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Enregistrement W2982913861 · doi:10.1182/blood-2019-129990

A 9-Gene Expression Signature Identifies Patients with Core-Binding Factor Acute Myeloid Leukemia at High Risk of Relapse after Achieving Complete Remission

2019· article· en· W2982913861 sur OpenAlexaff
Guillaume Richard‐Carpentier, Miriam Marquis, François Béliveau, Guy Sauvageau, Josée Hébert

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensInstitute for Research in Immunology and CancerUniversité de MontréalHôpital Maisonneuve-Rosemont
Organismes subventionnairesnon disponible
Mots-clésOncologyInternal medicineCytarabineProportional hazards modelHazard ratioMyeloid leukemiaMedicineUnivariate analysisGene signatureHematologyChemotherapy regimenChemotherapyBiologyGene expressionGeneMultivariate analysisConfidence intervalGenetics

Résumé

récupéré en direct d'OpenAlex

Background: Core-binding factor acute myeloid leukemias (CBF-AML) are characterized by inv(16)/t(16;16)(p13.1;q22) or t(8;21)(q22;q22.1) and encompass a distinct subgroup of AML with a favorable prognosis. This subgroup represents approximately 15% of newly diagnosed adult AML and is more frequent in young adults. With intensive chemotherapy, nearly all patients with CBF-AML achieve complete remission (CR) and the long-term overall survival (OS) is 75 to 85%. However, up to 40% of adult patients will relapse after achieving CR and some patients may die of progressive disease. Early identification of patients with higher risk disease would help to select patients who may benefit from targeted or higher intensity therapeutic approaches to reduce their risk of relapse. The aim of this study was to develop a gene expression signature to predict at time of diagnosis the risk of relapse in patients with CBF-AML. Methods: We analyzed the RNA sequencing (RNA-Seq) data of 44 diagnostic specimens from patients with de novo CBF-AML treated with intensive chemotherapy (7+3 regimen followed by high-dose cytarabine consolidations). We performed logarithmic transformation and standardization of RNA-Seq data normalized in RPKM and excluded genes with very low expression (< 1 RPKM in all specimens). We selected the genes which were significantly associated with relapse-free survival (RFS) in univariate Cox proportional hazard (CPH) models with a significance level of < 0.05. In this subset of genes, we fitted a CPH regression model with the LASSO algorithm using L1 penalty regularization on coefficients to minimize the 10-fold cross-validation partial likelihood deviance. We derived a score from the genes selected by the LASSO algorithm based on the sum of each gene expression weighted by their regression coefficients. The prognostic impact of the score was evaluated in CPH regression models for RFS and OS. We also assessed the prognostic impact of individual genes included in the score after dichotomizing them into binary variables. Results: Our cohort of CBF-AML included 28 patients with inv(16)/t(16;16) and 16 patients with t(8;21). Characteristics of the patients at diagnosis are summarized in table 1. The median age at diagnosis was 42.5 year-old (range, 17-75). Patients with inv(16)/t(16;16) had a higher white blood cell count at diagnosis (median 42.8 vs 18.7, p < 0.01). Rates of CR were 100% (16/16 pts) and 89% (25/28 pts) for patients with t(8;21) and inv(16)/t(16;16), respectively. The 3 pts with inv(16)/t(16;16) who did not achieve CR were older than 60 year-old and died during induction of chemotherapy-related complications. With a median follow-up of 6.8 years, 28 patients remain alive in remission, 12 patients relapsed and 1 patient died in CR. The 5-year OS and RFS are 72% (95% confidence interval [CI], 60 - 87%) and 67% (95% CI, 54 - 84%), respectively. Among 24 586 genes for which RNA-Seq data was available, 506 genes (2.06%) were significantly associated with RFS in univariate analyses. Nine genes were selected by the LASSO algorithm and were used for calculation of the CBF-score: CPSF6, DUSP2, H3F3A, NAB2, NOTCH3, PAG1, RASGEF1A, THRAP3, TRIM24. The CBF-score as continuous variable was significantly associated with RFS (p < 0.001) and OS (p = 0.003), and remained significant for both outcomes when adjusted for age. When the CBF-score was dichotomized on the median, none of the patients with a low score relapsed whereas 12/21 (57.1%) patients with a high score relapsed. The estimated 5-year RFS rates were 100% (95% CI, 100 - 100%) and 37% (95% CI, 20 - 65%) in patients with a low and high CBF-score, respectively (p < 0.001) (Figure 1A-1B). When each gene of the score were individually dichotomized, all except NOTCH3 were significantly associated with worse RFS. For any additional adverse single-gene expression signature, the hazard ratio for RFS was 2.17 (p < 0.001). In patients with 4 or more adverse single-gene signatures, the 5-year RFS was 25% (95% CI, 11 - 58%) versus 95% (95% CI, 86 - 100%) in patients with 3 or less single-gene signatures (Figure 1D). Conclusion: We developed in silico a 9-gene expression signature that predicts with high accuracy patients with CBF-AML at high risk of relapse (high CBF-score) and patients who are likely to be cured with standard chemotherapy (low CBF-score). This prognostic signature is currently being validated in independent internal and external validation cohorts. Disclosures Sauvageau: ExCellThera: Consultancy, Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,011
Tête enseignante GPT0,239
Écart entre enseignants0,229 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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