FLT3 ITD Signaling Profiles in AML Samples Harboring Mutations.
Notice bibliographique
Résumé
Abstract Abstract 1588 Poster Board I-614 Background Mutations in the receptor tyrosine kinase (RTK) Fms-like tyrosine kinase 3 (FLT3) gene are among the most common somatic mutations in AML with FLT3 internal tandem duplications (ITDs) occurring in 20-35% of adult and 5-15% of pediatric AML. While the presence of FLT3 ITD mutation does not appear to influence outcome to induction chemotherapy, this mutation has been shown to confer a poor prognosis with significantly shorter disease free and relapse free survival. For patients with intermediate risk cytogenetically normal AML, molecular testing for FLT3 ITD has recently been incorporated into the National Comprehensive Cancer Network (NCCN) guidelines for clinical practice. However, while molecular testing can identify a subset of patients at high risk for relapse, there remains clinical heterogeneity likely due to differences in activation of signal transduction networks. Objectives This study tested the ability to use single cell network profiling (SCNP), in which cells are perturbed with extracellular modulators and their response ascertained by multiparametric flow cytometry, to identify a more clinically predictive functional readout of activation state, intracellular signaling capabilities and pathway dysregulation in the context of FLT3 mutational status. Methods Modulated SCNP was performed sequentially on two independent sets of patient samples (n=32 peripheral blood and n=85 bone marrow samples respectively). 304 and 201 “node-metric” i.e. modulated read outs of dynamic elements on individual proteins in signaling pathways were measured in the two sets respectively. These were derived from pathways known to be relevant to Flt3 WT and Flt3-ITD signaling (e.g. Ras-Raf-Erk-S6, PI3K-Akt-S6, STATs), as well as in-vitro chemotherapeutic induction of apoptosis (cleaved PARP, cleaved caspases), phosphatases, drug transporters (e.g. MDR-1, ABCG2) and expression of growth factor RTKs (e.g. Flt3R, c-Kit). Results In the first study, univariate analysis revealed 76 nodes out of 304 tested that distinguished FLT3 ITD from FLT3 WT patient samples (i.e. AUC of ROC >0.7; p<0.05). Analysis of false discovery rate showed this frequency to be significantly greater than the number of nodes that can be expected by chance (p=0.0009). Although several nodes were found to be correlated, many were independent of each other and represented multiple signaling pathways. Importantly, multivariate analysis showed that combinations of independently predictive nodes improved stratification over the single nodes (AUC of ROC up to 0.99) with respect to distinguishing WT and ITD FLT3 samples. Independent analysis of a second set of samples, revealed several nodes in common between the 2 studies which distinguish FLT3 ITD from WT, including etoposide/c-PARP (apoptosis), IL-27/p-STAT3, 5 (JAK/STAT pathways) and Flt3L/p-S6 (Ras/Erk/mTOR/S6 or PI3K/mTor/S6 pathways). In both sample sets, Flt3 receptor expression did not differ significantly between FLT3 ITD and FLT3 WT samples. Conclusions Pathway analysis by SCNP revealed significant differences in signaling in FLT3 ITD relative to WT AML samples across multiple pathways. We propose that a functional signature of FLT3 signaling is distinct from the existing molecular typing and may improve the ability to predict prognostic outcomes in individual AML patients. The impact of other important prognostic, molecular markers within the FLT3 context (e.g. NPM1) are currently under investigation. Disclosures Kornblau: Nodality, Inc.: Consultancy. Rosen:Nodality, Inc.: Employment, Equity Ownership. Cleary Cohen:Nodality Inc.: Employment, Equity Ownership. Gayko:Nodality, Inc.: Employment, Equity Ownership. Putta:Nodality, Inc.: Employment, Equity Ownership. Woronicz:Nodality, Inc.: Employment, Equity Ownership. Fantl:Nodality, Inc.: Employment, Equity Ownership. Cesano:Nodality, Inc.: Employment, Equity Ownership.
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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 | 0,000 |
| 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) | 0,001 | 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 ».