Performance Evaluation of MHC Class-I Binding Prediction Tools Based on an Experimentally Validated MHC-Peptide Binding Data Set.

Knowing whether a protein can be processed and the resulting peptides presented by major histocompatibility complex (MHC) is highly important for immunotherapy design. MHC ligands can be predicted by in silico peptide-MHC class-I binding prediction algorithms. However, prediction performance differs considerably, depending on the selected algorithm, MHC class-I type, and peptide length. We evaluated the prediction performance of 13 algorithms based on binding affinity data of 8- to 11-mer peptides derived from the HPV16 E6 and E7 proteins to the most prevalent human leukocyte antigen (HLA) types. Peptides from high to low predicted binding likelihood were synthesized, and their HLA binding was experimentally verified by in vitro competitive binding assays. Based on the actual binding capacity of the peptides, the performance of prediction algorithms was analyzed by calculating receiver operating characteristics (ROC) and the area under the curve (AROC). No algorithm outperformed others, but different algorithms predicted best for particular HLA types and peptide lengths. The sensitivity, specificity, and accuracy of decision thresholds were calculated. Commonly used decision thresholds yielded only 40% sensitivity. To increase sensitivity, optimal thresholds were calculated, validated, and compared. In order to make maximal use of prediction algorithms available online, we developed MHCcombine, a web application that allows simultaneous querying and output combination of up to 13 prediction algorithms. Taken together, we provide here an evaluation of peptide-MHC class-I binding prediction tools and recommendations to increase prediction sensitivity to extend the number of potential epitopes applicable as targets for immunotherapy.

The ZEB1 pathway links glioblastoma initiation, invasion and chemoresistance.

Glioblastoma remains one of the most lethal types of cancer, and is the most common brain tumour in adults. In particular, tumour recurrence after surgical resection and radiation invariably occurs regardless of aggressive chemotherapy. Here, we provide evidence that the transcription factor ZEB1 (zinc finger E-box binding homeobox 1) exerts simultaneous influence over invasion, chemoresistance and tumourigenesis in glioblastoma. ZEB1 is preferentially expressed in invasive glioblastoma cells, where the ZEB1-miR-200 feedback loop interconnects these processes through the downstream effectors ROBO1, c-MYB and MGMT. Moreover, ZEB1 expression in glioblastoma patients is predictive of shorter survival and poor Temozolomide response. Our findings indicate that this regulator of epithelial-mesenchymal transition orchestrates key features of cancer stem cells in malignant glioma and identify ROBO1, OLIG2, CD133 and MGMT as novel targets of the ZEB1 pathway. Thus, ZEB1 is an important candidate molecule for glioblastoma recurrence, a marker of invasive tumour cells and a potential therapeutic target, along with its downstream effectors.