Pipeline improvement

myNEO prioritises on continuous improvement of the prediction pipeline by incorporating new algorithms based on in-house innovation and academic/industrial collaborations. Both private and public validated antigen sets are used to train, test and optimise the prediction platform.

The myNEO ImmunoEngine allows personalised detection of tumour-specific alterations and neoantigens. A variety of filters and ranking mechanisms, based on biological processes, enable retention of only the neoantigens providing the most clinical benefit. Continuous in-house and collaborative innovation efforts have focused on advanced algorithms providing a broad set of potential neoantigens with a high degree of confidence. These enable prediction of neoantigens in low mutational burden malignancies and further improve vaccine efficacy.

myNEO prioritises continuous improvement of the platform, focusing on technologies researched and developed in-house as well as via clinical and research collaborations to provide a broad set of potential neoantigens with a high degree of confidence. These novel algorithms have allowed myNEO to stepwise increase the ImmunoEngine platform performance, both by detecting novel types of tumour-specific alterations and by limiting the number of false positives via more stringent prediction algorithms.

Continuous improvement my NEO platform

Improvement is achieved by gathering and analysing data from public libraries and nondisclosed datasets from myNEO and its partners. Validated antigen sets - associated with clinical benefit - are used to train the myNEO machine learning algorithms to optimise the filtering thresholds and to continuously test the performance of the platform. Cohort studies allow interpretation of the patient’s specific cancer genome and transcriptome within the large set of other tumours with similar characteristics.

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