Titre
A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Şenbabaoğlu, Y.
Auteure/Auteur
Sümer, S.O.
Auteure/Auteur
Sánchez-Vega, F.
Auteure/Auteur
Bemis, D.
Auteure/Auteur
Ciriello, G.
Auteure/Auteur
Schultz, N.
Auteure/Auteur
Sander, C.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
1553-7358
Statut éditorial
Publié
Date de publication
2016
Volume
12
Numéro
2
Première page
e1004765
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural Publication Status: epublish
Résumé
Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as 'cancer hallmarks'. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody-related technical biases. Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways, innate and adaptive immune signaling, cell cycle, metabolism, and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer.
PID Serval
serval:BIB_143EFDA31CDA
PMID
Open Access
Oui
Date de création
2016-06-26T14:23:53.461Z
Date de création dans IRIS
2025-05-20T18:14:01Z
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Nom
BIB_143EFDA31CDA.P001.pdf
Version du manuscrit
preprint
Taille
7.43 MB
Format
Adobe PDF
PID Serval
serval:BIB_143EFDA31CDA.P001
URN
urn:nbn:ch:serval-BIB_143EFDA31CDA7
Somme de contrôle
(MD5):00c87d32b34d3d92bbb590a140a6e09b