Mutation and expression: Once biopsies have been performed, PIONEER™ identifies cancer-specific mutations by comparing DNA sequencing data from the tumor sample(s) and normal tissue sample using our proprietary AI-based somatic variant caller. Then, the expression levels of the genes found within the mutation are determined by analyzing tumor RNA sequencing data. PIONEER™ also calculates the mutation-specific expression levels using an in-house developed computational model.

Translation: PIONEER™ determines the effects of each cancer-specific mutation, as not all mutations result in altered protein sequences. Once non-synonymous mutations (where both the DNA sequence and the encoded amino acid are altered) are identified, the surrounding coding regions are translated into amino acid sequences, generating cancer-specific neoantigen sequences.

Personalized cancer immunotherapy: The identified neoantigens are put into our proprietary AI-based tool along with the patient’s HLA type, to identify the neoantigens that will be presented on the patient’s MHC molecules. PIONEER™ also predicts the likelihood of these neoantigens eliciting a T-cell response, which will trigger an immune response and tumor cell death. Additionally, for patients where DNA sequencing data from multiple tumor biopsies is available, PIONEER™ seamlessly integrates the data from each biopsy to target tumor-defining neoantigens. This means that, even though tumors continuously acquire novel mutations and do not necessarily encode and express the same neoantigens, PIONEER™ allows for the systematic eradication of the whole tumor as well as potential metastases in the patient.

Neoantigen administration: The selected neoantigens are manufactured and administered alongside standard-of-care, producing an anti-tumor effect in the patient.

We have discovered that endogenous retrovirus (ERVs) are a potential additional source of cancer antigens that can be used in personalized cancer immunotherapies to complement neoantigens. We have developed a bioinformatics model that can identify ERVs based on sequencing data and design a personalized ERV antigen treatment. We have proof of concept that our method works in preclinical models. 

For Evaxion, it enables us to facilitate and improve our personalized cancer immunotherapy pipeline. We believe that adding ObsERV™ to PIONEER™ would potentially allow us to further improve the personalized cancer immunotherapies we design, targeting cancer indications that generally have fewer neoantigens. Evaxion has already started the discussion with the reg. authorities on how to include these ERV targets in a personalized neoantigen treatment in future clinical trials.