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Nat Commun. 2016 Nov 16;7:13427. doi: 10.1038/ncomms13427.

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development.

Author information

1
Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, B301, 1101 33rd Street, Baltimore, Maryland 21218, USA.
2
The Johns Hopkins University, School of Medicine, Department of Otolaryngology, Head and Neck Cancer Research, 1550 Orleans Street, Baltimore, Maryland 21231, USA.
3
Laboratory of Bioinformatics, D. Rogachev Federal Research and Clinical Center for Pediatric Hematology, Oncology and Immunology, Samory Mashela 1, Moscow 117997, Russia.
4
Department of Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York 10461, USA.
5
BioTime, Inc., 1010 Atlantic Avenue, Alameda, California 94501, USA.
6
National Research Centre 'Kurchatov Institute', Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, 1, Akademika Kurchatova square, Moscow 123182, Russia.
7
Boston University, Department of Biomedical Engineering, 44 Cummington Street, Boston, Massachusetts 02215, USA.
8
Skolkovo Foundation, 5 Nobelya street, Skolkovo Innovation Centre, Mozhajskij region, Moscow 143026, Russia.
9
Nutrition and Metabolic Health group, Nestlé Institute of Health Sciences, CH-1015 Lausanne, Switzerland.
10
Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.
11
The Biogerontology Research Foundation, 2354 Chynoweth House, Trevissome Park, Truro TR4 8UN, UK.

Abstract

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.

Conflict of interest statement

Ivan V. Ozerov, Ksenia V. Lezhnina, Quentin Vanhaelen, Sergey Medintsev, Alexander Aliper, Artem V. Artemov, Anton Buzdin, Nikolay Borisov and Alex Zhavoronkov are affiliated with Insilico Medicine, Inc., a company engaged in aging research, which uses and provides both paid and free services using a variety of pathway activation scoring algorithms and hence may have competing financial interests.

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