Genetic studies associate Parkinson’s disease with alleles of the major histocompatibility complex1–3. We find that a defined set of peptides derived from α-synuclein, a protein aggregated in Parkinson’s disease4, act as antigenic epitopes displayed by these alleles and drive helper and cytotoxic T cell responses in Parkinson’s disease patients. These responses may explain the association of Parkinson’s disease with alleles of the acquired immune system.

Purpose: We generated a humanized antibody, HuLuc63, which specifically targets CS1 (CCND3 subset 1, CRACC, and SLAMF7), a cell surface glycoprotein not previously associated with multiple myeloma. To explore the therapeutic potential of HuLuc63 in multiple myeloma, we examined in detail the expression profile of CS1, the binding properties of HuLuc63 to normal and malignant cells, and the antimyeloma activity of HuLuc63 in preclinical models. Experimental Design: CS1 was analyzed by gene expression profiling and immunohistochemistry of multiple myeloma samples and numerous normal tissues. HuLuc63-mediated antimyeloma activity was tested in vitro in antibody-dependent cellular cytotoxicity (ADCC) assays and in vivo using the human OPM2 xenograft model in mice.Results: CS1mRNA was expressed in >90% of 532 multiple myeloma cases, regardless of cytogenetic abnormalities. Anti-CS1antibody staining of tissues showed strong staining of myeloma cells in all plasmacytomas and bone marrow biopsies. Flow cytometric analysis of patient samples using HuLuc63 showed specific staining of CD138+ myeloma cells, natural killer (NK), NK-like Tcells, and CD8+ Tcells, with no binding detected on hematopoietic CD34+ stem cells. HuLuc63 exhibited significant in vitro ADCC using primary myeloma cells as targets and both allogeneic and autologous NK cells as effectors. HuLuc63 exerted significant in vivo antitumor activity, which depended on efficient Fc-CD16 interaction as well as the presence of NK cells in the mice. Conclusions: These results suggest that HuLuc63 eliminates myeloma cells, at least in part, via NK-mediated ADCC and shows the therapeutic potential of targeting CS1with HuLuc63 for the treatment of multiple myeloma.

Currently, no approved monoclonal antibody (mAb) therapies exist for human multiple myeloma (MM). Here we characterized cell surface CS1 as a novel MM antigen and further investigated the potential therapeutic utility of HuLuc63, a humanized anti-CS1 mAb, for treating human MM. CS1 mRNA and protein was highly expressed in CD138-purified primary tumor cells from the majority of MM patients (more than 97%) with low levels of circulating CS1 detectable in MM patient sera, but not in healthy donors. CS1

Computational prediction of HLA class II restricted T cell epitopes has great significance in many immunological studies including vaccine discovery. In recent years, prediction of HLA class II binding has improved significantly but a strategy to globally predict the most dominant epitopes has not been rigorously defined. Using human immunogenicity data associated with sets of 15-mer peptides overlapping by 10 residues spanning over 30 different allergens and bacterial antigens, and HLA class II binding prediction tools from the Immune Epitope Database and Analysis Resource (IEDB), we optimized a strategy to predict the top epitopes recognized by human populations. The most effective strategy was to select peptides based on predicted median binding percentiles for a set of seven DRB1 and DRB3/4/5 alleles. These results were validated with predictions on a blind set of 15 new allergens and bacterial antigens. We found that the top 21% predicted peptides (based on the predicted binding to seven DRB1 and DRB3/4/5 alleles) were required to capture 50% of the immune response. This corresponded to an IEDB consensus percentile rank of 20.0, which could be used as a universal prediction threshold. Utilizing actual binding data (as opposed to predicted binding data) did not appreciably change the efficacy of global predictions, suggesting that the imperfect predictive capacity is not due to poor algorithm performance, but intrinsic limitations of HLA class II epitope prediction schema based on HLA binding in genetically diverse human populations.

T cell receptor (TCR) antigen–specific recognition is essential for the adaptive immune system. However, building a TCR-antigen interaction map has been challenging due to the staggering diversity of TCRs and antigens. Accordingly, highly multiplexed dextramer-TCR binding assays have been recently developed, but the utility of the ensuing large datasets is limited by the lack of robust computational methods for normalization and interpretation. Here, we present a computational framework comprising a novel method, ICON (Integrative COntext-specific Normalization), for identifying reliable TCR-pMHC (peptide–major histocompatibility complex) interactions and a neural network–based classifier TCRAI that outperforms other state-of-the-art methods for TCR-antigen specificity prediction. We further demonstrated that by combining ICON and TCRAI, we are able to discover novel subgroups of TCRs that bind to a given pMHC via different mechanisms. Our framework facilitates the identification and understanding of TCR-antigen–specific interactions for basic immunological research and clinical immune monitoring.