Evidence of Targetable Immune Dysfunction in the Bone Marrow of Patients with Intermediate/High-Risk Myelodysplastic Syndrome Refractory to Hypomethylating Agents
Notice bibliographique
Résumé
Abstract Background Growing evidence indicates that a dysfunctional adaptive immune response contributes to the pathogenesis of myelodysplastic syndrome (MDS). Intermediate/high (I/H)-risk MDS is associated with immunosuppression, and median overall survival in hypomethylating agent (HMA)-refractory patients (pts) remains poor at Methods Bone marrow aspirates from HMA-refractory, I/H-risk pts (n = 18) and normal donors (NDs; n = 20) were assessed by flow cytometry (FC), immunohistochemistry and RNA sequencing (RNAseq). An independent cohort of treatment-naive, I/H-risk pts (n = 9) was assessed for regulatory T-cell (Treg) counts by FC. T-cell subsets (Treg, T-naive, T-effector memory, T-central memory, T-late effector memory) were identified by FC based on established markers (CD3, CD4, CD8, CD197, CD45RO, CD127, CD25, FOXP3). Activation/proliferation status of T-cell subsets was measured by HLA-DR and Ki-67. MDS blasts were identified by CD45, CD34 and CD117. Immune checkpoint markers programmed death-1 (PD-1) and programmed death-ligands 1 and 2 (PD-L1/2) were measured on T cells and CD34+ blasts. T-cell immunoglobulin and ITIM domain (TIGIT) was also measured on CD4 and CD8 T cells. T cells (CD4, CD8) and PD-L1 were identified in decalcified bone marrow biopsies by immunohistochemistry. Whole transcriptome gene expression analysis was performed using the Illumina TruSeq RNA Access Library Prep Kit on bulk bone marrow aspirates. Results T cells were detected in bone marrow aspirates from HMA-refractory, I/H-risk pts at levels comparable to those in NDs. Treg counts (cells/µL) were increased in HMA-refractory vs treatment-naive pts (P= 0.017), with the highest Treg count associated with complex cytogenetic karyotypes (≥ 3 abnormalities). We observed increased T-cell activation and proliferation in these pts compared with NDs (MDS vs ND medians [% positive cells]; HLA-DR−/Ki-67+: 1.23 vs 0.75; HLA-DR+/Ki-67+: 1.71 vs 0.33; HLA-DR+/Ki-67−: 14.14 vs 4.80). No trends were seen in CD4-naive and memory subsets, whereas CD8-naive cells were lower and late effector memory cells were higher than in NDs (P= 0.035 each). We observed higher expression of immune checkpoint and regulatory molecules (PD-L1, PD-1, TIGIT) in MDS except for PD-L2, which was minimally detected ( Conclusions Bone marrow in pts with I/H-risk MDS demonstrate evidence of prolonged T-cell activation, an increase in suppressive immune cell populations (i.e., Tregs) and expression of immune checkpoint molecules (PD-L1, TIGIT) compared with normal bone marrow. Patients with complex karyotype had subtle differences, potentially relevant for identifying subsets responsive to checkpoint inhibitors. These data provide new insight into the immune landscape of HMA-refractory I/H-risk MDS. Disclosures Green: Genentech, Inc.: Employment. Yan: F. Hoffmann-La Roche Ltd: Employment. Nalle: Genentech, Inc.: Employment; Roche: Equity Ownership. Ma: Genentech, Inc.: Employment. Robert: Genentech, Inc.: Employment. Zhong: F. Hoffmann-La Roche Ltd.: Employment. Krishnan: Genentech, Inc.: Employment. Phuong: Genentech, Inc.: Employment. Byon: Genentech, Inc.: Employment. Woodard: Genentech, Inc.: Employment, Equity Ownership. Adamkewicz: Genentech Inc.: Employment. Venstrom: Genentech, Inc.: Employment. Dail: Genentech, Inc.: Employment.
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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 | 0,001 |
| 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 ».