posterior marginal probability of 88% for representing epistatic Ras activity.

This 'modular' analytic framework also permits us to interrogate not only whether a particular pathway is in effect, but which components of that pathway contribute the most to models of interest. For instance, when studying Dasatinib sensitivity in breast and prostate cell lines, different modules of the SRC pathway contribute differentially to sensitivity depending on tissue type. Certainly this is a concept we might understand intuitively--and our analytic strategy bears this out.

We will also test our models in solid tumors such as colon cancer that has a well-established paradigm of progression from normal tissue to malignant tissue. It has been long accepted that colon cancer arises out of initial, followed by successive mutational events causing perturbations in growth regulatory and tumor suppressor programs. Our hope is to model these very events by quantitatively evaluating the pathways and sub-pathways with our models. These models differ from identifying single gene mutations because the information from genome-scale transcriptional data allow one to add a quantitative understanding of pathway activity. In other words, we seek to understand not only whether a pathway is active or not, but how active it is.

Bringing these computational techniques back to experimental biology is an important component of this research. We will take advantage of new technologies that allow us to create customized assays of pathway activity in a multiplex fashion and use tools such as genome-wide RNAi libraries to directly interrogate pathway interactions, allowing us to identify pathway synergies and therapeutic opportunities in neoplasia.

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

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

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

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

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, 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

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

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

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

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