It’s long overdue, but we are proud to announce the launch of our new website! Not only does it provide a more responsive environment, but it also captures better our devotion to give each cancer patient with hard-to-treat and relapsed tumours a chance through personalised immunotherapy. The new website is easier to navigate through and shows what we’ve developed over the past years, our platform capabilities, as well as our mission and focus points.
More exciting information regarding the work we've been doing, the projects we've initiated, and the partnerships we have closed will follow in the coming weeks.
“myNEO is devoted to give patients with hard-to-treat and relapsed tumours a chance through personalised immunotherapy”
The new website expands better on how we discover tumour-specific alterations and surface molecules, and how we evaluate the immunogenic impact of these alterations. It is myNEO’s mission to efficiently identify and validate neoantigens in a rapid, cost-efficient manner, key to ensure a strong, long-lived, and broad immune response leading to tumour regression. Next to the impact on personalised immunotherapy, the website portrays a comprehensive description on the platform's usability within target and marker discovery is comprehensively as well.
Major technical improvements and leaps have been in the field of non-canonical shared antigen detection (such as tumour-specific smORFs in lncRNAs, alternative transcripts, etc.). By exploring such a broad antigen landscape, the platform can provide benefit in patients with a barely mutated cold tumour, where other forms of immunotherapy have not.
Also, results from two trained machine learning algorithms have been beyond expectations. The neoMS neural network focuses on predicting the probability of peptide presence on the tumour surface based on gathered MS datasets, and it has vastly improved upon the sensitivity of the currently available tools predicting solely peptide-MHC binding affinity. In addition, the neoIM algorithm (random forest-based) has shown interesting results when used to predict potential immunogenicity and T-cell response induction, both in oncology and general infectious disease setting.