Abstract 568: Decoding shared antigenic epitopes and their cognate TCR genes in melanoma TILs using a library of paired human cell-based pHLA multimers and artificial APCs
Notice bibliographique
Résumé
HLA-restricted T cell responses toward immunogenic peptides, whether mutated or non-mutated, can induce antitumor responses in patients with advanced cancer. The fact that potential non-mutated antigens are greater in number than mutated antigens by multiple orders of magnitude and the high polymorphism of HLA genes may have hampered comprehensive analyses of the specificity of antitumor T cell responses toward non-mutated antigens than mutated antigens. Unlike shared antigens, the vast majority of neoantigens are not shared and are unique to each patient. The elucidation of T cell epitopes derived from shared antigens may facilitate the robust development of an efficacious and safe adoptive T cell therapy that is readily available to a larger cohort of cancer patients. It is well established that melanoma tumor-infiltrating T lymphocytes (TILs) contain antitumor T cells that are specific for both non-mutated and mutated antigens. The adoptive transfer of TILs can induce sustained clinical responses in some patients with advanced melanoma. However, a precise and extensive understanding of the shared antigen targets of TILs has been lacking. In this study, TILs were isolated from 8 metastatic melanoma patients, polyclonally expanded in vitro, and their shared antigen specificities for all 45 (25 different) class I alleles were examined. The combination of structure-based analysis using human cell-based peptide/HLA (pHLA) multimers and functional analysis using artificial antigen-presenting cells (APCs) were used to determine antigen-specific T cell responses. We were able to determine the specificity of 12.2 ± 7.2% (mean ± SD, max 25.8%, min 4.6%) of CD8+ T cells in an expanded TIL culture toward 3.0 ± 1.8 (mean ± SD, max 6, min 1) previously known or novel peptides derived from shared antigens. Furthermore, we isolated a number of cognate TCR genes with potent tumor reactivity from the CD8+ T cells. The strategy employed in this study using a library of paired human cell-based pHLA multimers and artificial APCs has enabled us to decipher the antigen specificity of tumor-specific T cells for any given HLA class I allele, regardless of allele frequency and for an infinite number of peptides. It has also allowed us to build a large database of class I-restricted peptides and cognate tumor-reactive TCR genes at an unprecedented scale. This database will be a valuable tool in defining the immune response at the level of each individual cancer patient with precision as well as the identification and validation of biomarkers to aid in patient-based selection of a cancer immunotherapy regimen. Furthermore, this database will help the robust development of novel cancer vaccines and TCR gene therapies for patients with a low mutation burden.Citation Format: Kenji Murata, Kayoko Saso, Linh T. Nguyen, Douglas Millar, Marcus O. Butler, Pamela S. Ohashi, Naoto Hirano. Decoding shared antigenic epitopes and their cognate TCR genes in melanoma TILs using a library of paired human cell-based pHLA multimers and artificial APCs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 568.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».