myNEO is devoted to enhancing vaccine efficacy by supporting optimised delivery of neoantigens through the use of bioinformatic prediction models for selection of the most optimal sequence. These neoantigen sequences are primed for maximum stability, translational efficiency, and specificity as well as to obtain the best immunogenic features.
After neoantigen prediction and careful selection of the correct surface targets from a patient sample to direct the vaccine against, it is still of the essence to design an optimal vaccination construct containing the chosen antigens. Often a combination cocktail of multiple neoantigens is employed, to address tumour clonality and limit tumour escape. Several new vaccine administration methods have allowed for delivery of more than 100 antigens, by utilising “multi-epitope constructs” containing several selected targets. These constructs require extra optimisation through meticulous selection to ensure boosted immune response uniquely against the tumour antigens.
Targeting more epitopes at once logically will lead to a broader immune response and consequently a stronger immune response limiting the chance of outgrowth of the tumour cell missing the targeted alteration (D’Alise et al. 2019). However, it has been shown in literature that too many antigens might lead to oversaturation of MHC and by consequence lower vaccine efficacy. It is stated that a high number of epitopes in vaccines may not be advisable, as suboptimal epitopes may interfere and immunodominance may occur (Tokuyasa et al. 2018). Therefore, myNEO generally aims to select approximately 20 neoantigens. Next to determining the total number of administered neoepitopes in a vaccine, myNEO also takes a cautious approach to maximise the true positive fraction via antigen validation.
Example case: mRNA constructs as vaccination tool
Over the past decade, major technological innovations and research investments have enabled mRNA to become a promising therapeutic tool for vaccine development, both to in-vivo deliver neoantigens into the patient (Conry, 1995), and to electroporate neoantigens ex-vivo in the patient’s DCs (Melief, 2015). The rapid, flexible and well-documented mRNA production process has been responsible for its emergence as a message-bearer for delivering the neoantigens (Tavernier, 2011).
"myNEO researches enhancement of stability, production efficiency, and immunogenic properties of the mRNA construct encoding for the selected epitopes, via bioinformatic prediction models evaluating all possible construct design permutations. Variables that are refined to obtain the optimal mRNA properties are either the order wherein the different epitopes are placed within the construct, the linker sequences used to separate these epitopes, or the codons used to code for the amino acid sequence of the epitopes"
Advances have been made towards increasing the stability and translational efficiency of the produced mRNA. The use of optimised UTR’s flanking the open reading frame (ORF), the use of better capping reagents (e.g. CleanCapTM) and a blunt ending long poly-A tail have improved the expression of the proteins of interest.
However, there is still variability and associated uncertainty when designing the region of the mRNA construct containing the selected patient-specific epitopes, with every possible design differing in stability and efficacy. By varying either the order of the epitopes or the linker sequences between them, immunogenic properties of the final construct change (Schubert 2016). Another variable is the degenerate genetic code. mRNAs encoding the same polypeptide via different codon assignments can vary dramatically in the amount of protein translated (Nguyen2004; Angov 2011; Zhao 2017). Furthermore, synonymous codon changes can affect protein conformation and stability, change sites of post-translational modifications, and alter protein function (Hanson 2018; Mauro 2014).
In-vitro assays are unable to capture the properties of all possible construct permutations, as they require costly, and lengthy procedures. As such, myNEO optimises these constructs by manipulating the construct design variability in the multi-epitope coding region, improving upon the efficacy of the final mRNA construct, and by extent thus the vaccine.
Want to learn more about this?
D'Alise AM, Leoni G, Cotugno G, Troise F, Langone F, Fichera I, De Lucia M, Avalle L, Vitale R, Leuzzi A, Bignone V, Di Matteo E, Tucci FG, Poli V, Lahm A, Catanese MT, Folgori A, Colloca S, Nicosia A, Scarselli E. Adenoviral vaccine targeting multiple neoantigens as strategy to eradicate large tumours combined with checkpoint blockade. Nat Commun. 2019 Jun 19;10(1):2688
Tokuyasu TA, Huang JD. Primer on recent developments in cancer immunotherapy, with a focus on neoantigen vaccines. J Cancer Metastasis Treat 2018;4:2
Conry et al. Characterization of a messenger RNA polynucleotide vaccine vector. Cancer Res, 1995 Apr 1;55(7):1397– 1400 Melief et al. Therapeutic cancer vaccines. J Clin Invest, 2015 Sep;125(9):3401-3412
Tavernier et al. mRNA as gene therapeutic: how to control protein expression. J Control Release, 2011 Mar;150(3):238247 Schubert et al. Designing string-of-beads vaccines with optimal spacers. Genome Med, 2016 Jan;8(9)
Nguyen et al. Codon optimization of the HIV-1 vpu and vif genes stabilizes their mRNA and allows for highly efficient Rev-independent expression. Virology, 2004 Feb;319(2):163-175
Angov et al. Codon usage: Nature’s roadmap to expression and folding of proteins. Biotechnol J, 2011 Jun;6(6):650– 659
Zhao et al. Codon usage regulates protein structure and function by affecting translation elongation speed in Drosophila cells. Nucleic Acids, 2017 Jun;45(14):8484–8492
Hanson & Coller. Codon optimality, bias and usage in translation and mRNA decay. Nat Rev Molec Cell Biol, 2017 Oct;19,20-30
Mauro & Chappell. A critical analysis of codon optimization in human therapeutics. Trends in Molecular Medicine, 2014 Nov;20:604–613