Thursday 1 October 2020

(internal) Shared target discovery *ongoing*

Although myNEO’s focus area of personalised neoantigen-directed immunotherapy is showing great potential, it often comes with logistic and economical setbacks depending on the vaccination platform. Also, certain genomic profiles tend to be abundantly represented in tumour subgroups, which opens up to the possibility of off-the-shelf neoantigen-driven therapies for specific patient populations. As such, myNEO has joined the efforts in the development of pan-cancer or pan-patient cancer-specific treatments. To achieve this, various data-types – including but not limited to for example genomic, methylomic, and proteomic (Figure 1) – from multiple public and private resources have been combined in a state-of-the-art integrated multi-omics analysis. This, to reveal cancer-specific and -associated neoantigens shared by a significant portion of the patient population.

Figure 1. An overview of the datatypes and sources integrated in the shared variant discovery workflow. The genomic data ranges from variant status over (differential) expression analysis to proteomic data to assess population coverage and validate the findings.

Identification of such pan-patient shared neoantigens thus allows for more broadly applicable, off-the-shelf treatment options, that can be initiated almost immediately upon diagnosis. In the meantime, if needed, a more truly personalised treatment could be designed based on an assessment of the patient’s specific neoantigen repertoire. It is our believe that such a combined approach will have a synergistic tumouricidal effect and increase the treatment’s success rate.

At a first stage, the project has identified tumour-enriched gene-expression, shared SNVs/indels as well as intron-retention (IR) events (Figure 2). Extension onto fusion-genes, post-translational modifications, non-canonical expression, and copy-number-variation, among others, allows to even further broaden the scope of shared neoantigen identification. Preliminary results from this bioinformatics workflow have elucidated promising candidates in colon cancer for example, which have been validated via evidence in relevant proteomic sample cohorts. Additional in-depth analyses are ongoing in other tumour-stages (e.g. primary, relapse, and metastatic samples) and -types to extend the pan-cancer shared neoantigen portfolio of myNEO.

Figure 2. Genomic and clinical information are combined in a state-of-the-art workflow to identify features specifically enriched in certain sample populations.

Since the analysis includes samples across various tumour-stages, such a repertoire could also prove to be a valuable asset to chart mutational evolution upon treatment. For example, by integrating it in myNEO neoantigen prediction pipeline, it could help identify patient-specific mutations likely to influence tumour-relapse and guide the development of a personalised cancer vaccine to anticipate such events.