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An unbiased approach to defining bona fide cancer neoepitopes that elicit immune-mediated cancer rejection
Cory A. Brennick, Mariam M. George, Marmar M. Moussa, Adam T. Hagymasi, Sahar Al Seesi, Tatiana V. Shcheglova, Ryan P. Englander, Grant L.J. Keller, Jeremy L. Balsbaugh, Brian M. Baker, Andrea Schietinger, Ion I. Mandoiu, Pramod K. Srivastava
Cory A. Brennick, Mariam M. George, Marmar M. Moussa, Adam T. Hagymasi, Sahar Al Seesi, Tatiana V. Shcheglova, Ryan P. Englander, Grant L.J. Keller, Jeremy L. Balsbaugh, Brian M. Baker, Andrea Schietinger, Ion I. Mandoiu, Pramod K. Srivastava
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Research Article Immunology Oncology

An unbiased approach to defining bona fide cancer neoepitopes that elicit immune-mediated cancer rejection

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Abstract

Identification of neoepitopes that are effective in cancer therapy is a major challenge in creating cancer vaccines. Here, using an entirely unbiased approach, we queried all possible neoepitopes in a mouse cancer model and asked which of those are effective in mediating tumor rejection and, independently, in eliciting a measurable CD8 response. This analysis uncovered a large trove of effective anticancer neoepitopes that have strikingly different properties from conventional epitopes and suggested an algorithm to predict them. It also revealed that our current methods of prediction discard the overwhelming majority of true anticancer neoepitopes. These results from a single mouse model were validated in another antigenically distinct mouse cancer model and are consistent with data reported in human studies. Structural modeling showed how the MHC I–presented neoepitopes had an altered conformation, higher stability, or increased exposure to T cell receptors as compared with the unmutated counterparts. T cells elicited by the active neoepitopes identified here demonstrated a stem-like early dysfunctional phenotype associated with effective responses against viruses and tumors of transgenic mice. These abundant anticancer neoepitopes, which have not been tested in human studies thus far, can be exploited for generation of personalized human cancer vaccines.

Authors

Cory A. Brennick, Mariam M. George, Marmar M. Moussa, Adam T. Hagymasi, Sahar Al Seesi, Tatiana V. Shcheglova, Ryan P. Englander, Grant L.J. Keller, Jeremy L. Balsbaugh, Brian M. Baker, Andrea Schietinger, Ion I. Mandoiu, Pramod K. Srivastava

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Figure 4

Phenotypes of CD8+ TILs from mice immunized with a TRMN and a non-TRMN.

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Phenotypes of CD8+ TILs from mice immunized with a TRMN and a non-TRMN.
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Mice (n = 15 mice per group) were immunized with unpulsed BMDCs (green) or BMDCs pulsed with peptides FAM171bMUT (a TRMN, blue) or Cd9MUT (a non-TRMN, red) and challenged with MC38-FABF. Tumors were harvested on day 25 after tumor challenge and CD8+ TILs isolated. (A) Tumor growth of mice immunized with each group. IC50 values for cognate alleles and IFN-γ ELISpot response of CD8+ T cells from spleens of MC38-FABF–immunized mice are indicated for each peptide (0–50 spots/106 CD8+ cells = ++, >140 spots/106 CD8+ cells = ++++). (B) MFI of PD-1 in CD8+ TILs (left); bar graph representing percentage of PD-1lo and PD-1hi cells (middle; data represented as mean ± SD with individual points); quantification of MFI of PD-1 (right). n = 5 pooled mice per group, 3 technical replicates. (C) Flow cytometry contour plots with indicated markers in CD8+PD-1+ (low and high) TILs (left) with respective stacked bar graphs representing percentage of cells (middle) and quantification of MFI (right). Data represented as mean ± SD; n = 5 pooled mice per group, 3 technical replicates. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by ANOVA with Tukey’s multiple-comparison test (B and C). The data are representative of 3 independent experiments.

Copyright © 2026 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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