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Background: Discovery and validation of new antibody tractable targets is critical for the development of new antibody therapeutics to address unmet needs in oncology. Methods: A highly invasive clonal variant of the MDA-MB-435S cell line was used to generate monoclonal antibodies (MAbs), which were screened for anti-invasive activity against aggressive cancer cells in vitro. The molecular target of selected inhibitory MAb 9E1 was identified using immunoprecipitation/liquid chromatography-tandem mass spectrometry. The potential anti-tumour effects of MAb 9E1 were investigated in vitro together with immunohistochemical analysis of the 9E1 target antigen in normal and cancer tissues. Results: MAb 9E1 significantly decreases invasion in pancreatic, lung squamous and breast cancer cells and silencing of its target antigen, which was revealed as AnxA6, leads to markedly reduced invasive capacity of pancreatic and lung squamous cancer in vitro. IHC using MAb 9E1 revealed that AnxA6 exhibits a high prevalence of membrane immunoreactivity across aggressive tumour types with restricted expression observed in the majority of normal tissues. In pancreatic ductal adenocarcinoma, high AnxA6 IHC score correlated with the presence of tumour budding at the invasive front of tumours (P=0.082), the presence of perineural invasion (P= <0.0001) and showed a weak correlation with reduced survival (P=0.2242). Conclusions: This study highlights the use of phenotypic hybridoma screening as an effective strategy to select a novel function-blocking MAb, 9E1 with anti-cancer activity in vitro. Moreover, through characterisation of the 9E1 target antigen, AnxA6, our findings support further investigation of AnxA6 as a potential candidate target for antibody-mediated inhibition of pancreatic cancer.

Development of more effective therapeutic strategies for cancers of high unmet need requires the continued discovery of disease-specific protein targets for therapeutic antibody targeting. In order to identify novel proteins associated with cancer cell invasion/metastasis, we present here an alternative to antibody targeting of cell surface proteins with an established role in invasion; our functional antibody screening approach involves the isolation and selection of MAbs that are primarily screened for their ability to inhibit tumour invasion. A clonal population of the Mia PaCa-2, a pancreatic ductal adenocarcinoma (PDAC) cell line, which displays a highly invasive phenotype, was used to generate MAbs with the objective of identifying membrane targets directly involved in cancer invasion. Selected MAb 7B7 can significantly reduce invasion in a dose-responsive manner in Mia PaCa-2 clone 3 and DLKP-M squamous lung carcinoma cells. Using immunoprecipitation and liquid chromatographytandem mass spectrometry (LC-MS-MS) analysis, the target antigen of anti-invasive antibody, 7B7, was determined to be the heterodimeric Ku antigen, Ku70/80, a core protein composed of the Ku70 and Ku80 subunits which is involved in non-homologous end-joining (NHEJ) DNA repair. RNA interference-mediated knockdown of Ku70 and Ku80 resulted in a marked decrease in the invasive capacity of Mia PaCa-2 clone 3 and DLKP-M cells, indicating that Ku70/Ku80 is functionally involved in pancreatic and lung cancer invasion. Immunohistochemical analysis demonstrated Ku70/Ku80 immunoreactivity in 37 PDAC tumours, indicating that this heterodimer is highly expressed in this aggressive cancer type. This study demonstrates that a functional MAb screening approach coupled with immunoprecipitation/proteomic analyses can be successfully applied to identify functional antiinvasive MAbs and potential novel targets for therapeutic antibody targeting.

As Digital TV subscribers are offered more and more channels, it is becoming increasingly difficult for them to locate the right programme information at the right time. The personalized Electronic Programme Guide (pEPG) is one solution to this problem; it leverages artificial intelligence and user profiling techniques to learn about the viewing preferences of individual users in order to compile personalized viewing guides that fit their individual preferences. Very often the limited availability of profiling information is a key limiting factor in such personalized recommender systems. For example, it is well known that collaborative filtering approaches suffer significantly from the sparsity problem, which exists because the expected item-overlap between profiles is usually very low. In this article we address the sparsity problem in the Digital TV domain. We propose the use of data mining techniques as a way of supplementing meagre ratings-based profile knowledge with additional item-similarity knowledge that can be automatically discovered by mining user profiles. We argue that this new similarity knowledge can significantly enhance the performance of a recommender system in even the sparsest of profile spaces. Moreover, we provide an extensive evaluation of our approach using two large-scale, state-of-the-art online systems—PTVPlus, a personalized TV listings portal and Físchlár, an online digital video library system.