Descripción del proyecto
Un nuevo programa informático para diseñar vacunas contra el cáncer específicas para cada paciente
Los científicos consideran que la inmunoterapia podría curar el cáncer, pero esto implica la producción de vacunas personalizadas: un proceso en laboratorio complicado, largo y caro para atacar un caso específico de mutación. Por este motivo, los investigadores están desarrollando algoritmos para identificar neoantígenos inmunogénicos a través de datos de secuenciación de próxima generación obtenidos a partir de muestras de tumores. El proyecto MEDIVAC, financiado con fondos europeos, se propone impulsar este proceso aplicando el marco del aprendizaje automático de Oncolmmunity (OI). Supone una oportunidad para utilizar diversas bases de datos públicas y de otro tipo para mejorar considerablemente el rendimiento del proceso de identificación de neoantígenos relevantes. Así la medicina se aproximará mucho más a la producción de una prometedora vacuna personalizada para el tratamiento del cáncer.
Objetivo
Cancer is arguably the most feared of all diseases, destroying lives regardless of the age of its victims. Immunotherapies are currently regarded as the most promising avenue to delivering the holy grail of medicine i.e. providing a cure for cancer. Despite their Nobel-winning status, personalisation of immunotherapies remains akey challenge to which no cost-effective solution currently exists. Current methods for identifying the immunogenic neoantigens required to design patient-specific cancer vaccines typically utilize next generation sequencing (NGS) analysis of DNA and RNA coupled with wet lab methods (e.g. spectroscopy). However, these approaches are time consuming to perform, expensive and not readily scalable–which currently prohibits the mass roll-out of personalised cancer vaccines.
Despite the fact that intensive research has been dedicated to developing prediction algorithms which can identify immunogenic neoantigens from NGS data from tumor samples, their accuracy has not yet reached a competitive performance compared to wet lab methods. To bridge this gap, OncoImmunity (OI) has developed a comprehensive machine learning framework, trained using public and proprietary datasets to optimise performance. Once fed with patient NGS data from healthy and tumor tissue, OI’s algorithms identifies the most clinically relevant neoantigen candidates, with an unmatched accuracy (4-fold increase of prediction accuracy), which can be subsequently engineering into personalised vaccine cancer constructs.
Considering the potential of personalised therapy within cancer immunotherapy, OI’s core technology meets all the requirements to become a key enabling technology, providing cost-effective, scalable and sensitive identification of clinically relevant targets for vaccine development. Thus, it has the potential to serve as a cornerstone to revolutionize the fight against cancer – while untapping an immense business opportunity to fuel our company’s growth.
Ámbito científico
- medical and health sciencesbasic medicinepharmacology and pharmacypharmaceutical drugsvaccines
- medical and health sciencesclinical medicineoncology
- natural sciencesbiological sciencesgeneticsRNA
- medical and health sciencesbasic medicineimmunologyimmunotherapy
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Palabras clave
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-SMEInst-2018-2020-2
Régimen de financiación
SME-2 - SME instrument phase 2Coordinador
0379 OSLO
Noruega
Organización definida por ella misma como pequeña y mediana empresa (pyme) en el momento de la firma del acuerdo de subvención.