A rapid gene expression profiler predicts tumor responsiveness and patient outcome for standard-of-care therapies in acute myeloid leukemia
Notice bibliographique
Résumé
Abstract Introduction: The molecular heterogeneity of acute myeloid leukemia (AML) complicates clinical assessments for optimal therapeutic selection and management of emergent resistance based on mutations and cytogenetics alone. The incorporation of gene expression profiles has the potential to augment current prognostic stratification. In patients treated with intensive chemotherapy, a 17-gene expression-based signature of leukemia stem cells (LSC17) is predictive of survival outcomes and responsiveness (Ng et al., 2016). For patients ineligible for intensive chemotherapy, less differentiated tumors are more sensitive to hypomethylating agents plus the BCL2 inhibitor venetoclax (HMA+Ven), whereas differentiated tumors exhibit reduced sensitivity (Pei et al., 2020; Kuusanmäki et al., 2020). Transcriptomic classification of AML tumors according to differentiation state (van Galen et al., 2019) aligns with ex vivo Ven sensitivity (Bottomly et al., 2022; Zeng et al., 2022). Methods: We developed a rapid, gene expression-based assay called Myelo-ID to simultaneously profile AML stemness (LSC17), tumor differentiation states, and apoptotic target gene levels. RNA extracted from freshly isolated primary AML patient mononuclear cells was used to synthesize cDNA. Samples were loaded onto a custom TaqMan Array Card panel for the targeted genes and amplified by quantitative PCR (qPCR). Expression data were analyzed for quality control, normalization, calculation of enrichment scores, and report generation in a custom R workflow. Banked AML patient samples with matched RNAseq and ex vivo Aza+Ven drug sensitivity data were used to validate tumor cell state scores. LSC17 scores were validated through an exchange of reference cohort patient samples analyzed using a clinical Nanostring-based assay (Ng et al., 2022). Clinical, genetic, and treatment information was curated from patient medical records. Features for HMA+Ven-treated patients were analyzed by univariable and multivariable regression models for predictors (odds ratio (OR) or Cox hazard ratio (HR), area under the receiver operator characteristic curve (ROC AUC)) for achievement of composite complete remission (cCR) and overall survival (OS). Results: Profiling of 100 newly diagnosed (ND) AML patient samples spanning a range of clinical and genetic features revealed diverse patterns of cell state signature enrichment, in contrast to balanced expression in healthy marrow. Cell state enrichment scores were concordant with RNAseq-derived scores from the same samples (n=40; Pearson r: 0.531 to 0.943). LSC17 scores were highly correlated with scores derived from a Nanostring-based clinical assay (n=50; Pearson r=0.931), with significantly lower scores in patients achieving CR following 7+3 induction (p=2.76E-6). Primitive tumor expression profiles and increased BCL2 gene expression aligned with Aza+Ven ex vivo sensitivity (n=53; Pearson r: -0.623). Univariable analysis of 108 clinical, genetic, and gene expression features among 58 ND-AML patients treated with HMA+Ven identified mutated DNMT3A associated with both achieving cCR (OR: 7.22, p=0.016) and improved OS (HR: 0.37, p=0.010). Leukocytosis (OR: 0.16, p=0.005) and high promonocyte-like gene score (OR: 0.19, p=0.009) tracked with reduced likelihood of response; antecedent hematologic disorder (HR: 2.38, p=0.019) and mutated TP53 (HR: 2.89, p=0.005) were associated with shorter OS. High BCL2 gene expression was the most significant predictor of survival among all features examined (median OS: 19.6 vs 6.0 months; HR: 0.35, p=0.002). Multivariable regression models of the top clinical/mutation (leukocytosis, FLT3-ITD, mutated DNMT3A and RUNX1) and gene expression (promonocyte-like high) predictors for cCR showed value separately (ROC AUC: 0.890 and 0.693, respectively) but improved discrimination for responsiveness as a combined clinical/genetic/expression model (ROC AUC 0.920). High BCL2 gene expression alone was comparably predictive of 1-yr OS as the top clinical/genetic features (mutated DNMT3A andTP53; ROC AUC: 0.702 vs 0.688, respectively), with improved predictive value in a model combining these features (ROC AUC: 0.808).Conclusion:Incorporation of a rapid gene expression-based profiler of stemness, cell state, and apoptotic target genes, such as Myelo-ID assay, into the prospective evaluation of newly diagnosed AML patients may augment current clinical/genetic labs to help guide treatment selection and improve outcomes.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».