The development of therapeutic cancer vaccines to immunize against tumor antigens constitutes a promising modality. Mutation associated antigens are considered major targets given their specificity to tumor cells. These mutations are specific to the patients and require a tailor-made vaccine targeting mutations identified in each tumor. Many mutations are identified in the tumoral genome in most patients, but only a small fraction (around 1%) is suitable as vaccine target. Herein, we report data documenting the prediction performance of the algorithm used for the design of TG4050, a clinical stage patient specific viral-based neoantigen vaccine.
We have trained a set of independent machine learning algorithms to score each candidate neoantigen for several steps of the MHC antigen presentation pathway, including MHC binding, intracellular processing, similarity to self, and likelihood to elicit a T-cell response in peptide stimulated ELISPOT. Further, we have developed a novel graph neural network to combine all these scores to predict the likelihood that a neoantigen will elicit a T-cell response while also incorporating patient-specific factors, such as expression level and conservation of the mutation across different clones. To validate the system, we collected samples from 6 patients diagnosed with NSCLC, sequenced healthy and tumor tissue, identified mutations and ranked them using our algorithm; then, to evaluate immunogenicity, we focused our analysis on CD8+ T cell and measured the frequency of IFN γ+ cells against predicted peptides in autologous PBMC. Immunogenicity of peptides was assayed in 5 pools then deconvoluted against individual peptides.
From 3339 to 4782 somatic variants were detected in tumor tissue samples. After applying technical filtering, removing synonymous mutations, and filtering on transcript expression we detected a median of 281 (192-471) expressed tumor mutations resulting in a median of 2767 candidate class I epitopes (1769 - 4573). The model resulted in high accuracy allowing us to identify peptides with pre-existing ex vivo immunogenic responses in 5 out of 6 patients. Immunogenicity of peptide pools was correlated with ranking by the algorithm. Immunogenicity of the 6 top ranking individual epitopes in each patient showed a median of 5 (2-6) immunogenic peptides resulting in a 77% of true positive rate (TP). It should be noted that when no response was detected, it cannot be excluded that a response could be primed by a vaccine. In a similar setting, the netMHC 4.0 algorithm yielded a TP of 30% and only identified 39% of positive calls of our algorithm.
We demonstrate that the prediction algorithm is accurate in identifying immunogenic cancer mutations even among a large set of candidates. Ongoing TG4050 clinical studies (NCT03839524 and NCT04183166) will allow further validation of the antitumor activity of the elicited immune response.
Citation Format: Brandon Malone, Caroline Tosch, Benoit Grellier, Kousuke Onoue, Timo Sztyler, Karola Rittner, Yoshiko Yamashita, Eric Quemeneur, Kaidre Bendjama. Performance of neoantigen prediction for the design of TG4050, a patient specific neoantigen cancer vaccine [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4566.