In the last years, multiple efforts have been made to accurately predict neoantigens derived from somatic mutations in cancer patients, either to develop personalized therapeutic vaccines or to study immune responses after cancer immunotherapy. In this context, the increasing accessibility of paired whole-exome sequencing (WES) of tumor biopsies and matched normal tissue as well as RNA sequencing (RNA-Seq) has provided a basis for the development of bioinformatics tools that predict and prioritize neoantigen candidates. Most pipelines rely on the binding prediction of candidate peptides to the patient’s major histocompatibility complex (MHC), but these methods return a high number of false positives since they lack information related to other features that influence T cell responses to neoantigens. This review explores available computational methods that incorporate information on T cell preferences to predict their activation after encountering a peptide-MHC complex. Specifically, methods that predict i) biological features that may increase the availability of a neopeptide to be exposed to the immune system, ii) metrics of self-similarity representing the chances of a neoantigen to break immune tolerance, iii) pathogen immunogenicity, and iv) tumor immunogenicity. Also, this review describes the characteristics of these tools and addresses their performance in the context of a novel benchmark dataset of experimentally validated neoantigens from patients treated with a melanoma vaccine (VACCIMEL) in a phase II clinical study. The overall results of the evaluation indicate that current tools have a limited ability to predict the activation of a cytotoxic response against neoantigens. Based on this result, the limitations that make this problem an unsolved challenge in immunoinformatics are discussed.

Immunoinformatics is an emerging area focused on development and applications of methods used to facilitate vaccine development. There is a growing interest in the field of vaccinology centered on the new omic science named ‘vaccinomics’. However, this approach has not succeeded to provide a solution against major infections affecting both animals and humans, since tick vaccines are still being developed based on conventional biochemical or immunological methods to dissect the molecular structure of the pathogen, looking for a candidate antigen. The availability of complete genomes and the novel advanced technologies, such as data mining, bioinformatics, microarrays, and proteomics, have revolutionized the approach to vaccine development and provided a new impulse to tick research. The aim of this review is to explore how modern vaccinology will contribute to the discovery of new candidate antigens and to understand the research process to improve existing vaccines. Under this concept, the omic age of ticks will make it possible to design vaccines starting from a prediction based on the in silico analysis of gene sequences obtained by data mining using computer algorithms, without the need to keep the pathogen growing in vitro. This new genome-based approach has been named “reverse vaccinology 3.0” or “vaccinomics 1.0” and can be applied to ticks.