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Enregistrement 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 sur OpenAlexaff
Adrienne H. Long, Kaitlyn Tooker, Nicholas M. Pathoulas, Casey Beppler, Tatjana Bilich, Nathan A. Bracey, Mark M. Davis

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineImmunology and Microbiology
ThématiqueImmunotherapy and Immune Responses
Établissements canadiensUniversity of CalgaryInstitute of Infection and Immunity
Organismes subventionnairesnon disponible
Mots-clésCD8ImmunotherapyImmunogenicityT cellAntigenImmune systemT-cell receptorCancer immunotherapyCytotoxic T cell

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,016
Tête enseignante GPT0,308
Écart entre enseignants0,292 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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