An unbiased approach to defining bona fide cancer neoepitopes that elicit immune-mediated cancer rejection
Cory A. Brennick, … , Ion I. Mandoiu, Pramod K. Srivastava
Cory A. Brennick, … , Ion I. Mandoiu, Pramod K. Srivastava
Published December 15, 2020
Citation Information: J Clin Invest. 2021;131(3):e142823. https://doi.org/10.1172/JCI142823.
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Research Article Immunology Oncology

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

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 1

Unbiased identification of TRMNs.

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Unbiased identification of TRMNs. (A) All experimentally confirmed SNVs ...
(A) All experimentally confirmed SNVs of the MC38-FABF tumor, and screening strategy for tumor rejection. (B) Box-and-whisker plot representing the tumor control index (TCI) scores (9) for 58 of all 279 peptides, represented by numbers on the x axis. The remaining 221 peptides elicited no tumor control and are not shown. The negative control (extreme left) consists of mice immunized with unpulsed BMDCs. Peptides that elicited significant tumor control are marked by asterisks. P and T indicate activity in prophylaxis and therapy. Combination of 9 positive peptides (TRMNs) is on the extreme right. The IC50 values for peptide–MHC I (Kb/Db) were predicted using NetMHC 4.0; the values represent the highest predicted binder for each SNV or an experimentally verified precise neoepitope. Peptides are color coded by IC50 values as indicated in the box. n = 5–15 mice/group, except for the 9 active peptides (TRMNs), for which n = 20–50 mice per peptide. All peptides were tested at least 3 times; the 9 active peptides (TRMNs) were tested between 4 and 8 times each. (C) CD8+ (IFN-γ ELISpot) responses to peptides from B in MC38-FABF–immunized (blue bars) or naive mice (red bars) (n = 4 mice/group). To generate the box-and-whisker plots, data from every single mouse were entered. The box extends from the 25th to 75th percentiles, the middle line represents the median in each group, and the “+” represents the mean. The whiskers extend from the minimum to maximum value. Statistical analysis was conducted for peptides’ response against wells with no target. All peptides were tested at least 2 times. (B and C) Mean ± SD shown. *P < 0.05 by Student’s t test (B) or 2-way ANOVA (C).