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Machine learning software to design personalized neoantigen vaccines tailored to specific vaccine delivery systems

Project description

New software to design patient-specific cancer vaccines

Scientists expect personalised immunotherapy could cure cancer but this involves personalised vaccination – a complicated, time-consuming, costly laboratory process to target a specific case of mutation. For this reason, researchers are developing algorithms to identify immunogenic neoantigens via next generation sequencing (NGS) data from tumour samples. The EU-funded MEDIVAC project aims to support this process by applying the machine learning framework from Oncolmmunity (OI). It provides an opportunity to use several public and other databases to significantly increase the performance of the identification process for relevant neoantigens. This will bring medical science much closer to introducing a promising personalised vaccination for cancer therapy.


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.

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Net EU contribution
€ 2 200 406,25
Ullernchausseen 64
0379 Oslo

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Norge Oslo og Viken Oslo
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Other funding
€ 943 031,25