Tumor-specific mutations can generate neoantigens that drive CD8 T cell responses against cancer. Next-generation sequencing and computational methods have been successfully applied to identify mutations and predict neoantigens. However, only a small fraction of predicted neoantigens are immunogenic. Currently, predicted peptide binding affinity for MHC-I is often the major criterion for prioritizing neoantigens, although little progress has been made toward understanding the precise functional relationship between affinity and immunogenicity. We therefore systematically assessed the immunogenicity of peptides containing single amino acid mutations in mouse tumor models and divided them into two classes of immunogenic mutations. The first comprises mutations at a nonanchor residue, for which we find that the predicted absolute binding affinity is predictive of immunogenicity. The second involves mutations at an anchor residue; here, predicted relative affinity (compared with the WT counterpart) is a better predictor. Incorporating these features into an immunogenicity model significantly improves neoantigen ranking. Importantly, these properties of neoantigens are also predictive in human datasets, suggesting that they can be used to prioritize neoantigens for individualized neoantigen-specific immunotherapies.
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January 15 2020
Mutation position is an important determinant for predicting cancer neoantigens
Aude-Hélène Capietto
,
Genentech, South San Francisco, CA
Aude-Hélène Capietto: capietta@gene.com
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Suchit Jhunjhunwala
,
Genentech, South San Francisco, CA
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Samuel B. Pollock
,
Samuel B. Pollock
Data curation
Genentech, South San Francisco, CA
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Patrick Lupardus
,
Patrick Lupardus
Methodology
Genentech, South San Francisco, CA
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Jim Wong
,
Jim Wong
Formal analysis
Genentech, South San Francisco, CA
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Lena Hänsch
,
Lena Hänsch
Investigation
Genentech, South San Francisco, CA
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James Cevallos
,
James Cevallos
Data curation
Genentech, South San Francisco, CA
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Yajun Chestnut
,
Yajun Chestnut
Investigation
Genentech, South San Francisco, CA
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Ajay Fernandez
,
Ajay Fernandez
Investigation
Genentech, South San Francisco, CA
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Nicolas Lounsbury
,
Nicolas Lounsbury
Data curation
Genentech, South San Francisco, CA
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Tamaki Nozawa
,
Tamaki Nozawa
Data curation
Genentech, South San Francisco, CA
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Manmeet Singh
,
Manmeet Singh
Data curation
Genentech, South San Francisco, CA
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Zhiyuan Fan
,
Zhiyuan Fan
Investigation
Genentech, South San Francisco, CA
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Cecile C. de la Cruz
,
Cecile C. de la Cruz
Project administration
Genentech, South San Francisco, CA
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Qui T. Phung
,
Qui T. Phung
Data curation
Genentech, South San Francisco, CA
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Lucia Taraborrelli
,
Lucia Taraborrelli
Resources
Genentech, South San Francisco, CA
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Benjamin Haley
,
Benjamin Haley
Methodology
Genentech, South San Francisco, CA
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Jennie R. Lill
,
Jennie R. Lill
Conceptualization
Genentech, South San Francisco, CA
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Ira Mellman
,
Ira Mellman
Conceptualization
Genentech, South San Francisco, CA
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Richard Bourgon
,
Richard Bourgon
Conceptualization
Genentech, South San Francisco, CA
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Lélia Delamarre
Genentech, South San Francisco, CA
Correspondence to Lélia Delamarre: delamarre.lelia@gene.com
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Aude-Hélène Capietto
Conceptualization
Genentech, South San Francisco, CA
Suchit Jhunjhunwala
Conceptualization
Genentech, South San Francisco, CA
Samuel B. Pollock
Data curation
Genentech, South San Francisco, CA
Patrick Lupardus
Methodology
Genentech, South San Francisco, CA
Jim Wong
Formal analysis
Genentech, South San Francisco, CA
Lena Hänsch
Investigation
Genentech, South San Francisco, CA
James Cevallos
Data curation
Genentech, South San Francisco, CA
Yajun Chestnut
Investigation
Genentech, South San Francisco, CA
Ajay Fernandez
Investigation
Genentech, South San Francisco, CA
Nicolas Lounsbury
Data curation
Genentech, South San Francisco, CA
Tamaki Nozawa
Data curation
Genentech, South San Francisco, CA
Manmeet Singh
Data curation
Genentech, South San Francisco, CA
Zhiyuan Fan
Investigation
Genentech, South San Francisco, CA
Cecile C. de la Cruz
Project administration
Genentech, South San Francisco, CA
Qui T. Phung
Data curation
Genentech, South San Francisco, CA
Lucia Taraborrelli
Resources
Genentech, South San Francisco, CA
Benjamin Haley
Methodology
Genentech, South San Francisco, CA
Jennie R. Lill
Conceptualization
Genentech, South San Francisco, CA
Ira Mellman
Conceptualization
Genentech, South San Francisco, CA
Richard Bourgon
Conceptualization
Genentech, South San Francisco, CA
Lélia Delamarre
Conceptualization
Genentech, South San Francisco, CA
Correspondence to Lélia Delamarre: delamarre.lelia@gene.com
Aude-Hélène Capietto: capietta@gene.com
*
A-H. Capietto and S. Jhunjhunwala contributed equally to this paper.
J Exp Med (2020) 217 (4): e20190179.
Article history
Received:
January 28 2019
Revision Received:
September 28 2019
Accepted:
December 02 2019
Citation
Aude-Hélène Capietto, Suchit Jhunjhunwala, Samuel B. Pollock, Patrick Lupardus, Jim Wong, Lena Hänsch, James Cevallos, Yajun Chestnut, Ajay Fernandez, Nicolas Lounsbury, Tamaki Nozawa, Manmeet Singh, Zhiyuan Fan, Cecile C. de la Cruz, Qui T. Phung, Lucia Taraborrelli, Benjamin Haley, Jennie R. Lill, Ira Mellman, Richard Bourgon, Lélia Delamarre; Mutation position is an important determinant for predicting cancer neoantigens. J Exp Med 6 April 2020; 217 (4): e20190179. doi: https://doi.org/10.1084/jem.20190179
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