Page 27 - Read Online

Page 8 of 24 Tokuyasu et al. J Cancer Metastasis Treat 2018;4:2 I http://dx.doi.org/10.20517/2394-4722.2017.52

Table 1. Strengths and weakness of the neoantigen vaccine approach
Strengths Weaknesses
Precise targeting Need for tumor biopsy (in general)
Mild adverse events Need to overcome tumor defenses
Few constraints on dosage Slow induction of immune response
Better profile than TAA vaccines May not be applicable to tumors with few mutations
No need for T cell extraction and ex vivo growth Unreliable epitope binding prediction algorithms
Many opportunities to optimize/combine formulations Time lag from biopsy to vaccine
Multi-epitope designs can compensate for inaccurate binding predictions, tumor Cost
heterogeneity and evolution
Induction of antigen spreading and immune memory can cope with occult disease

[92]
[93]
Other considerations are choice of carrier, delivery vehicle (including bacteria or viral vectors ),
[91]
and administration route (intravenous, intratumoral, subcutaneous, intra-lymph node, nasal, ingested).
Further afield, cancer vaccine engineering has emerged to offer benefits such as lymph node targeting,
reduced systemic toxicity, elimination of ex vivo expansion requirements, and controlled release of
immunomodulators while ignoring suppressive signals [94-96] .

Neo-epitope binding prediction
Neoantigen vaccines are produced by first inspecting the patient’s tumor for immunogenic peptides,
[97]
specifically epitopes . TCRs recognize linear epitopes, i.e. a continuous fragment of an antigen. Note that
pMHC binding is a necessary but not sufficient condition for immunogenicity.

The neo-epitope selection problem can thus be reduced to finding mutant peptides that bind well to the
patient’s MHC alleles. This is amenable to computational treatment and is one of the most prominent
[98]
applications of machine learning to immunology . The realization that such bioinformatic approaches
can reveal a “gold mine” of targets and that neoantigen vaccines were feasible can be traced back to a 2008
[99]
paper .
A simplified neo-epitope selection pipeline can be described as follows:
• Perform exome sequencing of tumor and normal tissue to identify non-synonymous single nucleotide
variants and generate an initial list of candidate genes;
• Perform RNA-Seq to confirm expression;
• Use informatics tools to predict neoantigen-derived peptides that bind to the patient’s set of HLA alleles;
• Filter candidates based on survival or growth function (“driver genes”);
• Choose the top 10 or 20 epitopes.

Proteasomal cleavage predictions [100] can also be incorporated into the workflow, although the predictive
value is rather low, due to the lack of sufficient training data .
[98]

Numerous excellent reviews of the available tools are available [82,98,101] . The Immune Epitope Database [102]
is probably the most prominent epitope database and analysis resource, freely available on the Web.
TANTIGEN [103] is a database of tumor-tissue derived antigens with experimentally validated HLA binding.
Step-by-step instructions on the use of a prominent suite of tools is available [104] . Mutant Peptide Extractor
and Informer [105] is a web-based tool that attempts to integrate best practices and simplify neo-epitope
analysis and selection for non-bioinformaticians (limited to MHC-I epitopes). ImmunoNodes [106] is a
software framework for building complex immunoinformatics workflows, such as those for neo-epitope
selection.

Amongst other challenges, prediction of MHC-II peptide binding lags behind MHC-I prediction, partly due
to the greater length of loaded peptides that interact in flanking regions with highly polymorphic alleles.