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Comparison of personal and shared frameshift neoantigen vaccines in a mouse mammary cancer model

BACKGROUND: It is widely hoped that personal cancer vaccines will extend the number of patients benefiting from checkpoint and other immunotherapies. However, it is clear creating such vaccines will be challenging. It requires obtaining and sequencing tumor DNA/RNA, predicting potentially immunogeni...

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Autores principales: Peterson, Milene, Murphy, Sierra Nicole, Lainson, John, Zhang, Jian, Shen, Luhui, Diehnelt, Chris W., Johnston, Stephen Albert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7201681/
https://www.ncbi.nlm.nih.gov/pubmed/32370785
http://dx.doi.org/10.1186/s12865-020-00350-3
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author Peterson, Milene
Murphy, Sierra Nicole
Lainson, John
Zhang, Jian
Shen, Luhui
Diehnelt, Chris W.
Johnston, Stephen Albert
author_facet Peterson, Milene
Murphy, Sierra Nicole
Lainson, John
Zhang, Jian
Shen, Luhui
Diehnelt, Chris W.
Johnston, Stephen Albert
author_sort Peterson, Milene
collection PubMed
description BACKGROUND: It is widely hoped that personal cancer vaccines will extend the number of patients benefiting from checkpoint and other immunotherapies. However, it is clear creating such vaccines will be challenging. It requires obtaining and sequencing tumor DNA/RNA, predicting potentially immunogenic neoepitopes and manufacturing a one-use vaccine. This process takes time and considerable cost. Importantly, most mutations will not produce an immunogenic peptide and many patient’s tumors do not contain enough DNA mutations to make a vaccine. We have discovered that frameshift peptides (FSP) created from errors in the production of RNA rather than from DNA mutations are potentially a rich source of neoantigens for cancer vaccines. These errors are predictable, enabling the production of a FSP microarray. Previously we found that these microarrays can identify both personal and shared neoantigens. Here, we compared the performance of personal cancer vaccines (PCVs) with that of a shared antigen vaccine, termed Frameshift Antigen Shared Therapeutic (FAST) vaccine, using the 4 T1 breast cancer model. Sera from 4 T1-tumor bearing mice were assayed on the peptide microarray containing 200 Fs neoantigens, for the PCV, the top 10 candidates were select and personal vaccines constructed and administrated to the respective mice. For the FAST, we selected the top 10 candidates with higher prevalence among all the mice challenged. Seven to 12 days challenged mice were immunized, combined or not with immune checkpoint inhibitor (ICI) (αPD-L1 and αCTLA-4). Primary and secondary tumor clearance and growth were evaluated as well as cellular and humoral immune response against the vaccine targets by IFN-γ ELISPOT and ELISA. Lastly, we analyzed the immune response of the FAST-vaccinated mice by flow cytometry in comparison to the control group. RESULTS: We found that PCVs and FAST vaccines both reduced primary tumor incidence and growth as well as lung metastases when delivered as monotherapies or in combination with ICI. Additionally, the FAST vaccine induces a robust and effective T-cell response. CONCLUSIONS: These results suggest that FSPs produced from RNA-based errors are potent neoantigens that could enable production of off-the-shelf shared antigen vaccines for solid tumors with efficacy comparable to that of PCVs.
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spelling pubmed-72016812020-05-08 Comparison of personal and shared frameshift neoantigen vaccines in a mouse mammary cancer model Peterson, Milene Murphy, Sierra Nicole Lainson, John Zhang, Jian Shen, Luhui Diehnelt, Chris W. Johnston, Stephen Albert BMC Immunol Research Article BACKGROUND: It is widely hoped that personal cancer vaccines will extend the number of patients benefiting from checkpoint and other immunotherapies. However, it is clear creating such vaccines will be challenging. It requires obtaining and sequencing tumor DNA/RNA, predicting potentially immunogenic neoepitopes and manufacturing a one-use vaccine. This process takes time and considerable cost. Importantly, most mutations will not produce an immunogenic peptide and many patient’s tumors do not contain enough DNA mutations to make a vaccine. We have discovered that frameshift peptides (FSP) created from errors in the production of RNA rather than from DNA mutations are potentially a rich source of neoantigens for cancer vaccines. These errors are predictable, enabling the production of a FSP microarray. Previously we found that these microarrays can identify both personal and shared neoantigens. Here, we compared the performance of personal cancer vaccines (PCVs) with that of a shared antigen vaccine, termed Frameshift Antigen Shared Therapeutic (FAST) vaccine, using the 4 T1 breast cancer model. Sera from 4 T1-tumor bearing mice were assayed on the peptide microarray containing 200 Fs neoantigens, for the PCV, the top 10 candidates were select and personal vaccines constructed and administrated to the respective mice. For the FAST, we selected the top 10 candidates with higher prevalence among all the mice challenged. Seven to 12 days challenged mice were immunized, combined or not with immune checkpoint inhibitor (ICI) (αPD-L1 and αCTLA-4). Primary and secondary tumor clearance and growth were evaluated as well as cellular and humoral immune response against the vaccine targets by IFN-γ ELISPOT and ELISA. Lastly, we analyzed the immune response of the FAST-vaccinated mice by flow cytometry in comparison to the control group. RESULTS: We found that PCVs and FAST vaccines both reduced primary tumor incidence and growth as well as lung metastases when delivered as monotherapies or in combination with ICI. Additionally, the FAST vaccine induces a robust and effective T-cell response. CONCLUSIONS: These results suggest that FSPs produced from RNA-based errors are potent neoantigens that could enable production of off-the-shelf shared antigen vaccines for solid tumors with efficacy comparable to that of PCVs. BioMed Central 2020-05-05 /pmc/articles/PMC7201681/ /pubmed/32370785 http://dx.doi.org/10.1186/s12865-020-00350-3 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Peterson, Milene
Murphy, Sierra Nicole
Lainson, John
Zhang, Jian
Shen, Luhui
Diehnelt, Chris W.
Johnston, Stephen Albert
Comparison of personal and shared frameshift neoantigen vaccines in a mouse mammary cancer model
title Comparison of personal and shared frameshift neoantigen vaccines in a mouse mammary cancer model
title_full Comparison of personal and shared frameshift neoantigen vaccines in a mouse mammary cancer model
title_fullStr Comparison of personal and shared frameshift neoantigen vaccines in a mouse mammary cancer model
title_full_unstemmed Comparison of personal and shared frameshift neoantigen vaccines in a mouse mammary cancer model
title_short Comparison of personal and shared frameshift neoantigen vaccines in a mouse mammary cancer model
title_sort comparison of personal and shared frameshift neoantigen vaccines in a mouse mammary cancer model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7201681/
https://www.ncbi.nlm.nih.gov/pubmed/32370785
http://dx.doi.org/10.1186/s12865-020-00350-3
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