Proteomic investigation of intra-tumor heterogeneity using network-based contextualization - A case study on prostate cancer.

Goh, WWB; Zhao, Y; Sue, AC-H; Guo, T; Wong, L

Proteomic investigation of intra-tumor heterogeneity using network-based contextualization - A case study on prostate cancer.

Goh, WWB Zhao, Y Sue, AC-H Guo, T Wong, L

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Journal of proteomics, 2019, 206
Issue Date:: 2019-08

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Field	Value	Language
dc.contributor.author	Goh, WWB
dc.contributor.author	Zhao, Y
dc.contributor.author	Sue, AC-H
dc.contributor.author	Guo, T
dc.contributor.author	Wong, L
dc.date.accessioned	2020-05-11T05:46:48Z
dc.date.available	2019-07-08
dc.date.available	2020-05-11T05:46:48Z
dc.date.issued	2019-08
dc.identifier.citation	Journal of proteomics, 2019, 206
dc.identifier.issn	1874-3919
dc.identifier.issn	1876-7737
dc.identifier.uri	http://hdl.handle.net/10453/140629
dc.description.abstract	Cancer is a heterogeneous disease, confounding the identification of relevant markers and drug targets. Network-based analysis is robust against noise, potentially offering a promising approach towards biomarker identification. We describe here the application of two network-based methods, qPSP (Quantitative Proteomics Signature Profiling) and PFSNet (Paired Fuzzy SubNetworks), in an intra-tissue proteome data set of prostate tissue samples. Despite high basal variation, we find that traditional statistical analysis may exaggerate the extent of heterogeneity. We also report that network-based analysis outperforms protein-based feature selection with concomitantly higher cross-validation accuracy. Overall, network-based analysis provides emergent signal that boosts sensitivity while retaining good precision. It is a potential means of circumventing heterogeneity for stable biomarker discovery.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier BV
dc.relation.ispartof	Journal of proteomics
dc.relation.isbasedon	10.1016/j.jprot.2019.103446
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0601 Biochemistry and Cell Biology, 0607 Plant Biology
dc.subject.classification	Analytical Chemistry
dc.subject.classification	Biochemistry & Molecular Biology
dc.title	Proteomic investigation of intra-tumor heterogeneity using network-based contextualization - A case study on prostate cancer.
dc.type	Journal Article
utslib.citation.volume	206
utslib.location.activity	Netherlands
utslib.for	0601 Biochemistry and Cell Biology
utslib.for	0607 Plant Biology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-05-11T05:46:46Z
pubs.publication-status	Published
pubs.volume	206

Abstract:

Cancer is a heterogeneous disease, confounding the identification of relevant markers and drug targets. Network-based analysis is robust against noise, potentially offering a promising approach towards biomarker identification. We describe here the application of two network-based methods, qPSP (Quantitative Proteomics Signature Profiling) and PFSNet (Paired Fuzzy SubNetworks), in an intra-tissue proteome data set of prostate tissue samples. Despite high basal variation, we find that traditional statistical analysis may exaggerate the extent of heterogeneity. We also report that network-based analysis outperforms protein-based feature selection with concomitantly higher cross-validation accuracy. Overall, network-based analysis provides emergent signal that boosts sensitivity while retaining good precision. It is a potential means of circumventing heterogeneity for stable biomarker discovery.

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http://hdl.handle.net/10453/140629