Anticancer immune responses can be enhanced by immune intervention that promotes complex biological mechanisms involving several cellular populations. The classical immune monitoring for biological-based cancer clinical trials is often based on single-cell analysis. However, the overall effect could be lost by such a reductionist approach explaining the lack of correlation among clinical and immunological endpoints often reported. Microarray technology could give the possibility of studying in a multiparametric setting the immune therapy effects. The application of microarray is leading to an improved understanding of the immune responses to tumor immunotherapy. In fact, analysis of cancer vaccine-induced host responses using microarrays is proposed as valuable alternative to the standard cell-based methods. This paper shows successful examples of how high-throughput gene expression profiling contributed to the understanding of anticancer immune responses during biological therapy, introducing as well the integrative platforms that allow the network analysis in molecular biology studies.

Immune-based therapies that enhance the natural antitumor immune response represent an attractive therapeutic approach. However, the ongoing development of such therapies, which includes vaccines, use of immune modulators, DC therapy, and adoptive immunotherapy, has been labored by the lack of a general method for the assessment of quantity, functional activity, and overall effect of the immune response. Immunologic monitoring of cancer immunotherapy trials has been historically focused on cell-based assays to quantify tumor antigen-specific cellular immune responses via phenotypic characterization or functional analysis associated to an antigen-specific response [1–3]. A boost to the immune monitoring derived from the development of peptide-MHC Class I tetramers has become a key tool for the antigen-specific cancer vaccine immune monitoring to ex vivo identify, count, and isolate antigen-specific T cells directly from blood patient samples [4, 5]. However, the major disadvantages of such assay compared to other assays such as ELISPOT or intracellular cytokine flow cytometry are the low number of different specificities that can be assessed at the same time and the lack of functional characterization. This could lead to paradoxical coexistence of a tumor-specific immune response and the progression of the disease [6, 7]. Even more, since immune therapy impacts multiple facets of the immune response, those broad complex interactions could easily be lost if the monitoring is focused and limited to the antigen-specific phenotypic immune response analysis [8]. This was the case of tetramer-specific vaccine-induced cells in a peptide-based cancer immunotherapy setting, since the phenotypic characterization was showing a paradoxical presence of tetramer-specific cells [2, 6], able to produce interferon gamma [2] associated to clinical failure of the therapy [8]. The interplay between cancer and immune system, in the setting of cancer vaccine immune monitoring, must consider that both cancer and all the portion of the immune system may affect each other's functionality by direct contact or through the production of molecules with immune modulatory properties. Such complexity could only be recently appreciated in all its extent by high-throughput tools capable of providing a global view of biological processes [9]. Thanks to microarray analysis, we were able to understand the paradoxical coexistence of tumor antigen-specific cells and tumor in the same host by describing the quiescent status of the antigen-specific T cells [6, 7]. In order to dissect this complex interaction in a single-cell functional focus, recently, the group of Heath proposed the use of clinical microchip for the evaluation of single immune cell functionality [10]. This innovative single-cell-based analysis can lead to the resolution of the contribution of each single cell to the overall effect in response to therapy, suggesting the possible use as a potential application to the field of immune monitoring for cancer vaccines as well [10]. This study however can be considered complementary to the information that is generated through microarray. In fact, the array technology leads to complementary information since it is able to enlarge the view on the final overall occurring biological phenomena during the vaccination protocol as resultant of the action of each single-cell activity.

High-throughput technologies generate a big amount of data leading to a parallel need of implement computational platforms that integrate pathway, protein-protein interaction, genome, and literature mining data to result in comprehensive networks for a list of genes or proteins. In a recent and comprehensive review, Thomas and Bonchev from the center for the study of biological complexity, Virginia Commonwealth University, summarized the most used software for network analysis in living cells, the nodes in which are represented as protein-protein interactions or gene regulatory networks [18]. They compared the three major software packages for network analysis used CYTOSCAPE, INGENUITY, and PATHWAY STUDIO, and in the same paper, they compared in detail their characteristics as well as their associated databases. For example, characteristic and unique for the ingenuity pathway analysis is the database for human genes/peptides created from manually evaluated literature searches (Ingenuity ExpertAssist Findings). Not included in the previous paper but of high interest is a tool made available from the group of computational Systems Biology Laboratory, Institute of Biomedicine and Genome-Scale Biology Program at University of Helsinki. Dr. Hautaniemi and Dr. Laakso have implemented a computational platform (Moksiskaan) that is now able to generate hypothetical pathways for a set of genes or proteins as well as to estimate their activation statuses using regulation information in pathway repositories. The result of this type of analysis is a document that provides description of the query genes and biological processes. An important function is the generation of hypothetical drug targets that can sensibly empower a translational type of research in the oncologic field. Moksiskaan networks results can ultimately be analyzed as well in Cytoscape for further personalized analysis. (Moksiskaan and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/moksiskaan/ [19].)