The aim of antitumor immunotherapy is to induce CTL responses against autologous tumors. Previous work has shown that fusion of human dendritic cells and autologous tumor cells induce CTL responses against autologous tumor cells in vitro. However, in the clinical setting of patients with colorectal carcinoma, a major difficulty is the preparation of sufficient amounts of autologous tumor cells. In the present study, autologous dendritic cells from patients with colorectal carcinoma were fused to allogeneic colorectal tumor cell line, COLM-6 (HLA-A2⁻/HLA-24⁻), carcinoembryonic antigen (CEA)⁺, and MUC1⁺ as an alternative strategy to deliver shared colorectal carcinoma antigens to dendritic cells. Stimulation of autologous T cells by the fusion cells generated with autologous dendritic cells (HLA-A2⁺ and/or HLA-A24⁺) and allogeneic COLM-6 resulted in MHC class I– and MHC class II–restricted proliferation of CD4⁺ and CD8⁺ T cells, high levels of IFN-γ production in both CD4⁺ and CD8⁺ T cells, and the simultaneous induction of CEA- and MUC1-specific CTL responses restricted by HLA-A2 and/or HLA-A24. Finally, CTL induced by dendritic cell/allogeneic COLM-6 fusion cells were able to kill autologous colorectal carcinoma by HLA-A2- and/or HLA-A24-restricted mechanisms. The demonstration of CTL activity against shared tumor-associated antigens using an allogeneic tumor cell line, COLM-6, provides that the presence of alloantigens does not prevent the development of CTL with activity against autologous colorectal carcinoma cells. The fusion of allogeneic colorectal carcinoma cell line and autologous dendritic cells could have potential applicability to the field of antitumor immunotherapy through the cross-priming against shared tumor antigens and provides a platform for adoptive immunotherapy.

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A highly optimized Monte Carlo (MC) code package for simulating light transport is developed on the latest graphics processing unit (GPU) built for general-purpose computing from NVIDIA - the Fermi GPU. In biomedical optics, the MC method is the gold standard approach for simulating light transport in biological tissue, both due to its accuracy and its flexibility in modelling realistic, heterogeneous tissue geometry in 3-D. However, the widespread use of MC simulations in inverse problems, such as treatment planning for PDT, is limited by their long computation time. Despite its parallel nature, optimizing MC code on the GPU has been shown to be a challenge, particularly when the sharing of simulation result matrices among many parallel threads demands the frequent use of atomic instructions to access the slow GPU global memory. This paper proposes an optimization scheme that utilizes the fast shared memory to resolve the performance bottleneck caused by atomic access, and discusses numerous other optimization techniques needed to harness the full potential of the GPU. Using these techniques, a widely accepted MC code package in biophotonics, called MCML, was successfully accelerated on a Fermi GPU by approximately 600x compared to a state-of-the-art Intel Core i7 CPU. A skin model consisting of 7 layers was used as the standard simulation geometry. To demonstrate the possibility of GPU cluster computing, the same GPU code was executed on four GPUs, showing a linear improvement in performance with an increasing number of GPUs. The GPU-based MCML code package, named GPU-MCML, is compatible with a wide range of graphics cards and is released as an open-source software in two versions: an optimized version tuned for high performance and a simplified version for beginners (http://code.google.com/p/gpumcml).