Identification of MAGE-3 Epitopes Presented by HLA-DR Molecules to CD4+ T Lymphocytes

MAGE-type genes are expressed by many tumors of different histological types and not by normal cells, except for male germline cells, which do not express major histocompatibility complex (MHC) molecules. Therefore, the antigens encoded by MAGE-type genes are strictly tumor specific and common to many tumors. We describe here the identification of the first MAGE-encoded epitopes presented by histocompatibility leukocyte antigen (HLA) class II molecules to CD4+ T lymphocytes. Monocyte-derived dendritic cells were loaded with a MAGE-3 recombinant protein and used to stimulate autologous CD4+ T cells. We isolated CD4+ T cell clones that recognized two different MAGE-3 epitopes, MAGE-3114–127 and MAGE-3121–134, both presented by the HLA-DR13 molecule, which is expressed in 20% of Caucasians. The second epitope is also encoded by MAGE-1, -2, and -6. Our procedure should be applicable to other proteins for the identification of new tumor-specific antigens presented by HLA class II molecules. The knowledge of such antigens will be useful for evaluation of the immune response of cancer patients immunized with proteins or with recombinant viruses carrying entire genes coding for tumor antigens. The use of antigenic peptides presented by class II in addition to peptides presented by class I may also improve the efficacy of therapeutic antitumor vaccination

MAP-INFORMED UNROLLED ALGORITHMS FOR HYPER-PARAMETER ESTIMATION

International audienceHyper-parameter tuning, and especially regularisation parameter estimation, is a challenging but essential task when solving inverse problems. The solution is obtained here through the minimization of a functional composed of a data fidelity term and a regularization term. Those terms are balanced through a (or several) regularisation parameter(s) whose estimation is made under an unrolled strategy together with the inverse problem solving. The resulting network is trained while incorporating information on the model through Maximum a Posteriori estimation which drastically decreases the amount of data needed for the training and results in better estimation results. The performances are demonstrated in a deconvolution context where the regularisation is performed in the wavelet domain

Les cellulites cervico-faciales odontogènes (description, physiopathologie et thérapeutique)

LYON1-BU Santé Odontologie (693882213) / SudocSudocFranceF

Keynote Lecture: From the identification of tumor antigens recognized by T cells to immunotherapy

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