Oncogenomics Section

Javed Khan, M.D.


Guest Researcher
Andrew Brohl, M.D.
Special Volunteer
Tai Chi (Adam) Cheuk, Ph.D.
Bioinformatics Analyst
Hsien-Chao Chou, Ph.D.
Research Assistant
Vineela Gangalapudi
Special Volunteer
Berkley Gryder, Ph.D.
Research Collaborator
Robert G. Hawley, Ph.D.
Clinical Fellow
Yong Kim, M.D., Ph.D.
Predoctoral Fellow (Medical Student)
Katherine Masih, B.S.
Postdoctoral Fellow
David E. Milewski, Ph.D.
Young Song, Ph.D.
Program Specialist
Beverly Stalker
Postdoctoral Fellow (Visiting)
Meijie Tian, Ph.D.
Chaoyu Wang, M.S.
Bioinformatics Analyst
Xinyu Wen, M.S.
Postbaccalaureate Fellow
Jerry T. Wu, B.S.
Postdoctoral Fellow (Visiting)
Arwa I. Fallatah, Ph.D.
Prebaccalaureate Fellow
Benjamin J. Somerville
Postbaccalaureate Fellow
Zahin Islam, B.S.
Predoctoral Fellow (Medical Student)
Abdelrahman Rahmy, B.S.

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Nature Medicine Cover - June 2001

Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks

Published Date


The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRBCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy.


Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks.  Khan J, Wei JS, Ringnér M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C, Meltzer PS. Nat Med. 2001 Jun;7(6):673-9.

Cancer Research Cover - October 2004

Prediction of clinical outcome using gene expression profiling and artificial neural networks for patients with neuroblastoma

Published Date


Currently, patients with neuroblastoma are classified into risk groups (e.g., according to the Children’s Oncology Group risk-stratification) to guide physicians in the choice of the most appropriate therapy. Despite this careful stratification, the survival rate for patients with high-risk neuroblastoma remains <30%, and it is not possible to predict which of these high-risk patients will survive or succumb to the disease. Therefore, we have performed gene expression profiling using cDNA microarrays containing 42,578 clones and used artificial neural networks to develop an accurate predictor of survival for each individual patient with neuroblastoma. Using principal component analysis we found that neuroblastoma tumors exhibited inherent prognostic specific gene expression profiles. Subsequent artificial neural network-based prognosis prediction using expression levels of all 37,920 good-quality clones achieved 88% accuracy. Moreover, using an artificial neural network-based gene minimization strategy in a separate analysis we identified 19 genes, including 2 prognostic markers reported previously, MYCN and CD44, which correctly predicted outcome for 98% of these patients. In addition, these 19 predictor genes were able to additionally partition Children’s Oncology Group-stratified high-risk patients into two subgroups according to their survival status (P 0.0005). Our findings provide evidence of a gene expression signature that can predict prognosis independent of currently known risk factors and could assist physicians in the individual management of patients with high-risk neuroblastoma.


Prediction of clinical outcome using gene expression profiling and artificial neural networks for patients with neuroblastoma. Wei JS, Greer BT, Westermann F, Steinberg SM, Son CG, Chen QR, Whiteford CC, Bilke S, Krasnoselsky AL, Cenacchi N, Catchpoole D, Berthold F, Schwab M, and Khan J.  Cancer Res. 2004 Oct 1;64(19):6883-91.