Recent developments with B-cell epitope identification for predictive studies

This review discusses currently available methods for predicting B-cell epitopes on proteins. The use of animals for assessing protein immunogenicity is addressed primarily to highlight the differences in B- and T-cell epitope recognition between species. These differences have to be considered when interpreting potential B-cell epitopes identified by the methods addressed here. "In vitro alternatives" focuses on the strengths and limitations of peptide-based technologies. Three types of computer-based methods for identifying potential B-cell epitopes are discussed: (i) methods applying physico-chemical and structural propensity scales for predicting linear epitopes from the primary structure of a protein, (ii) comparative methods basing prediction upon amino acid sequence and structural similarities between antigenically known and unknown proteins, and (iii) a method combining structural features with a B-cell epitope motif database for predicting linear and conformational antigenic determinants. With respect to human safety, the usefulness of antibody-based tests is limited to comparative studies between an antigenically known protein and variants thereof. Similarly, computer-based methods using data mining can address similarities in B-cell epitope profiles between related proteins, if a proper cut off can be defined for the minimal amino acid sequence similarity required for obtaining an acceptable accuracy. Among the physico-chemical and structural scales, scales identifying in a protein hairpin and non-specific turns seem useful for predicting epitopes with a continuous primary binding site. When conformational epitopes have to be identified as well, a novel computer-based tool seems to be the most promising alternative to X-ray crystallography. However, both methods remain to be extensively evaluated and validated. Thus, promising tools for B-cell epitope identification have been developed. But, no validated method for B-cell epitope identification on antigenically unknown proteins is available yet.

Cytomics - importance of multimodal analysis of cell function and proliferation in oncology

Cancer is a highly complex and heterogeneous disease involving a succession of genetic changes (frequently caused or accompanied by exogenous trauma), and resulting in a molecular phenotype that in turn results in a malignant specification. The development of malignancy has been described as a multistep process involving self-sufficiency in growth signals, insensitivity to antigrowth signals, evasion of apoptosis, limitless replicative potential, sustained angiogenesis, and finally tissue invasion and metastasis. The quantitative analysis of networking molecules within the cells might be applied to understand native-state tissue signalling biology, complex drug actions and dysfunctional signalling in transformed cells, that is, in cancer cells. High-content and high-throughput single-cell analysis can lead to systems biology and cytomics. The application of cytomics in cancer research and diagnostics is very broad, ranging from the better understanding of the tumour cell biology to the identification of residual tumour cells after treatment, to drug discovery. The ultimate goal is to pinpoint in detail these processes on the molecular, cellular and tissue level. A comprehensive knowledge of these will require tissue analysis, which is multiplex and functional; thus, vast amounts of data are being collected from current genomic and proteomic platforms for integration and interpretation as well as for new varieties of updated cytomics technology. This overview will briefly highlight the most important aspects of this continuously developing field.