Computational prediction of neoantigens: do we need more data or new approaches?
Authors: A.C. Eklund, Z. Szallasi
Journal: Annals of Oncology (2018)

The capability of the immune system for self-recognition has recently become the focus of interest for a broadening research community due to the impressive clinical results achieved with immune checkpoint inhibitors and other immunotherapies in the treatment of advanced metastatic carcinoma [1.Hammerbacher J. Snyder A. Informatics for cancer immunotherapy.Ann Oncol. 2017; 28: xii56-xii73Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar]. A significant portion of this clinical efficacy is attributed to the fact that the genomic instability of cancer generates mutation-derived peptides (neopeptides) that are not present in normal cells. A subset of these neopeptides may be neoantigens (also called neoepitopes) which are recognized as alien by cytotoxic T cells. Ideally, a cell producing neoantigens should be eliminated by the immune system. However, tumors are able to inhibit this immune response by activating various checkpoint mechanisms; these can be overcome by the now widely used therapeutic agents of checkpoint inhibitors such as anti PD-L1 or anti-CTLA-4 antibodies. There is increasing evidence that such neoantigen-driven immune responses are responsible for the significant clinical response shown to immune checkpoint inhibitors in at least one specific type of tumors, microsatellite instable cancer [2.Le D.T. Uram J.N. Wang H. et al.PD-1 blockade in tumors with mismatch-repair deficiency.N Engl J Med. 2015; 372: 2509-2520Crossref PubMed Scopus (6169) Google Scholar, 3.Germano G. Lamba S. Rospo G. et al.Inactivation of DNA repair triggers neoantigen generation and impairs tumour growth.Nature. 2017; 552: 116-120Crossref PubMed Scopus (351) Google Scholar]. Cytotoxic T-cell activation by tumor cells is driven by the MHC/peptide complexes specifically binding to alpha-beta T-cell receptors (TCR), a process that is determined by the amino acid sequence of all three key players. This sequence specificity, if it can be harnessed, could move cancer therapy to a truly personalized level, in which the neoantigens that might be present in only a single patient’s tumor would be used to design individualized peptide vaccines [4.Ott P.A. Hu Z. Keskin D.B. et al.An immunogenic personal neoantigen vaccine for patients with melanoma.Nature. 2017; 547: 217-221Crossref PubMed Scopus (1561) Google Scholar, 5.Sahin U. Derhovanessian E. Miller M. et al.Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer.Nature. 2017; 547: 222-226Crossref PubMed Scopus (0) Google Scholar]. Tumors, especially those with high-mutational burden such as MSI tumors or melanoma, may carry hundreds or thousands of neopeptides. In principle, each of these potential neoantigens can be experimentally tested whether its presentation by the MHC molecules of the tumor leads to the activation of cytotoxic T cells. However, despite recent successes with parallelizing this process [6.Bentzen A.K. Marquard A.M. Lyngaa R. et al.Large-scale detection of antigen-specific T cells using peptide-MHC-I multimers labeled with DNA barcodes.Nat Biotechnol. 2016; 34: 1037-1045Crossref PubMed Scopus (181) Google Scholar] it would be desirable to reduce the starting pool of potential neoantigens to be experimentally tested. We and others have produced software to aid in this process [7.Bjerregaard A.-M. Nielsen M. Hadrup S.R. et al.MuPeXI: prediction of neo-epitopes from tumor sequencing data.Cancer Immunol Immunother. 2017; 66: 1123-1130Crossref PubMed Scopus (108) Google Scholar, 8.Hundal J. Carreno B.M. Petti A.A. et al.pVAC-Seq: a genome-guided in silico approach to identifying tumor neoantigens.Genome Med. 2016; 8: 11.Crossref PubMed Scopus (234) Google Scholar], but we recognize that these models and parameters are chosen based mainly on expert knowledge and relatively little empirical data. Thus, the development of optimal methods for identifying and/or prioritizing neopeptides that are most likely to elicit a T-cell response remains an important goal. There is convincing evidence that using machine learning approaches certain aspects determining the immunogenicity of short peptides, such as binding by a given HLA molecule, can be reasonably well predicted based on their amino acid sequences [9.Jurtz V. Paul S. Andreatta M. et al.NetMHCpan-4.0: improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data.JI. 2017; 199: 3360-3368Crossref PubMed Scopus (683) Google Scholar]. This is probably due to the fact that the three-dimensional structure or electron cloud of the short peptide is determined by its molecular structure (albeit these cannot be directly derived from the actual amino acid sequence based on first principles). Other predictions about the function of the immune system, however, will probably resist such automated sequence-based analysis. For example, the actual TCRs present in a patient will determine whether a given HLA/peptide complex will elicit cytotoxic reaction or not. In theory, the complete set of ∼1015 possible TCR sequences can be deduced from genome sequencing. However, most of these either do not get produced or become eliminated, and humans are left with a much smaller set of TCRs (on the order of 108). This reduction probably has a significant stochastic component and is also influenced by several difficult- to-model physiological events such as exposure to viruses. It is hard to envision a simple sequence-based machine learning method that could predict the exact set of TCR sequences present in an individual. Nevertheless, several studies have aimed to understand the factors that determine whether a somatic mutation yields a true neoantigen. One study compared the predicted HLA binding affinity of neoantigens and their unmutated counterparts and found, for the majority of known neoantigens, that the binding affinities were similar [10.Fritsch E.F. Rajasagi M. Ott P.A. et al.HLA-binding properties of tumor neoepitopes in humans.Cancer Immunol Res. 2014; 2: 522-529Crossref PubMed Scopus (139) Google Scholar]. We recently examined a set of published neopeptides for which some elicited a T-cell response and others did not [11.Bjerregaard A.-M. Nielsen M. Jurtz V. et al.An analysis of natural T cell responses to predicted tumor neoepitopes.Front Immunol. 2017; 8: 1566.Crossref PubMed Scopus (58) Google Scholar]. Our results suggested that there are two paths to immunogenicity: in one, the neopeptide and its unmutated counterpart both bind to MHC, in which case dissimilarity between the two is predictive of immunogenicity. This suggests that, due to TCR cross-reactivity, selection against T cells recognizing self-peptides leaves a hole in the T-cell repertoire which prevents recognition of neopeptides that are too similar. In the second path, the MHC binds more strongly to the neopeptide than to the unmutated counterpart, in which case dissimilarity between the two is not predictive. This suggests that, in this case, there is not a hole in the T-cell repertoire, because the unmutated peptide has not been presented by the MHC. In this issue, Kim et al. used machine learning to identify a set of nine features that together can accurately predict neoantigens [12.Kim S. Kim H.S. Kim E. et al.Neopepsee: accurate genome-level prediction of neoantigens by harnessing sequence and amino acid immunogenicity information.Ann Oncol. 2018; 29: 1030-1036Abstract Full Text Full Text PDF PubMed Scopus (86) Google Scholar]. Several of these factors, such as the NetMHC predicted binding affinity [9.Jurtz V. Paul S. Andreatta M. et al.NetMHCpan-4.0: improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data.JI. 2017; 199: 3360-3368Crossref PubMed Scopus (683) Google Scholar], are widely used for such predictions in the field. Other factors touch on dissimilarity measures as described above. The authors found that these combined characteristics add a significant amount of new information beyond binding affinity alone, resulting in improved accuracy. These results seem impressive. However, although this algorithm was evaluated in all publicly available datasets for which tumor RNA-seq data and T-cell reactivity determinations were available, this amounted to only five patients. It will require a much larger test dataset before the accuracy of such predictions can be confidently assessed. Thus, it is likely that neoantigen prediction algorithms are still in their infancy, and the availability of data from a larger number of tumors will accelerate progress in this field. However, it is also worth considering that the availability of an appropriate TCR may be a stochastic process, meaning that it may never be possible to predict neoantigens with perfect accuracy given only information from the tumor. It is intriguing to consider how knowledge of a patient’s TCR repertoire, and methods to associate TCR sequence with specificity [13.Lanzarotti E. Marcatili P. Nielsen M. Identification of the cognate peptide-MHC target of T cell receptors using molecular modeling and force field scoring.Mol Immunol. 2017; 94: 91-97Crossref PubMed Scopus (23) Google Scholar, 14.Shugay M. Bagaev D.V. Zvyagin I.V. et al.VDJdb: a curated database of T-cell receptor sequences with known antigen specificity.Nucleic Acids Res. 2018; 46: D419-D427Crossref PubMed Scopus (204) Google Scholar, 15.Glanville J. Huang H. Nau A. et al.Identifying specificity groups in the T cell receptor repertoire.Nature. 2017; 547: 94-98Crossref PubMed Scopus (475) Google Scholar], might someday circumvent these limitations and bring predictions to a level where experimental validation is unnecessary. Breast Cancer Research Foundation (to ZS) (no grant number apply).