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 2

Characterization of the activity of TRMNs.

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Characterization of the activity of TRMNs. (A) Tumor growth curves (top)...
(A) Tumor growth curves (top) and percentage survival (bottom) of mice immunized prophylactically with FAM171bMUT (red) or unpulsed BMDCs (gray). Each line shows tumor volume for 1 mouse. The experiment was done 2 times (n = 10 and n = 5). (B) TCI scores of mice treated with each of the 9 TRMNs on days 0 and 7 after tumor challenge. n = 10 mice/group. The experiment was done twice. (C) Tumor growth curves (top) and percentage survival (bottom) of mice treated on days 10 and 17 after tumor challenge (indicated by arrows) with FAM171bMUT (red) or unpulsed BMDC (gray), n = 10 mice/group. The experiment was done twice. (D) TCI scores of mice immunized with the 9 TRMNs and depleted of CD8+ (purple) or CD4+ cells (orange) or treated with an isotype control antibody (αLTF2) (black). The experiment was done twice. n = 5 mice/group. (E) Mice (n = 15) were immunized with unpulsed or FAM171bMUT-pulsed BMDCs. Five days later, CD8+ cells were isolated from the inguinal and popliteal lymph nodes. Two million CD8+ T cells were adoptively transferred into 9 mice/group. Mice were challenged with MC38-FABF on the right flank and MC38 on the left flank. Tumor growth was monitored. Data represent area under the curve (top) and growth inhibition (bottom) in mice that received T cell transfers from unpulsed BMDC-immunized mice (gray) or FAM171bMUT-immunized mice (red). *P < 0.05; **P < 0.01 by log-rank (Mantel-Cox) test (survival plots in A and C), Student’s t test (B and E), or 2-way ANOVA with Tukey’s multiple-comparison test (D). Box-and-whisker plots were generated as in Figure 1.