Tolerance toward tumor antigens, which are shared by normal tissues, have often limited the efficacy of cancer vaccines. However, wild type epitopes can be tweaked to activate cross-reactive T-cell clones, resulting in antitumor activity. The design of these analogs (i.e., heteroclitic peptides) can be difficult and time-consuming since no automated in silico tools are available. Hereby we describe the development of an in silico framework to improve the selection of heteroclitic peptides. The Epitope Discovery and Improvement System (EDIS) was first validated by studying the model antigen SIINFEKL. Based on artificial neural network (ANN) predictions, we selected two mutant analogs that are characterized by an increased MHC-I binding affinity (SIINFAKL) or increased TCR stimulation (SIIWFEKL). Therapeutic vaccination using optimized peptides resulted in enhanced antitumor activity and against B16.OVA melanomas in vivo. The translational potential of the EDIS platform was further demonstrated by studying the melanoma-associated antigen tyrosinase related protein 2 (TRP2). Following therapeutic immunization with the EDIS-derived epitope SVYDFFAWL, a significant reduction in the growth of established B16.F10 tumors was observed, suggesting a break in the tolerance toward the wild type epitope. Finally, we tested a multi vaccine approach, demonstrating that combination of wild type and mutant epitopes targeting both TRP2 and OVA antigens increases the antitumor response. In conclusion, by taking advantage of available prediction servers and molecular dynamics simulations, we generated an innovative platform for studying the initial sequences and selecting lead candidates with improved immunological features. Taken together, EDIS is the first automated algorithm-driven platform to speed up the design of heteroclitic peptides that can be publicly queried online.
Keywords: Cancer vaccine; heteroclitic peptides; immunotherapy; in silico; prediction servers; tumor antigens.