Vaccination-induced functional competence of circulating human tumor-specific CD8 T-cells.

T-cells specific for foreign (e.g., viral) antigens can give rise to strong protective immune responses, whereas self/tumor antigen-specific T-cells are thought to be less powerful. However, synthetic T-cell vaccines composed of Melan-A/MART-1 peptide, CpG and IFA can induce high frequencies of tumor-specific CD8 T-cells in PBMC of melanoma patients. Here we analyzed the functionality of these T-cells directly ex vivo, by multiparameter flow cytometry. The production of multiple cytokines (IFNγ, TNFα, IL-2) and upregulation of LAMP-1 (CD107a) by tumor (Melan-A/MART-1) specific T-cells was comparable to virus (EBV-BMLF1) specific CD8 T-cells. Furthermore, phosphorylation of STAT1, STAT5 and ERK1/2, and expression of CD3 zeta chain were similar in tumor- and virus-specific T-cells, demonstrating functional signaling pathways. Interestingly, high frequencies of functionally competent T-cells were induced irrespective of patient's age or gender. Finally, CD8 T-cell function correlated with disease-free survival. However, this result is preliminary since the study was a Phase I clinical trial. We conclude that human tumor-specific CD8 T-cells can reach functional competence in vivo, encouraging further development and Phase III trials assessing the clinical efficacy of robust vaccination strategies.

Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.