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The analysis of a protein-expression pattern from tissue microarray (TMA)data will not immediately give an answer on synergistic or antagonistic effectsbetween the expression of the observed proteins. But contrary to apparent firstimpression, it is possible to reveal those cooperative phenomena from TMA data.We present here a largely assumption-free combinatorial analysis, related tocorrelation networks but with much less arbitrary constraints. A strong focuswas put on the analysis of the basic data to analyze how the cooperativephenomena might be imprinted in the TMA data structure. The study design wasbased on two independent panels of 589 and 366 invasive breast cancer casesfrom different institutions, assembled on tissue microarrays. The combinatorialanalysis generates an optimal rank ordering of protein-expression coherence.The outcome of the analysis corresponds to all the single observationsscattered over several publications and integrates them in one context. Thismeans all these scattered observations can also be deduced from one TMAexperiment. A comprehensive statistical meta-analysis of the TMA data suggeststhe existence of a superposition of three basic coherence situations, andoffers the opportunity to analyze these data properties with additionalreal-world data and synthetic data in more detail. The presented algorithmgives molecular pathologists a tool to extract dependency information from TMAdata. Beyond this practical benefit, some light was shed on how dependencyaspects might be imprinted into expression data. This will certainly foster therefinement of algorithms to reconstruct dependency networks. The implementationof the algorithm is at the moment not end-user suitable, but available onrequest.

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