Single Cell Network Profiles in Non-M3 AML Associated with Patient Response to Standard Induction Therapy.
Notice bibliographique
Résumé
Abstract Abstract 1582 Poster Board I-608 Background Traditional AML prognostic markers are based on clinical characterization (e.g. age) or static measurements of leukemia biology present at diagnosis, such as cytogenetics and isolated molecular events (e.g. presence of FLT3 ITD mutation). No validated methods currently exist to predict the disease response to standard AML induction chemotherapy for individual patients. Objectives: Single Cell Network Profiling (SCNP) was used to measure intracellular signaling in response to extracellular modulators in order to develop a new proteomic tool to characterize and monitor AML biology in the context of therapeutic applications. Methods Modulated SCNP using a multiparametric flow cytometry platform was performed evaluating the phosphorylation of intracellular signaling molecules in their basal states and after treatment with modulators in specific cell populations (e.g. leukemic cells). Since multiple signaling pathways may be dysregulated in AML and contribute to the likelihood of response to a given therapy, pathways that affect proliferation, apoptosis, and DNA damage were analyzed. Analyses were aimed to assess assay reproducibility, identify a signaling profile associated with likelihood of response to standard induction chemotherapy (first training set, n=34), and test extrapolation of the identified profile to a fully independent set of AML samples (second training set; n=88). Results High assay reproducibility (Pearson correlation coefficients ≥ 0.8) was observed. In the first training study univariate analysis revealed multiple “nodes” (modulated read outs of proteins in signaling pathways) associated with disease response to conventional induction therapy (i.e. AUC of ROC >0.66; p<0.05). Importantly combination of some of the independently predictive nodes improved disease response stratification (AUC of ROC up to 1.0; p<0.05). Extrapolation of the assay to a second independent set of samples revealed similar findings after accounting for clinical covariates. Specifically, for patients <60 years, the presence of intact apoptotic pathways was correlated with complete response (CR) while in samples from patients ≥60 years increased p-Akt and p-Erk levels in response to FLT3L stimulation correlated with non response (NR). Importantly, the predictive values of these nodes was independent from cytogenetic and FLT3 mutational status. Conclusions The two studies reported here show that AML biology characterization in individual patients using modulated SCNP can be performed with high technical accuracy and reproducibility to quantitatively characterize the biology of AML. This approach can be used to generate highly predictive tests for therapeutic response independently of classic prognostic factors. Disclosures Kornblau: Nodality, Inc.: Consultancy. Rosen:Nodality, Inc.: Employment, Equity Ownership. Putta:Nodality, Inc.: Employment, Equity Ownership. Cohen:Nodality, Inc.: Employment, Equity Ownership. Covey:Nodality, Inc.: Employment, Equity Ownership. Fantl:Nodality, Inc.: Employment, Equity Ownership. Gayko:Nodality, Inc.: Employment, Equity Ownership. Cesano:Nodality, Inc.: Employment, Equity Ownership.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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 ».