Abstract B63: Immune gene expression profiling of acute myeloid leukemia identifies predictors of survival and actionable targets for treatment
Notice bibliographique
Résumé
Abstract Background: Acute myeloid leukemia (AML) is characterized by clonal expansion of poorly differentiated myeloid precursors, resulting in impaired hematopoiesis and often bone marrow (BM) failure. The general therapeutic approach in patients with AML has not changed substantially in more than 30 years. Investigation of new strategies, including immunotherapy, remains a priority. Tumor phenotypes are dictated not only by major oncogene drivers, but also by the tumor immunologic microenvironment (TIME) which is inherently immunosuppressive, is equipped to hamper effector T-cell function and includes immune and inflammatory cells, soluble mediators such as interferon (IFN)-gamma and extracellular matrix components. Herein, we profiled the TIME of AML to identify gene signatures that are reflective of general immune status and predictive of antileukemia immune potential. Methods: We used the hybridization-based nCounter® system (NanoString Technologies®, Seattle, WA) and the RNA Pan-Cancer Immune Profiling PanelTM to analyze BM aspirates from 290 adults and 40 children with newly diagnosed, nonpromyelocytic AML. Data were normalized to a set of reference genes and log2-transformed for bioinformatics analysis. The clinical relevance of the association between immune gene signatures and patient outcome was further validated in silico using publicly available cancer transcriptomic datasets (The Cancer Genome Atlas; TCGA). Results: We identified distinct sets of co-expressed genes corresponding to innate immunity, adaptive immunity and IFN-gamma signaling. BM samples with IFN-gamma-dominant gene expression profiles showed up-regulation of actionable immune checkpoints, such as B7-H3, PD-L1 and CTLA-4. A set of 4 differentially expressed immune genes between children and adults with AML (FDR<0.005) was associated with adverse cytogenetic features, leukemia relapse and shorter patient survival, and also stratified survival in the TCGA cohort of adult patients with AML. Finally, all four genes were amplified, deleted or mutated in 7% of 51,175 TCGA solid tumors, with the highest frequency of mutations being detected in primary CNS lymphoma (40%) and diffuse large B-cell lymphoma (25%). Conclusions: Our study identified distinct immune gene programs in the TIME of patients with newly diagnosed AML. From a translational standpoint, immune-enriched and IFN-gamma-dominant AMLs may benefit from immune checkpoint blockade and from new immunotherapy approaches, including dual-affinity T-cell redirecting antibodies targeting CD123. Grant support: John and Lucille van Geest Foundation, UK; Roger Counter Foundation, UK; Qatar National Research Fund (#NPRP8-2297-3-494). Citation Format: Sergio Rutella, Jayakumar Vadakekolathu, Tressa Hood, Stephen Reeder, Sarah E. Warren, Patrick Danaher, Jan Davidson-Moncada, Alessandra Cesano, Joseph M. Beechem, Sarah K. Tasian, Mark D. Minden. Immune gene expression profiling of acute myeloid leukemia identifies predictors of survival and actionable targets for treatment [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr B63.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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) | 0,000 | 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 ».