Abstract IA011: Identifying tumor-restricted target antigens for adoptive cellular immunotherapy to treat Ewing Sarcoma using multi-omic discovery platforms
Notice bibliographique
Résumé
Abstract Background: In the last four decades, no significant improvement in survival rates for children with Ewing Sarcoma (EWS) has been made. Less than 30% of patients with metastatic disease are long- term survivors and standard therapy incorporates dose-intensive cytotoxic chemotherapy risking lifelong late effects. Engineered T cells (TCRs/CARs) can mediate impressive clinical anti-tumor activity and would represent a promising treatment option for patients. Development of potent and safe adoptive cellular immunotherapies (ACT) relies on antigents that are absent from any normal, vital tissue in the body to avoid on-target/off-tumor recognition, which conventional methods often fail to identify. Therefore, novel pipelines for rational target antigen discovery are critical for advancing ACT for the treatment of the EWS. Methods: To identify tumor antigens that meet the requirement for ACT targets, we performed differential gene expression analysis of compiled transcriptomes from EWS tumors (n=120) and normal tissues (n=42). Bioinformatic cell-surface annotation identified top differentially expressed targets best suited for CAR T cells (Heitzeneder et. al, JNCI 2019). However, more than 70% of the human proteome represent intracellular molecules, a target-pool accessible to TCRs through presentation of peptides via MHC. To empirically determine the immunopeptidome, we profiled EWS cells by immunoprecipitation with a pan-MHC-I antibody, followed by liquid chromatography and tandem mass spectrometry (LC-MS/MS). To identify peptide-MHC antigens best suited for TCRs, this data was incorporated with the differential tumor-to-normal transcriptome. Results: We identified 584 genes as overexpressed in EWS(LogFC>1, P<.01) compared to normal tissue. A total of 24 genes showed high abundance in EWS (AVElog2[TPM+1]>5) and low normal tissue expression (AVElog2[TPM+1]<2). Of those, 11 were predicted to be cell surface-associated and possess the potential to serve as CAR T-cell targets. This identified pregnancy-associated plasma protein-A (PAPP-A), a placental antigen expressed at the maternal-fetal interface (Heitzeneder et al, JNCI 2019). Immunopeptidome analysis of HLA-A*0201+EW8 cells showed that peptides of ~30% of the top 24 genes were presented by MHC-I. No neoantigenic peptides caused by common mutations in EWS or the EWS-Fli1 breakpoint region were found. Instead, immunopeptidome analysis identified oncofetal and cancer-testis antigens for TCR-based treatment approaches. Conclusions: Truly tumor-specific antigens are rare in pediatric solid tumors, yet, they often manifest stalled fetal developmental programs and continue to express placental, oncofetal and cancer testis antigens. Those represent an attractive pool of target antigens for the development of ACT to treat EWS, which is otherwise characterized by lofe immunogenicity. Comprehensive analysis of the surfaceome and immunopeptidome has proven an efficient method to identify such target antigens and facilitates the preclinical development of CAR-T cell and TCR-based immunotherapies. Citation Format: Sabine Heitzeneder. Identifying tumor-restricted target antigens for adoptive cellular immunotherapy to treat Ewing Sarcoma using multi-omic discovery platforms [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr IA011.
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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».