Table 2.

Advantages and drawbacks of the available techniques to identify immunogenic mutations/neoantigens

Technique or softwarePlatformStrengthsWeaknessesRelevance for antigenome predictionReferences
WGSMutational profilingBoth coding and noncoding DNA sequences are analysed.Sequencing depth is usually low, which prevents detection of some subclonal mutations.++(97)
WESMutational profilingProvides high sequence coverage across exome, increasing reliability and ability to detect subclonal mutations.
  • (i) Only covers the ≈1% coding regions of the genome.

  • (ii) Some mutations may be missed due to uneven capture efficiency across exons.

+++(97)
MSI profilingMicrosatellite instabilitySeveral methods all well-validated.Only provides information on the microsatellite instability.+(9)
CGHGenomic instabilityGlobal picture of the overall genomic instability.
  • (i) Only provides copy-number variations and translocations of large portions of the genome.

  • (ii) No access to the DNA sequence.

+
RNA-seqExpression profiling and coding mutation analysis
  • (i) Focuses on translated mutations only, that are the most likely to have functional consequences.

  • (ii) Analysis not restricted to known genes: potential for discovering novel transcripts, splice variants or fusions.

  • (iii) Possibility to correlate mutational data with gene expression.

  • (i) Access to matched normal is key but cannot be achieved in many cases: hard to distinguish tumor-specific mutations from polymorphisms.

  • (ii) Limited calling of mutations within RNA species due to their low levels, either because of low level gene expression or because of mRNA stability.

+++(97)
NetCHOP 20SPCM (WAPP package)FragPredict (MAPPP package)Proteasomal processing prediction trained on in vitro dataN.R.Predictions from in vitro data do not capture the full complexity of proteasomal processing.+(98)
PCleavage
NetCHOPCtermProteasomal processing prediction trained on in vivo data
  • (i) In vivo data provide accurate prediction as predictions are made on the entire processing machinery (action of several proteasomes, cytosolic proteases…)

  • (ii) May also capture transport efficiency.

N.R.+++(98, 99)
PredTAPTAP transport predictionNo comparative study available.No comparative study available.N.R.(98, 100)
SVMTAP (WAPP package)
SMM (stabilized matrix method)Allele-specific HLA binding affinity predictionN.R.(ii) Does not account for non-linearities and interdependencies between amino acids.+(101–103)
ARB average relative binding (matrix-based methods)
NetMHC [artificial neural networks (ANN)-based method]Allele-specific HLA binding affinity predictionNonlinear model.Does not allow prediction for all known HLA alleles.++(103, 104)
NetMHCpan [Pan-specific artificial neural networks (ANN)-based method]Pan-specific HLA binding affinity prediction
  • (i) Allows predictions to be made for all known HLA Class I alleles, including alleles for which no prediction is available with NetMHC.

  • (ii) NetMHCpan is the best-performing method for allele-specific HLA binding affinity prediction.

N.R.+++(105)
AthlatesHLA typingN.R.
  • (i) Early tool with lower accuracy than that of up-to-date tools.Restricted to the use of WES data

+(98)
PolysolverHLA typing
  • (i) Provides improved retrieval and alignment of HLA reads.Polysolver infers HLA-type information with 97% sensitivity and 98% precision from exome-capture sequencing data.

  • (ii) Allows identification of patient-specific mutations in HLA alleles.

(ii)Restricted to the use of WES data+++(106)
OptiTypeHLA typing
  • (i) Performs fully automated HLA typing with four-digit resolution on NGS data from RNA-Seq, WES and WGS technologies.

  • (ii) OptiType showed an accuracy of 99.3% on two-digit-level and of 97.1% on four-digit-level typing using datasets of RNA-Seq, WES and WGS technologies.

  • (i) Zygosity detection occasionally fails in cases where alleles with high sequence similarity constitute a heterozygous locus.

  • (ii) Not able to resolve all ambiguities for every genotype.

+++(107)
MHC multimer technologyT-cell reactivity analysis
  • (i) Gold-standard assay to identify immunogenic peptides. Can be used to detect even low frequencies of antigen-specific T cells on small amounts of clinical material.

  • (ii) “Peptide exchange technology” allows the production of large collections containing a lot of different peptide–MHC complexes for T-cell staining.

N.R.+++(44, 97)
  • NOTE: Multiple NGS technologies, bioinformatics tools, and pipelines are available to analyze tumor samples and predict immunogenic mutations/potential neoantigens in patients (see corresponding steps in Fig. 3). Primarily, genomic data are generated using various NGS technologies, most frequently including WES and RNA-seq to integrate both nsSNVs and expressed nsSNVs. These data are then analyzed using dedicated prediction algorithms corresponding to each step of the neoantigen generation biological process. These filtering tools guide the selection of immunogenic neoantigens among the bulk of candidate neoantigens. Although official guidelines are currently lacking on which tool should preferably be used, most often used algorithms include NetCHOPCterm for proteasomal processing prediction and NetMHC/NetMHCpan for HLA binding prediction. Eventually, a functional validation may be performed using an in vitro T-cell reactivity assay to validate the immunogenicity of the predicted neoantigens.

  • Abbreviations: CGH, comparative genomic hybridization; MSI, microsatellite instability; N.R., not reported; WGS, whole-genome sequencing.