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Cambridge, MA, United States

Xing H.,Gene Network science | McDonagh P.D.,Gene Network science | Bienkowska J.,Biogen Idec | Cashorali T.,Gene Network science | And 8 more authors.
PLoS Computational Biology | Year: 2011

Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86 - a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28. © 2011 Xing et al.

Khalil I.,Gene Network science | Brewer M.A.,University of Health Center | Neyarapally T.,Gene Network science | Runowicz C.D.,University of Health Center
Gynecologic Oncology | Year: 2010

Ovarian cancer is one of the most common gynecologic malignancies and is the 5th leading cause of cancer mortality in women in the United States. Understanding the biology and molecular pathogenesis of ovarian epithelial tumors is key to developing improved prognostic indicators and effective therapies. The selection of ovarian serous carcinomas as one of the three cancer types for extensive genomic and proteomic characterization of The Cancer Genome Atlas (TCGA) project offers an important opportunity to extend our knowledge of ovarian cancer. The data portal includes molecular characterization, high throughput sequencing, and clinical data. Models to determine which of these genes act as "key drivers" of ovarian carcinogenesis and which are innocent "passengers" are needed. Standard statistical approaches often fail to differentiate between these driver and passenger genes, given that the correlation between sets of genes or genes and endpoints alone does not establish causality. As contrasted to basic correlations analyses, biological network models offer the ability to resolve causality by elucidating the directional linkages between genetics, molecular characterizations of the system, and clinical measures. This article describes the use of a novel, supercomputer-driven approach named REFS™ to learn network models directly from the TGCA ovarian cancer data set and simulate these models to learn the "key drivers" of ovarian carcinogenesis. The model can be validated by out-of-sample testing, and may provide a powerful new tool for ovarian cancer research. © 2009 Elsevier Inc. All rights reserved.

Gumus Z.H.,New York Medical College | Siso-Nada F.,Gene Network science | Gjyrezi A.,New York Medical College | McDonagh P.,Gene Network science | And 3 more authors.
10th IEEE International Conference on Bioinformatics and Bioengineering 2010, BIBE 2010 | Year: 2010

We present an integrated experimental and computational approach designed to identify the key cellular components that either contribute to or drive therapeutic synergy of drug combinations with anticancer activity. The approach includes (i) quantification of drug synergy in high throughput transcriptome experiments, (ii) data-driven reverse engineering and forward simulation technology to develop an in silico model predictive of drug synergy, and (iii) utilization of databases of interaction and functional information in hypothesis generation that are validated experimentally in a final step (iv). The goal is to develop an integrated framework that aids in understanding the mechanistic details of drug synergy to design better combination drugs. We illustrate this approach with an application to the analysis of transcriptome changes in cells exposed to the synergistic anticancer drug combination of farnesyl transferase inhibitors (FTIs) combined with taxanes. © 2010 IEEE.

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