An analytical approach differentiates between individual and collective cancer invasion.

Tumour cells employ a variety of mechanisms to invade their environment and to form metastases. An important property is the ability of tumour cells to transition between individual cell invasive mode and collective mode. The switch from collective to individual cell invasion in the breast was shown recently to determine site of subsequent metastasis. Previous studies have suggested a range of invasion modes from single cells to large clusters. Here, we use a novel image analysis method to quantify and categorise invasion. We have developed a process using automated imaging for data collection, unsupervised morphological examination of breast cancer invasion using cognition network technology (CNT) to determine how many patterns of invasion can be reliably discriminated. We used Bayesian network analysis to probabilistically connect morphological variables and therefore determine that two categories of invasion are clearly distinct from one another. The Bayesian network separated individual and collective invading cell groups based on the morphological measurements, with the level of cell-cell contact the most discriminating morphological feature. Smaller invading groups were typified by smoother cellular surfaces than those invading collectively in larger groups. Interestingly, elongation was evident in all invading cell groups and was not a specific feature of single cell invasion as a surrogate of epithelial-mesenchymal transition. In conclusion, the combination of cognition network technology and Bayesian network analysis provides an insight into morphological variables associated with transition of cancer cells between invasion modes. We show that only two morphologically distinct modes of invasion exist.

Automated analysis and detailed quantification of biomedical images using Definiens Cognition Network Technology.

Biomedicine has seen tremendous advances in the field of image acquisition. The generation of digital images of high information content has become so straightforward and efficient that the volume of images accumulating in biomedical disciplines is posing significant challenges. Until now, conventional image analysis solutions are generally pixel-based and limited in the amount of information that they extract. However, a software system enabling the complex analysis of biomedical images should not impose restrictions on detection, classification and quantification of structures, but rather allow unlimited freedom to answer exhaustively all conceivable questions about the interactions and relationships between structures. Crucial to this is the precise and robust segmentation of relevant structures in digital micrographs. This challenge involves bringing structure, morphology and context into play. Based on the Definiens Cognition Network Technology, solutions have been deployed for use in biomedicine. The technology is object-oriented, multi-scale, context-driven and knowledge-based. Images are interpreted on the properties of networked image objects, which results in numerous advantages. This approach enables users to bring in detailed expert knowledge and enables complex analyses to be performed with unprecedented accuracy, even on poor quality data or for structures exhibiting heterogeneous properties or variable phenotypes. Extracted structures are the basis for detailed morphometric, structural and relational measurements which can be exported for each individual structure. These data can be used for decision support or correlated against experimental or molecular data, thus bridging classical biomedicine with molecular biology. An overview of the technology is provided with examples from different biomedical applications.

Localisation of breast cancer resistance protein in microvessel endothelium of human brain.

Movement of substrates between blood and brain is known to be influenced by P-glycoprotein (P-gp) at the luminal surface of the endothelium lining brain microvessels and by multidrug resistance associated protein 1 (MRP1) at the basolateral surface of the choroid plexus epithelium. Here, using RT-PCR and Western blotting, we investigate other ABC transporters in both normal and tumour human brain tissue and demonstrate the presence of breast cancer resistance protein (BCRP). Immunofluorescence confocal microscopy demonstrates that BCRP is located at the blood-brain barrier, mainly at the luminal surface of microvessel endothelium. This localization closely resembles that of P-gp. BCRP has several substrates in common with P-gp and may pose an additional barrier to drug access to the brain.