Literature DB >> 25688112

The shortest path is not the one you know: application of biological network resources in precision oncology research.

Inna Kuperstein1, Luca Grieco2, David P A Cohen1, Denis Thieffry3, Andrei Zinovyev1, Emmanuel Barillot4.   

Abstract

Several decades of molecular biology research have delivered a wealth of detailed descriptions of molecular interactions in normal and tumour cells. This knowledge has been functionally organised and assembled into dedicated biological pathway resources that serve as an invaluable tool, not only for structuring the information about molecular interactions but also for making it available for biological, clinical and computational studies. With the advent of high-throughput molecular profiling of tumours, close to complete molecular catalogues of mutations, gene expression and epigenetic modifications are available and require adequate interpretation. Taking into account the information about biological signalling machinery in cells may help to better interpret molecular profiles of tumours. Making sense out of these descriptions requires biological pathway resources for functional interpretation of the data. In this review, we describe the available biological pathway resources, their characteristics in terms of construction mode, focus, aims and paradigms of biological knowledge representation. We present a new resource that is focused on cancer-related signalling, the Atlas of Cancer Signalling Networks. We briefly discuss current approaches for data integration, visualisation and analysis, using biological networks, such as pathway scoring, guilt-by-association and network propagation. Finally, we illustrate with several examples the added value of data interpretation in the context of biological networks and demonstrate that it may help in analysis of high-throughput data like mutation, gene expression or small interfering RNA screening and can guide in patients stratification. Finally, we discuss perspectives for improving precision medicine using biological network resources and tools. Taking into account the information about biological signalling machinery in cells may help to better interpret molecular patterns of tumours and enable to put precision oncology into general clinical practice.
© The Author 2015. Published by Oxford University Press on behalf of the UK Environmental Mutagen Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Mesh:

Year:  2015        PMID: 25688112     DOI: 10.1093/mutage/geu078

Source DB:  PubMed          Journal:  Mutagenesis        ISSN: 0267-8357            Impact factor:   3.000


  12 in total

Review 1.  Intracellular and intercellular signaling networks in cancer initiation, development and precision anti-cancer therapy: RAS acts as contextual signaling hub.

Authors:  Peter Csermely; Tamás Korcsmáros; Ruth Nussinov
Journal:  Semin Cell Dev Biol       Date:  2016-07-06       Impact factor: 7.727

2.  Network biology elucidates metastatic colon cancer mechanisms.

Authors:  Inna Kuperstein; Sylvie Robine; Andrei Zinovyev
Journal:  Cell Cycle       Date:  2015       Impact factor: 4.534

3.  Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application.

Authors:  Gaye Lightbody; Valeriia Haberland; Fiona Browne; Laura Taggart; Huiru Zheng; Eileen Parkes; Jaine K Blayney
Journal:  Brief Bioinform       Date:  2019-09-27       Impact factor: 11.622

4.  DeDaL: Cytoscape 3 app for producing and morphing data-driven and structure-driven network layouts.

Authors:  Urszula Czerwinska; Laurence Calzone; Emmanuel Barillot; Andrei Zinovyev
Journal:  BMC Syst Biol       Date:  2015-08-14

5.  NaviCell Web Service for network-based data visualization.

Authors:  Eric Bonnet; Eric Viara; Inna Kuperstein; Laurence Calzone; David P A Cohen; Emmanuel Barillot; Andrei Zinovyev
Journal:  Nucleic Acids Res       Date:  2015-05-09       Impact factor: 16.971

6.  Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks.

Authors:  José Lages; Dima L Shepelyansky; Andrei Zinovyev
Journal:  PLoS One       Date:  2018-01-25       Impact factor: 3.240

7.  NetNorM: Capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis.

Authors:  Marine Le Morvan; Andrei Zinovyev; Jean-Philippe Vert
Journal:  PLoS Comput Biol       Date:  2017-06-26       Impact factor: 4.475

8.  Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms.

Authors:  Alexander Mazein; Marek Ostaszewski; Inna Kuperstein; Steven Watterson; Nicolas Le Novère; Diane Lefaudeux; Bertrand De Meulder; Johann Pellet; Irina Balaur; Mansoor Saqi; Maria Manuela Nogueira; Feng He; Andrew Parton; Nathanaël Lemonnier; Piotr Gawron; Stephan Gebel; Pierre Hainaut; Markus Ollert; Ugur Dogrusoz; Emmanuel Barillot; Andrei Zinovyev; Reinhard Schneider; Rudi Balling; Charles Auffray
Journal:  NPJ Syst Biol Appl       Date:  2018-06-02

9.  Signalling maps in cancer research: construction and data analysis.

Authors:  Maria Kondratova; Nicolas Sompairac; Emmanuel Barillot; Andrei Zinovyev; Inna Kuperstein
Journal:  Database (Oxford)       Date:  2018-01-01       Impact factor: 3.451

Review 10.  Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine.

Authors:  Ekaterina A Kotelnikova; Mikhail Pyatnitskiy; Anna Paleeva; Olga Kremenetskaya; Dmitriy Vinogradov
Journal:  Oncotarget       Date:  2016-08-09
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.