Immunization with DNA coding for gp100 results in CD4 T-cell independent antitumor immunity

BACKGROUND: Xenogeneic DNA immunization can exploit small differences in expressed protein sequence resulting in immune recognition of self-molecules. We hypothesized that immunizing mice with xenogeneic DNA coding for the human melanosomal membrane glycoprotein gp100 would overcome immune ignorance or tolerance and result in tumor immunity. We also investigated the immunologic mechanisms of the antitumor immunity. METHODS: C57BL/6 mice were immunized with DNA coding for human gp100, mouse gp100, or control vector by gene gun. After immunization, mice were challenged with a syngeneic melanoma expressing gp100, and tumor growth was analyzed. Mice deficient in major histocompatibility complex class I or class II molecules were similarly studied to assess the immunologic mechanism of the tumor protection. RESULTS: There was significant tumor protection after vaccination with xenogeneic human gp100 DNA. Class I, but not class II, major histocompatibility complex molecules were required for tumor immunity. In addition, mice immunized with human gp100 demonstrated autoimmunity manifested as coat color depigmentation. CONCLUSIONS: Immunization with xenogeneic DNA coding for the melanosomal glycoprotein gp100 results in tumor protection and autoimmune depigmentation. These results show that xenogeneic DNA vaccines can lead to cancer immunity without CD4(+) T-cell help with potential implications for rational vaccine design.     

Functional evaluation of out-of-the-box text-mining tools for data-mining tasks

Objective The trade-off between the speed and simplicity of dictionary-based term recognition and the richer linguistic information provided by more advanced natural language processing (NLP) is an area of active discussion in clinical informatics. In this paper, we quantify this trade-off among text processing systems that make different trade-offs between speed and linguistic understanding. We tested both types of systems in three clinical research tasks: phase IV safety profiling of a drug, learning adverse drug–drug interactions, and learning used-to-treat relationships between drugs and indications.Materials We first benchmarked the accuracy of the NCBO Annotator and REVEAL in a manually annotated, publically available dataset from the 2008 i2b2 Obesity Challenge. We then applied the NCBO Annotator and REVEAL to 9 million clinical notes from the Stanford Translational Research Integrated Database Environment (STRIDE) and used the resulting data for three research tasks.Results There is no significant difference between using the NCBO Annotator and REVEAL in the results of the three research tasks when using large datasets. In one subtask, REVEAL achieved higher sensitivity with smaller datasets.Conclusions For a variety of tasks, employing simple term recognition methods instead of advanced NLP methods results in little or no impact on accuracy when using large datasets. Simpler dictionary-based methods have the advantage of scaling well to very large datasets. Promoting the use of simple, dictionary-based methods for population level analyses can advance adoption of NLP in practice.