Unraveling the Power of NAP-CNB’s Machine Learning-enhanced Tumor Neoantigen Prediction
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Curated by eLife
eLife assessment
This important study assesses a novel in silico neoantigen prediction algorithm combined with in vivo validation to determine important parameters of neoantigen immunogenicity and tumor control. The strength of evidence is compelling. This study contributes to the field and will aid in the development of improved personalized cancer vaccines.
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Abstract
In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverages RNAseq data to consider the relative expression of neoantigens in tumors. Our experiments show the efficacy of NAP-CNB. Predicted TNAs elicited potent antitumor responses in vivo following classical vaccination protocols. Notably, optimal antitumor activity was observed when targeting the antigen with higher expression in the tumor, which was not the most immunogenic. Additionally, the vaccination combining different neoantigens resulted in vastly improved responses compared to each one individually, showing the worth of multiantigen-based approaches. These findings validate NAP-CNB as an innovative TNA-identification platform and make a substantial contribution to advancing the next generation of personalized immunotherapies
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Author response:
Reply to Reviewer #1 (Public Review):
The post-processing increases number of putative neoantigens. As shown in Author response image 1, this is done through data augmentation or “mutations” of individual amino acids in a sequence by their most similar amino acid in the BLOSUM62 embedding. If most of the mutations result in a positive prediction (which we binarize through a >0.5 score) the sequence changes its prediction.
Author response image 1.
Post-processing pipeline to increase the number of putative neoantigens. Sequences can either be predicted using the forward method, for which a raw score is produced, or it can be introduced to a majority-vote prediction of the ensemble prediction of similar protein sequences.
In this article, we obtain the following candidates after post-processing.
Author response table …
Author response:
Reply to Reviewer #1 (Public Review):
The post-processing increases number of putative neoantigens. As shown in Author response image 1, this is done through data augmentation or “mutations” of individual amino acids in a sequence by their most similar amino acid in the BLOSUM62 embedding. If most of the mutations result in a positive prediction (which we binarize through a >0.5 score) the sequence changes its prediction.
Author response image 1.
Post-processing pipeline to increase the number of putative neoantigens. Sequences can either be predicted using the forward method, for which a raw score is produced, or it can be introduced to a majority-vote prediction of the ensemble prediction of similar protein sequences.
In this article, we obtain the following candidates after post-processing.
Author response table 1.
As mentioned, the prediction column shows a binary label. The full list contained 402 sequences did not include any other sequences that met the majority vote criteria.
As noted by the reviewer, the Table 3 of our original paper includes the scores of the direct prediction, which has four sequences in common with the post-processing criteria (*Pnp, *Adar, *Lrrc28 and *Nr1h2). * indicates the mutated form of the peptide, i.e neoantigen.
We selected the top 4 predicted antigens (present both by direct prediction and after post-processing; (*Pnp, *Adar, *Lrrc28 and *Nr1h2) (Wert-Carvajal et al. 2021), but we encountered difficulty in synthesizing, *Nr1h2 (Mutated Nr1h2), and thus it could not be included in the study.
We also decided to evaluate the immunogenicity of *Wiz, which was identified as a potential TNA only after postprocessing. *Wiz exhibited lower levels of immunogenicity compared to *Pnp, *Adar, and *Lrrc28. However, unlike these, *Wiz is highly expressed in the tumor, and vaccination with *Wiz provided the strongest protection levels. These findings led us to incorporate post-processingg into the NAP-CNB platform.
We chose *Herc6 as a mutated antigen predicted not to be a TNA over other candidates because its expression in the tumor was similar to that of *Wiz.
Depending on the experiment we used 4 or 5 animals per group (this will be clarify in the revised version)
The software used for statistical analysis was GraphPad Prism.
Reply to Reviewer #2 (Public Review):
This is true, binding affinity does not always predict immune responses but in most cases, high affinity peptides are immunogenic. There are of course other parameters that drive the effective priming of tumor-reactive CD8+ T cells through antigen cross-presentation, but the mechanisms of antigen presentation are yet not completely understood. High affinity peptides are desirable as good candidates in neoantigen-based vaccines.
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eLife assessment
This important study assesses a novel in silico neoantigen prediction algorithm combined with in vivo validation to determine important parameters of neoantigen immunogenicity and tumor control. The strength of evidence is compelling. This study contributes to the field and will aid in the development of improved personalized cancer vaccines.
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Reviewer #1 (Public Review):
Summary:
The authors of the study are trying to show that RNAseq can be used for neoantigen prediction and that the machine learning approach to the prediction can reveal very useful information for the selection of neoantigens for personalized antitumor vaccination.
Strengths:
The authors demonstrated that RNA expression of a neoantigen is a very important factor in the selection of peptides for the creation of personalized vaccines. They proved in vivo that in silico-predicted neoantigens can trigger an antitumor response in mice.
Weaknesses:
The selection of the peptides for vaccination is not clear. Some peptides were selected before and some after processing. What processing is also not clear. The authors didn't provide the full list of peptides before and after processing, please add those. And it …
Reviewer #1 (Public Review):
Summary:
The authors of the study are trying to show that RNAseq can be used for neoantigen prediction and that the machine learning approach to the prediction can reveal very useful information for the selection of neoantigens for personalized antitumor vaccination.
Strengths:
The authors demonstrated that RNA expression of a neoantigen is a very important factor in the selection of peptides for the creation of personalized vaccines. They proved in vivo that in silico-predicted neoantigens can trigger an antitumor response in mice.
Weaknesses:
The selection of the peptides for vaccination is not clear. Some peptides were selected before and some after processing. What processing is also not clear. The authors didn't provide the full list of peptides before and after processing, please add those. And it wasn't clear that these peptides were previously published. Looking at the previously published table with peptide from B16 F10 (https://www.nature.com/articles/s41598-021-89927-5/tables/3), there are other genes with high expression, e.g. Tab2, Tm9sf3 that have higher expression than Herc6, please clarify the choice.
It's not clear how many mice were used for each group in each experiment, please add this information to the text and figures. It would be good to add this, to aid the understanding of a broader audience.
Please provide information about what software was used for statistical analysis.
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Reviewer #2 (Public Review):
Summary:
The authors develop a new neoantigen prediction tool (NAP-CNB) which primarily predicts neoantigens based on expression (RNAseq) and ranks mutations using binding affinity. The validated predicted neoantigens in mice demonstrate that neoantigens with higher expression (but not necessarily the highest immunogenicity) lead to the greatest tumor control.
Strengths:
There is in vivo validation of the neoantigens.
Demonstrates comparability to other prediction algorithms that are commonly used.
Demonstrates that expression holds a higher value than T-cell responses in actual tumor control.Weaknesses:
Binding affinity does not always predict immune responses or tumor control in vivo which is used as part of the selection criteria.
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