Usefulness of Docking and Molecular Dynamics in Selecting Tumor Neoantigens to Design Personalized Cancer Vaccines: A Proof of Concept.

Personalized cancer vaccines based on neoantigens are a new and promising treatment for cancer; however, there are still multiple unresolved challenges to using this type of immunotherapy. Among these, the effective identification of immunogenic neoantigens stands out, since the in silico tools used generate a significant portion of false positives. Inclusion of molecular simulation techniques can refine the results these tools produce. In this work, we explored docking and molecular dynamics to study the association between the stability of peptide-HLA complexes and their immunogenicity, using as a proof of concept two HLA-A2-restricted neoantigens that were already evaluated in vitro. The results obtained were in accordance with the in vitro immunogenicity, since the immunogenic neoantigen ASTN1 remained bound at both ends to the HLA-A2 molecule. Additionally, molecular dynamic simulation suggests that position 1 of the peptide has a more relevant role in stabilizing the N-terminus than previously proposed. Likewise, the mutations may have a "delocalized" effect on the peptide-HLA interaction, which means that the mutated amino acid influences the intensity of the interactions of distant amino acids of the peptide with the HLA. These findings allow us to propose the inclusion of molecular simulation techniques to improve the identification of neoantigens for cancer vaccines.

Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding.

The search for an effective drug is still urgent for COVID-19 as no drug with proven clinical efficacy is available. Finding the new purpose of an approved or investigational drug, known as drug repurposing, has become increasingly popular in recent years. We propose here a new drug repurposing approach for COVID-19, based on knowledge graph (KG) embeddings. Our approach learns "ensemble embeddings" of entities and relations in a COVID-19 centric KG, in order to get a better latent representation of the graph elements. Ensemble KG-embeddings are subsequently used in a deep neural network trained for discovering potential drugs for COVID-19. Compared to related works, we retrieve more in-trial drugs among our top-ranked predictions, thus giving greater confidence in our prediction for out-of-trial drugs. For the first time to our knowledge, molecular docking is then used to evaluate the predictions obtained from drug repurposing using KG embedding. We show that Fosinopril is a potential ligand for the SARS-CoV-2 nsp13 target. We also provide explanations of our predictions thanks to rules extracted from the KG and instanciated by KG-derived explanatory paths. Molecular evaluation and explanatory paths bring reliability to our results and constitute new complementary and reusable methods for assessing KG-based drug repurposing.

Personalized neoantigen-based cancer immunotherapy. A literature review / Inmunoterapia personalizada contra el cáncer basada en neoantígenos. Revisión de la literatura

Abstract Introduction: The progress made in cancer immunotherapy and the clinical response of patients who have undergone this type of therapy have made it the fourth pillar of cancer treatment. Objective: To briefly describe the biological rationale of personalized neoantigen-based cancer immunotherapy, the current perspectives regarding its development, and some of the clinical outcomes achieved with this therapy. Materials and methods: A literature search was performed in PubMed, Scopus and EBSCO using the following search strategy: type of articles: original experimental studies, clinical trials, and narrative and systematic reviews addressing methods to identify mutations found in tumors and cancer immunotherapy strategies based on neoantigen-based vaccines; study population: humans and animal models; publication period: January 1989 - December 2019; language: English and Spanish; search terms: "Immunotherapy", "Neoplasms", "Mutation" and "Cancer Vaccines". Results: The initial search started with 1 344 records. Once duplicates were removed (n=176), 780 studies were excluded after reading their abstract and title. The full text of 338 articles was read to confirm which met the inclusion criteria, finally including 73 studies for full analysis. All articles retrieved were published in English and were mainly conducted in the USA (43.83%) and Germany (23.65%). In the case of original studies (n=43), 20 were performed in humans only, 9 in animals only, 2 in both models, and 12 used in silico methodology. Conclusion: Personalized cancer immunotherapy with tumor neoantigen-based vaccines is strongly emerging as a new alternative to treat cancer. However, to achieve its appropriate implementation, it is necessary to use it in combination with conventional treatments, produce more knowledge that helps clarify cancer immunobiology, and reduce the costs associated with its production.

Resumen Introducción. Los avances que se han hecho en inmunoterapia contra el cáncer y la respuesta clínica de los pacientes que han recibido este tipo de terapia la han convertido en el cuarto pilar para el tratamiento del cáncer. Objetivo. Describir brevemente el fundamento biológico de la inmunoterapia personalizada contra el cáncer basada en neoantígenos, las perspectivas actuales de su desarrollo y algunos resultados clínicos de esta terapia. Materiales y métodos. Se realizó una búsqueda de la literatura en PubMed, Scopus y EBSCO utilizando la siguiente estrategia de búsqueda: tipo de artículos: estudios experimentales originales, ensayos clínicos y revisiones narrativas y sistemáticas sobre métodos de identificación de mutaciones generadas en los tumores y estrategias de inmunoterapia del cáncer con vacunas basadas en neoantígenos; población de estudio: humanos y modelos animales; periodo de publicación: enero de 1989 a julio de 2019; idioma: inglés y español; términos de búsqueda: "Immunotherapy", "Neoplasms", "Mutation" y "Cancer Vaccines". Resultados. La búsqueda inicial arrojó 1 344 registros; luego de remover duplicados (n = 176), 780 fueron excluidos después de leer su resumen y título, y se evaluó el texto completo de 338 para verificar cuáles cumplían con los criterios de inclusión, seleccionándose finalmente 73 estudios para análisis completo. Todos los artículos recuperados se publicaron en inglés, y fueron realizados principalmente en EE. A (43.83%) y Alemania (23.65%). En el caso de los estudios originales (n=43), 20 se realizaron únicamente en humanos, 9 solo en animales, 2 en ambos modelos, y 12 usaron metodología in silico. Conclusión. La inmunoterapia personalizada contra el cáncer con vacunas basadas en neoantígenos tumorales se está convirtiendo de forma contundente en una nueva alternativa para tratar el cáncer. Sin embargo, para lograr su implementación adecuada, es necesario usarla en combinación con tratamientos convencionales, generar más conocimiento que contribuya a aclarar la inmunobiología del cáncer y reducir los costos asociados con su producción.