Descrizione del progetto
Nuovo software per la progettazione di vaccini contro il cancro specifici del paziente
Gli scienziati presumono che l’immunoterapia personalizzata possa riuscire a curare il cancro, ma ciò comporta una vaccinazione personalizzata, un processo di laboratorio complicato, che richiede tempo e denaro per affrontare i casi specifici di mutazione. Per questo motivo, i ricercatori stanno sviluppando algoritmi per identificare i neoantigeni immunogenici tramite i dati di sequenziamento di nuova generazione (NGS, Next Generation Sequencing) provenienti da campioni di tumore. Il progetto MEDIVAC, finanziato dall’UE, mira a sostenere questo processo applicando la struttura di apprendimento automatico di Oncolmmunity (OI). Esso offre l’opportunità di utilizzare diverse banche dati pubbliche e di altro tipo per aumentare significativamente le prestazioni del processo di identificazione per i neoantigeni pertinenti. Ciò porterà la scienza medica molto più vicino all’introduzione di una promettente vaccinazione personalizzata per la terapia del cancro.
Obiettivo
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.
Campo scientifico
- 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
Parole chiave
Programma(i)
Argomento(i)
Invito a presentare proposte
Vedi altri progetti per questo bandoBando secondario
H2020-SMEInst-2018-2020-2
Meccanismo di finanziamento
SME-2 - SME instrument phase 2Coordinatore
0379 OSLO
Norvegia
L’organizzazione si è definita una PMI (piccola e media impresa) al momento della firma dell’accordo di sovvenzione.