Abstract A13: Immunogenomic approaches to optimize immunotherapeutic targeting of neuroblastoma
Notice bibliographique
Résumé
Abstract Neuroblastoma (NB) is the most common extracranial solid cancer in children. Although multimodal therapies with differentiating agents and immunotherapy with anti-GD2 antibody and GM-CSF have shown promising results, it remains deadly in approximately 50% of patients with high-risk disease. Chimeric antigen receptor T-cell therapies (CAR-T) have been found to be effective in treating refractory and relapsed leukemia and lymphoma, and two of them have been recently approved by the FDA. However, current CARs frequently lose efficacy due to T-cell exhaustion, and CARs against solid tumor antigens often lack enough specificity due to a low incidence of somatic mutations resulting in a paucity of tumor neoantigens. There have not been effective CAR-T therapies against other solid cancers to date, although many clinical trials are under way. Therefore, we attempted to develop a high-throughput way of identifying optimal CART cell binders that show activation and expansion in the presence of targets but lack of exhaustion. We previously identified two cell surface cancer-associated antigens, GPC2 (Glypican-2) and CD276 (B7-H3), both highly expressed in NB tumor cells but expressed at low or undetectable levels in normal organs. 14 established binders as well as novel binders targeting these two antigens were cloned into CAR lentiviral constructs and then were separately transduced into T cells to develop 14 CAR-T cells using a 2nd-generation design. All 14 CAR-T cells were pooled and cocultured with CD276/GPC2-expressing NB cancer cells (target cells) for 24 hr. To identify the effective GPC2 or CD276-specific targeting CAR-T cells, we utilized a combined proteomics and transcriptomics method for every single CAR-T cell to quantify RNA and protein at the same. Cocultured CAR-T cells were examined for their activation, exhaustion, cytotoxicity state and distinguished different cell types by staining with CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) antibodies, and then molecularly barcoded using 10X Genomics platform for single-cell RNA-sequencing (scRNA-seq). The data are currently being analyzed and will be presented. Using this method, we will be able to identify which of the CARs are enriched and have an activated T-cell signature, and lack exhaustion marks as determined by the CITE-seq and RNAseq analyses. Finally, top candidate binders for each antigen will be developed into “AND” or “OR” CARs and will be tested in in vitro and in vivo models of NB. Thus, we will develop a high-throughput way to identify high-affinity functional binders against tumor cell surface antigens. This study also will provide novel immunogenomics methods of CARs optimization for development of highly effective immunotherapies against NB and other cancers. Citation Format: Meijie Tian, Adam Tai-Chi Cheuk, Jeetendra Kumar, Young K. Song, Sivasish Sindiri, Nan Li, Christopher M. Dower, Mitchell Ho, Brad St. Croix, Javed Khan. Immunogenomic approaches to optimize immunotherapeutic targeting of neuroblastoma [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A13.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».