<i>IL1R1</i> Expression Predicts the Benefit from Allogeneic Hematopoietic Stem Cell Transplantation in Patients with Acute Myeloid Leukemia and Intermediate-Risk Cytogenetics
Notice bibliographique
Résumé
Introduction: Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy with variable clinical outcomes depending on baseline patient and disease characteristics. After achieving complete remission (CR) with induction chemotherapy, eligible patients are treated with consolidation chemotherapy or allogeneic hematopoietic stem cell transplantation (HSCT) to prevent relapse. In patients with intermediate-risk cytogenetics (IRC) without adverse-risk mutations, the decision for post-remission therapy is challenging because of the significant risks of morbidity and mortality associated with HSCT which can offset its benefit in preventing relapse. Therefore, novel biomarkers are needed to support the decision to recommend HSCT in first CR (CR1) in patients with IRC AML. Methods: We used the Leucegene AML prognostic cohort composed of patients diagnosed with de novo IRC AML and treated with intensive induction chemotherapy (N = 316) to identify biomarkers that are predictive for the benefit of HSCT in CR1. Whole-transcriptome sequencing was performed on diagnostic patients' samples. Gene expression data was normalized in transcripts per million (TPM) then log transformed and standardized into Z-scores. We used univariable and multivariable Cox proportional hazards (CPH) regression models for overall survival (OS) and relapse-free survival (RFS) to evaluate the association between gene expression and clinical outcomes. For multivariable analyses (MVA), we included age and white blood cell (WBC) count at diagnosis and NPM1, FLT3-ITD, DNMT3A, biallelic CEBPA, ASXL1 and RUNX1 mutations as covariables. To identify genes predictive of the benefit of HSCT in CR1, we evaluated interaction terms between gene expression and HSCT in CR1 as a time-dependent variable (HSCT-TD) in extended CPH regression models for OS and RFS. We dichotomized the expression of genes based on optimal cutoffs for p value and hazard ratio (HR) for OS and RFS while optimizing their clinical utility to predict the benefit of HSCT. Results: We identified IL1R1 as the top gene associated with RFS in MVA and with a significant interaction with HSCT-TD (p < 0.05). Based on an optimal cutoff of 2.0 TPM, 193 (61%) and 123 (39%) patients had low (< 2.0 TPM, IL1R1low) and high (≥ 2.0 TPM, IL1R1high) expression of IL1R1, respectively. Patients with IL1R1high were older (median age 59 years with IL1R1high vs 54 years with IL1R1low, p < 0.01), had a higher WBC count (44 vs 31 x 109/L, p = 0.06), and a higher frequency of myelomonocytic or monocytic differentiation (43% vs 25%, p < 0.01). FLT3-ITD (54% vs 33%, p < 0.01) and RUNX1 (18% vs 9%, p = 0.04) mutations were more frequent in patients with IL1R1high whereas biallelic CEBPA mutations (1% vs 6%, p=0.04) were more frequent in patients with IL1R1low. High expression of IL1R1 was associated with worse clinical outcomes. The CR rate was 74% and 85% in patients with IL1R1high and IL1R1low, respectively (p = 0.02). The 5-year OS rate was 10% in patients with IL1R1high vs 38% in patients with IL1R1low (HR 2.27, p < 0.01). The 5-year cumulative incidence of relapse (CIR) was 17% higher in patients with IL1R1high (76% vs 59%, p < 0.01). In MVA, high expression of IL1R1 was independently associated with OS (HR 1.80, p < 0.01) and RFS (HR 1.81, p < 0.01). HSCT in CR1 significantly improved OS in patients with IL1R1high (HR for HSCT-TD 0.26, p < 0.01), but not in patients with IL1R1low (HR 0.70, p = 0.17). With a 6-month landmark analysis, the 5-year OS rates were 67% vs 27% among patients with IL1R1high and 62% vs 54% among patients with IL1R1low in patients who underwent HSCT in CR1 or not, respectively (Figure 1). Specifically among patients without FLT3-ITD mutations, patients with IL1R1high benefited from HSCT in CR1 (HR 0.41, p = 0.04) whereas patients with IL1R1low did not (HR 1.03, p = 0.92). Among patients who have undergone HSCT in CR1, the 5-year post-HSCT OS was 60% vs 56% in patients with IL1R1high and IL1R1low, respectively (HR 0.85, p = 0.68), confirming that HSCT may abrogate the adverse impact of high IL1R1 expression. Conclusion: In patients with IRC AML, high expression of IL1R1 is independently associated with worse OS and RFS which may be overcome by HSCT in CR1. Among patients without FLT3-ITD mutations who account for ~60% of IRC AML, those with IL1R1high benefit from HSCT in CR1, as opposed to those with IL1R1low. IL1R1 expression may guide the decision to proceed with HSCT in CR1 in these patients. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal
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 | 0,001 | 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 | 0,001 | 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) | 0,001 | 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 ».