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Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach.

Authors
  • Tranchevent, Léon-Charles1
  • Nazarov, Petr V1
  • Kaoma, Tony1
  • Schmartz, Georges P1, 2
  • Muller, Arnaud1
  • Kim, Sang-Yoon1
  • Rajapakse, Jagath C3
  • Azuaje, Francisco4
  • 1 Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, Strassen, L-1445, Luxembourg. , (Luxembourg)
  • 2 Bioinformatics bachelor program, Universität des Saarlandes, Saarbrücken, Germany. , (Germany)
  • 3 Bioinformatics Research Center, School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore. , (Singapore)
  • 4 Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, Strassen, L-1445, Luxembourg. [email protected] , (Luxembourg)
Type
Published Article
Journal
Biology Direct
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Jun 07, 2018
Volume
13
Issue
1
Pages
12–12
Identifiers
DOI: 10.1186/s13062-018-0214-9
PMID: 29880025
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients. We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be combined to build more powerful models. We demonstrate that the networks inferred from omics data contain clinically relevant information and that patient clinical outcomes can be predicted using only network topological data. This article was reviewed by Yang-Yu Liu, Tomislav Smuc and Isabel Nepomuceno.

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