ATLAS-AML: An Automated Bioinformatics Pipeline for Drug Target Characterization in Acute Myeloid Leukemia
Notice bibliographique
Résumé
Acute myeloid leukemia (AML) is an aggressively heterogeneous disease with poor survival outcomes. An important checkpoint for AML drug development is ensuring target expression is enriched on leukemia cells compared to normal hematopoietic cells to avoid perturbing normal hematopoiesis. However, drugs satisfying this criterion still face additional challenges attributable to heterogeneity in AML, causing patients to develop resistance and relapse. In AML, chemoresistance and relapse are mediated by adverse genomic drivers and leukemia stem cell (LSC)-enriched cellular hierarchies (Zeng Nat Med 2022). This underscores the importance of identifying targets both enriched in leukemia and associated with sources of AML heterogeneity. Although many transcriptomic tools and pipelines have emerged for AML, none have linked gene expression to deep functional properties and non-genetic sources of intersample heterogeneity to enable informed predictions. To bridge this gap, we introduce ATLAS-AML, an automated bioinformatics pipeline for transcriptomic meta-analysis of genes and gene signatures in adult and pediatric AML. ATLAS-AML integrates 30 bulk (2172 donors) and single-cell (975,220 cells from 283 donors) RNA-sequencing datasets with preconfigured pipelines to streamline three key analyses for genes or gene signatures: (1) expression across normal and leukemic hematopoietic hierarchies, (2) enrichment in functionally-validated LSC+ fractions and (3) associations with relapse, genomics, clinical characteristics and patient survival. For established targets, ATLAS-AML can determine which patients, based on their mutational and cytogenetic profiles, are most likely to respond favorably to a potential treatment. ATLAS-AML is available as a containerized R package that experimental scientists can employ without bioinformatics expertise. To demonstrate how ATLAS-AML can guide target prioritization and characterization, we systematically analyzed published targets in ATLAS-AML. We identified 70 targets reported in the literature to be either enriched in leukemia, associated with LSCs, overexpressed at relapse, or predictive of patient survival. After benchmarking each gene against the aforementioned outcomes in ATLAS-AML, we observed that published targets were often optimized for particular outcomes, potentially overlooking other critical perspectives of AML biology. For example, several genes were enriched on leukemia cells, but not associated with genomics and stemness perspectives of AML heterogeneity nor clinical outcomes like relapse and patient survival. ATLAS-AML also constitutes a powerful framework for accelerating new target discovery. Reinterrogating our dataset with differential expression, we identified genes with enrichment in the same outcomes we benchmarked published targets against. Applying a meta p-value analysis, ATLAS-AML uncovered 13 targets that were overexpressed on leukemia cells compared to normal hematopoietic cells, enriched in functionally-validated LSCs, associated with disease relapse, and predictive of patient survival. For example, ATLAS-AML nominated CNST, a trans-Golgi network receptor for targeting connexins to the plasma membrane. CNST is differentially expressed on leukemia cells (p=0.000043), linked to functional LSC engraftment (p=0.000015), overexpressed at relapse (p=0.00036) and associated with poor prognosis in multivariable survival analysis in three independent cohorts (HR 1.23, p=0.04), lending to CNST's therapeutic viability in AML. Furthermore, ATLAS-AML determined that CNST expression was highest in RUNX1-mutated AMLs, identifying a patient group to prioritize for anti-CNST therapies. Altogether, ATLAS-AML enables scientists to leverage insights from single-cell and bulk transcriptomics to inform preclinical studies towards risk-tailored treatments in AML.
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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 | 0,000 | 0,000 |
| 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) | 0,000 | 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 ».