Back to Journals » Open Access Bioinformatics » Volume 5

Analyzing the basic principles of tissue microarray data measuring the cooperative phenomena of marker proteins in invasive breast cancer

Authors Buerger H, Boecker F, Packeisen J, Agelopoulos K, Poos K, Nadler W, Korsching E

Received 31 July 2012

Accepted for publication 25 October 2012

Published 17 January 2013 Volume 2013:5 Pages 1—21


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Horst Buerger,1 Florian Boecker,2 Jens Packeisen,3 Konstantin Agelopoulos,4 Kathrin Poos,2 Walter Nadler,5 Eberhard Korsching2

1Institute of Pathology, Paderborn, Germany; 2Institute of Bioinformatics, University of Münster, Münster, Germany; 3Institute of Pathology, Osnabrück, Germany; 4Department of Medicine, Hematology and Oncology, University of Münster, Münster, Germany; 5Institute for Advanced Simulation (IAS), Jülich Supercomputing Centre (JSC), Jülich, Germany

Background: The analysis of a protein-expression pattern from tissue microarray (TMA) data will not immediately give an answer on synergistic or antagonistic effects between the observed proteins. But contrary to apparent first impression, it is possible to reveal those cooperative phenomena from TMA data. The data is (1) preserving a lot of the original physiological information content and (2) because of minor variances between the tumor samples, contains several related slightly different biological states. We present here a largely assumption-free combinatorial analysis, related to correlation networks but with much less arbitrary constraints. A strong focus was put on the analysis of the basic data to analyze how the cooperative phenomena might be imprinted in the TMA data structure.
Results: The study design was based on two independent panels of 589 and 366 invasive breast cancer cases from different institutions, assembled on tissue microarrays. The combinatorial analysis generates an optimal rank ordering of protein-expression coherence. The outcome of the analysis corresponds to all the single observations scattered over several publications and integrates them in one context. This means all these scattered observations can also be deduced from one TMA experiment. A comprehensive statistical meta-analysis of the TMA data suggests the existence of a superposition of three basic coherence situations, and offers the opportunity to analyze these data properties with additional real-world data and synthetic data in more detail.
Conclusion: The presented algorithm gives molecular pathologists a tool to extract dependency information from TMA data. Beyond this practical benefit, some light was shed on how dependency aspects might be imprinted into expression data. This will certainly foster the refinement of algorithms to reconstruct dependency networks. The implementation of the algorithm is at the moment not end-user suitable, but available on request.

tissue microarrays, protein expression, dependency structure, breast cancer, progression, algorithm, biological networks

Creative Commons License This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.

Download Article [PDF] 


Readers of this article also read:

Determination of factors controlling the particle size and entrapment efficiency of noscapine in PEG/PLA nanoparticles using artificial neural networks

Shalaby KS, Soliman ME, Casettari L, Bonacucina G, Cespi M, Palmieri GF, Sammour OA, El Shamy AA

International Journal of Nanomedicine 2014, 9:4953-4964

Published Date: 23 October 2014

Cell carriers for oncolytic viruses: current challenges and future directions

Roy DG, Bell JC

Oncolytic Virotherapy 2013, 2:47-56

Published Date: 10 October 2013

Baseline medication adherence and response to an electronically delivered health literacy intervention targeting adherence

Ownby RL, Waldrop-Valverde D, Caballero J, Jacobs RJ

Neurobehavioral HIV Medicine 2012, 4:113-121

Published Date: 18 October 2012

The ClaudicatioNet concept: design of a national integrated care network providing active and healthy aging for patients with intermittent claudication

Lauret GJ, Gijsbers HJ, Hendriks EJ, Bartelink ML, de Bie RA, Teijink JA

Vascular Health and Risk Management 2012, 8:495-503

Published Date: 24 August 2012

Using biomedical networks to prioritize gene–disease associations

Arrais JP, Oliveira JL

Open Access Bioinformatics 2011, 3:123-130

Published Date: 25 August 2011

Capsular-type prediction by phylogenetic tree of glycosyltransferase gene polymorphism in Streptococcus pneumoniae

Yuka Tomita, Akira Okamoto, Keiko Yamada, et al

Open Access Bioinformatics 2011, 3:67-73

Published Date: 11 March 2011

Jet lag syndrome: circadian organization, pathophysiology, and management strategies

Andrew M Vosko, Christopher S Colwell, Alon Y Avidan

Nature and Science of Sleep 2010, 2:187-198

Published Date: 19 August 2010

Modeling short-term antidepressant responsiveness with artificial neural networks

Eugene Lin, Po See Chen, I Hui Lee, et al

Open Access Bioinformatics 2010, 2:55-60

Published Date: 1 June 2010