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Record W4417011284 · doi:10.1182/blood-2025-626

Integrating thymic expression patterns into tumor antigen selection enhances immunotherapy design and stratifies clinical responses to immunotherapies

2025· article· en· W4417011284 on OpenAlexaff
Adrienne H. Long, Kaitlyn Tooker, Nicholas M. Pathoulas, Casey Beppler, Tatjana Bilich, Nathan A. Bracey, Mark M. Davis

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity of CalgaryInstitute of Infection and Immunity
Fundersnot available
KeywordsCD8ImmunotherapyImmunogenicityT cellAntigenImmune systemT-cell receptorCancer immunotherapyCytotoxic T cell

Abstract

fetched live from OpenAlex

Abstract Introduction: Tumor associated antigens (TAAs; non-mutated proteins overexpressed in cancer relative to healthy tissues) are promising immunotherapeutic targets, particularly for those cancers with low mutational burdens such as hematologic malignancies and pediatric tumors. Historically, TAA selection has focused on differential expression between tumor and healthy tissue, with little attention paid to how immunological tolerance may differentially shape T cell responses to various TAAs. As non-mutated self-antigens, TAAs may be subject to immune tolerance, though the extent to which this is true remains poorly understood. Here, we report considerable variability in the immunogenicity of different TAAs and identify thymic central tolerance that is established early in life as a major factor contributing to the efficacy of immunotherapeutics based on these antigens. Results: We first evaluated CD8 T cell reactivity to a panel of commonly targeted HLA-A*02-restricted TAAs. Healthy donor PBMCs were peptide stimulated and CD8 T cell responses measured using peptide-MHC tetramers. We found that a subset of TAAs consistently induced potent CD8 T cell responses, while other TAAs induced only modest responses. There was no difference in the TCR affinities within CD8 T cells targeting TAAs eliciting strong vs modest responses, suggesting instead a fundamental difference in CD8 T cell state. Using scRNAseq, we found cell-intrinsic differences in interferon response pathways between CD8 T cells targeting different TAAs. Taken together, our findings suggest that despite all TAAs being self-antigens, there exists a striking variability in their immunogenicity. TAAs have limited expression in healthy tissue. Therefore, we hypothesized that thymic central tolerance, versus peripheral tolerance, might be key to regulating these differences in TAA immunogenicity. Central tolerance is mediated through exposure of developing thymocytes to self-antigens presented by thymic epithelial cells (TECs). Thus to test the contribution of central tolerance to TAA immunogenicity, we leveraged a single-cell thymus atlas to develop a TEC expression score that quantifies TAA expression within subsets of TECs. We found that across TAAs, our TEC expression score inversely correlated with immunogenicity (higher TEC expression associated with poor T cell responses, consistent with central tolerance). In particular, TEC expression scores calculated from fetal thymuses accounted for a large portion of the variability in TAA immunogenicity. Thus, our data suggest that central tolerance, especially that induced early in life, plays a major role in shaping immune responses to TAAs. To validate the critical role of central tolerance in regulating CD8 T cell responses to TAAs, we analyzed RNAseq data from melanoma patients, performed on tumor biopsies before and during treatment with checkpoint inhibitors. At both timepoints for each patient, we generated a set of TAA epitopes predicted to bind to class I HLA molecules. We found that patients with objective clinical responses to checkpoint inhibitors had higher rates of TAA “drop-out” compared to non-responders. Importantly, those TAAs that dropped-out with therapy had lower TEC expression scores compared to TAAs that were retained, reflecting the role of central tolerance in regulating the immune pressures unleashed by checkpoint inhibitors. Furthermore, combining the quantity of TAA epitopes with their TEC expression scores allowed for the stratification of responders versus non-responders from pre-treatment samples. Lastly, to leverage our findings toward improving therapeutic development, we used our TEC expression score to re-prioritize candidate target antigens from cancer immunopeptidomics data. With antigens identified using this method, we find an enhanced ability to isolate potent TCRs capable of eliciting strong T cell responses against antigen-positive tumors. Conclusions: We report that baseline immunogenicity across different TAAs is highly variable, which has significant implications in target selection when developing novel immunotherapeutics. This variability in immunogenicity is largely shaped by thymic central tolerance that appears to be established early in life. Importantly, the contribution of central tolerance to TAA immunogenicity can be quantitatively modeled, which can be used to enhance immunotherapeutic antigen selection and to stratify patient responses to immunotherapies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.308
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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