Tumor-associated antigen-based personalized dendritic cell vaccine in solid tumor patients.

Tumor-associated antigens (TAAs) have been tested in various clinical trials in cancer treatment but the patterns of specific T cell response to personalized TAA immunization remains to be fully understood. We report antigen-specific T cell responses in patients immunized with dendritic cell vaccines pulsed with personalized TAA panels. Tumor samples from patients were first analyzed to identify overexpressed TAAs. Autologous DCs were then transfected with pre-manufactured mRNAs encoding the full-length TAAs, overexpressed in the patients' tumors. Patients with glioblastoma multiforme (GBM) or advanced lung cancer received DC vaccines transfected with personalized TAA panels, in combination with low-dose cyclophosphamide, poly I:C, imiquimod and anti-PD-1 antibody. Antigen-specific T cell responses were measured. Safety and efficacy were evaluated. A total of ten patients were treated with DC vaccines transfected with personalized TAA panels containing 3-13 different TAAs. Among the seven patients tested for anti-TAA T cell responses, most of the TAAs induced antigen-specific CD4+ and/or CD8+ T cell responses, regardless of their expression levels in the tumor tissues. No Grade III/IV adverse events were observed among these patients. Furthermore, the treated patients were associated with favorable overall survival when compared to patients who received standard treatment in the same institution. Personalized TAA immunization-induced-specific CD4+ and CD8+ T cell responses without obvious autoimmune adverse events and was associated with favorable overall survival. These results support further studies on DC immunization with personalized TAA panels for combined immunotherapeutic regimens in solid tumor patients.Trial registration ClinicalTrials.gov, NCT02709616 (March, 2016), NCT02808364 (June 2016), NCT02808416 (June, 2016).

Two-stage deep neural network for diagnosing fungal keratitis via in vivo confocal microscopy images.

Timely and effective diagnosis of fungal keratitis (FK) is necessary for suitable treatment and avoiding irreversible vision loss for patients. In vivo confocal microscopy (IVCM) has been widely adopted to guide the FK diagnosis. We present a deep learning framework for diagnosing fungal keratitis using IVCM images to assist ophthalmologists. Inspired by the real diagnostic process, our method employs a two-stage deep architecture for diagnostic predictions based on both image-level and sequence-level information. To the best of our knowledge, we collected the largest dataset with 96,632 IVCM images in total with expert labeling to train and evaluate our method. The specificity and sensitivity of our method in diagnosing FK on the unseen test set achieved 96.65% and 97.57%, comparable or better than experienced ophthalmologists. The network can provide image-level, sequence-level and patient-level diagnostic suggestions to physicians. The results show great promise for assisting ophthalmologists in FK diagnosis.

A Multi-Element Expression Score Is A Prognostic Factor In Glioblastoma Multiforme.

PURPOSE: Glioblastoma multiforme (GBM) is a highly malignant tumor of the central nervous system. Although primary GBM patients receive extensive therapies, tumors may recur within months, and there is no objective and scientific method to predict prognosis. Adoptive immunotherapy holds great promise for GBM treatment. However, the expression profiles of the tumor-associated antigens (TAAs) and tumor immune microenvironment (TME) genes used in immunotherapy of GBM patients have not been fully described. The present study aimed to develop a predictive tool to evaluate patient survival based on full analysis of the expression levels of TAAs and TME genes. METHODS: Expression profiles of a panel of 87 TAAs and 8 TME genes significantly correlated with poor prognosis were evaluated in 44 GBM patients and 10 normal brain tissues using quantitative real-time polymerase chain reaction (qRT-PCR). A linear formula (the LASSO algorithm based in the R package) weighted by regression coefficients was used to develop a multi-element expression score to predict prognosis; this formula was cross-validated by the leave-one-out method in different GBM cohorts. RESULTS: After analysis of gene expression, clinical features, and overall survival (OS), a total of 8 TAAs (CHI3L1, EZH2, TRIOBP, PCNA, PIK3R1, PRKDC, SART3 and EPCAM), 1 TME gene (FOXP3) and 4 clinical features (neutrophil-to-lymphocyte (NLR), number of basophils (BAS), age and treatment with standard radiotherapy and chemotherapy) were included in the formula. There were significant differences between high and low scoring groups identified using the formula in different GBM cohorts (TCGA (n=732) and GEO databases (n=84)), implying poor and good prognosis, respectively. CONCLUSION: The multi-element expression score was significantly associated with OS of GBM patients. The improve understanding of TAAs and TMEs and well-defined formula could be implemented in immunotherapy for GBM to provide better care.