Diagnosis and prognosis

The deep screening of the tumour sample and the extensive fingerprint it provides, are used to evaluate known clinical parameters. Specific mutations in genes or gene regions that have known associations with the prognosis of the patient are analysed and, based on the results, an optimal treatment plan is chosen. Not all patients benefit in early stage from immunotherapy and the myNEO platform analyses its necessity and feasibility compared to other treatment directions. Predictions are made based on the clinical results of the therapy correlated with the genomic data of the patient.

Thanks to the extensiveness of the sequencing analysis, our NGS-based approach can provide more information than standard gene panels in the current clinical routine. So next to the tumour-specific antigens, additional clinical parameters are extracted from the sequencing data as well including MHC (sub) type, TMB, CTA gene expression, clinical tumour classification etc. All these parameters are used to classify patients into treatment groups in a personalised manner.

Next to analysing already defined and validated markers, myNEO is continuously screening gathered datasets in the search for novel markers for diagnostic and prognostic purposes, similarly to the approach for shared target discovery. These markers can be employed to enhance early detection of extremely aggressive tumours to improve therapy efficacy predictions and to distinguish responders from non-responders in clinical trials. For clinical practice and translational studies, there is a high need for discovering novel markers based on non-invasive samples (i.e. ctDNA detection in blood).

As an example, the efficacy of immunotherapy for a specific patient is currently being approximated by the number of mutations present in the tumour (TMB), combined with the immune signature. However, this classifier is far from ideal, limiting the number of patients undergoing successful results in clinical trials. myNEO is thus aiding companies in increasing the performance of such classifiers, by co-discovering new diagnostic markers.