Cytotoxic T-cell precursor frequencies to HER-2 (369 – 377) in patients with HER-2/neu-positive epithelial tumours

HER-2/neu oncoprotein contains several major histocompatibility complex class I-restricted epitopes, which are recognised by cytotoxic T lymphocyte (CTL) on autologous tumours and therefore can be used in immune-based cancer therapies. Of these, the most extensively studied is HER-2(9(369)). In the present report, we used dendritic cells pulsed with HER-2(9(369)) to stimulate, in the presence of IL-7 and IL-12, the production of IFN-gamma by patients' CTL detected by the enzyme-linked immunosorbent spot-assay. Frequencies of peptide-specific precursors were estimated in HLA-A2, HLA-A3 and HLA-A26 patients with HER-2/neu-positive (+) breast, ovarian, lung, colorectal and prostate cancers and healthy individuals. We found increased percentages of such precursors in HLA-A2 (25%) and HLA-A26 (30%) patients, which were significantly higher (60%) in HLA-A3 patients. Our results demonstrate for the first time that pre-existing immunity to HER-2(9(369)) occurs in patients with colorectal, lung and prostate cancer. They also suggest that HER-2(9(369)) can be recognised by CTL, besides HLA-A2, also in the context of HLA-A3 and HLA-A26, thus increasing the applicability of HER-2(9(369))-based vaccinations in a considerably broader patients' population.Journal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe

Cell Encapsulation in Sub-mm Sized Gel Modules Using Replica Molding

For many types of cells, behavior in two-dimensional (2D) culture differs from that in three-dimensional (3D) culture. Among biologists, 2D culture on treated plastic surfaces is currently the most popular method for cell culture. In 3D, no analogous standard method—one that is similarly convenient, flexible, and reproducible—exists. This paper describes a soft-lithographic method to encapsulate cells in 3D gel objects (modules) in a variety of simple shapes (cylinders, crosses, rectangular prisms) with lateral dimensions between 40 and 1000 μm, cell densities of 105 – 108 cells/cm3, and total volumes between 1×10−7 and 8×10−4 cm3. By varying (i) the initial density of cells at seeding, and (ii) the dimensions of the modules, the number of cells per module ranged from 1 to 2500 cells. Modules were formed from a range of standard biopolymers, including collagen, Matrigel™, and agarose, without the complex equipment often used in encapsulation. The small dimensions of the modules allowed rapid transport of nutrients by diffusion to cells at any location in the module, and therefore allowed generation of modules with cell densities near to those of dense tissues (108 – 109 cells/cm3). This modular method is based on soft lithography and requires little special equipment; the method is therefore accessible, flexible, and well suited to (i) understanding the behavior of cells in 3D environments at high densities of cells, as in dense tissues, and (ii) developing applications in tissue engineering

Predicting Outcomes of Prostate Cancer Immunotherapy by Personalized Mathematical Models

Therapeutic vaccination against disseminated prostate cancer (PCa) is partially effective in some PCa patients. We hypothesized that the efficacy of treatment will be enhanced by individualized vaccination regimens tailored by simple mathematical models.We developed a general mathematical model encompassing the basic interactions of a vaccine, immune system and PCa cells, and validated it by the results of a clinical trial testing an allogeneic PCa whole-cell vaccine. For model validation in the absence of any other pertinent marker, we used the clinically measured changes in prostate-specific antigen (PSA) levels as a correlate of tumor burden. Up to 26 PSA levels measured per patient were divided into each patient's training set and his validation set. The training set, used for model personalization, contained the patient's initial sequence of PSA levels; the validation set contained his subsequent PSA data points. Personalized models were simulated to predict changes in tumor burden and PSA levels and predictions were compared to the validation set. The model accurately predicted PSA levels over the entire measured period in 12 of the 15 vaccination-responsive patients (the coefficient of determination between the predicted and observed PSA values was R(2) = 0.972). The model could not account for the inconsistent changes in PSA levels in 3 of the 15 responsive patients at the end of treatment. Each validated personalized model was simulated under many hypothetical immunotherapy protocols to suggest alternative vaccination regimens. Personalized regimens predicted to enhance the effects of therapy differed among the patients.Using a few initial measurements, we constructed robust patient-specific models of PCa immunotherapy, which were retrospectively validated by clinical trial results. Our results emphasize the potential value and feasibility of individualized model-suggested immunotherapy protocols