Gene Expression Profile of Enriched T-Cells Prior to Chimeric Antigen Receptor (CAR) T-Cell Manufacturing Is Predictive of CAR T-Cell Response
Notice bibliographique
Résumé
Introduction Anti-CD19 CAR T-cells have revolutionized treatment for relapsed, aggressive B-cell cancers. Our group has reported outcomes of our second generation, anti-CD19 CAR T-cell produced locally on demand utilizing a novel construct (aCD19/4-1BB/CD3z but utilizing TNFS19 hinge and transmembrane) tested in a phase 1b/2 clinical trial (ACIT001/EXC002). This construct demonstrates strong efficacy and safety akin to standard of care CAR T-cells. 42% of patients have failed to achieve long term remission on trial. CAR T-cells represent an expensive, time consuming and resource heavy therapy. Finding reliable and accurate methods of predicting outcomes to better identify appropriate candidates for CAR T-cells are still lacking. While T-cell phenotype has been helpful, exhausted T-cells is not a guarantee of failure. Here, we report our preliminary results of gene expression profile (GEP) of T-cells pre and post CAR T-cell manufacturing based on response to treatment. Methods In this trial, 30 patients have been accrued to date with 29 dosed. 25 patients have sufficient samples that are evaluable; 21 with non-Hodgkin lymphoma (NHL) and 5 with acute lymphoblastic leukemia (ALL). We performed gene expression profiling using Nanostring nCounter technology and the CAR-T Characterization panel on the sub-cohort of lymphoma patients. Sample included patient derived enriched CD3 T-cells pre-transfection, and the CAR-T cell product used for treatment on trial. Differentially expressed genes (DE) and gene set analysis (GSA) were identified using the Rosalind analysis platform among patients who had progressive disease compared to patients who had complete response. DE genes were selected using a false discovery rate (pAdj) cut of 0.05 and +/-1.5 fold change. A global significance score greater than 1.5 was set as a cut off for GSA. Results We identified DE genes and activated pathways in pre-CAR engineered enriched T-cells from patient who failed to respond (progressive disease, PD) to CAR-T infusion versus those who had a complete response (CR). The most significant genes that were upregulated in PD patients included MYL9, CXCL10, IL6, IFITM3, FCGR3A/B indicated inherent issues with interferon signaling, T-cell exhaustion, apoptosis and toxicity. DE genes and activated pathways were also in identified in transduced CAR-T cells from PD patients compared to CR patients. There were a higher number of differences in GEP following CAR T-cell production including up-regulation in 65 genes vs 30 that were down regulated. Top upregulated genes included LILRA5, CXCL8, CD14, IFITM3; while top down-regulated genes included ACSL5, TIMM17A, LAMP1, and NOTCH1. This suggests that PD patient CAR T-cells have issues with exhaustion, toxicity, and TCR diversity. Conclusion We identified DE genes between patients who had progressive disease or a complete response in both the CAR-T cell product and pre-transfection enriched T-cells in our anti-CD19 CAR-T trial. The results suggest that the state of the T cells prior to transfection play a role in determining the ability of CAR T-cells to generate an effective treatment response. These findings may be used to help identify patients more likely to have production of successful CAR-T cell product in future trials.
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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».