Ex Vivo High-Throughput Flow Cytometry Screening Identifies Subsets of Responders to Differentiation Agents in Individual AML Patient Samples
Notice bibliographique
Résumé
Abstract Background: Prognoses for acute promyelocytic leukemia (APL) patients improved drastically upon the introduction of differentiation therapy with all-trans-retinoic acid (ATRA) in combination with conventional chemotherapy. Unfortunately, this therapeutic approach has not translated to other genetic subtypes of acute myeloid leukemia (AML) where patients demonstrate marked heterogeneity to differentiating agents. To provide improved detection of drug-induced differentiation in AML patients, we have developed a high-throughput, flow cytometry-based personalized medicine platform. Methods: Total white blood cells were isolated from each patient sample by red cell lysis, plated in serum-free media in 384-well format and incubated with drugs for 3 days. Viable cells remaining after each drug treatment were identified and quantified using cell surface marker expression, cell membrane integrity, and morphology (FSC/SSC) to determine the compound's efficacy and specificity against the blast population. Changes in cell surface marker expression and shifts in morphology indicative of blast differentiation were also evaluated with each compound. As a control for ex vivo differentiation, two APL patient samples were treated ex vivo with ATRA and we observed the blasts gaining CD66b expression indicating granulocytic differentiation. Results: A refractory AML patient was identified whose leukemic blasts exhibited a strong differentiating response to dexamethasone treatment ex vivo. This resulted in loss of CD34 expression (a marker of immature blast cells), gain of CD163 expression (a marker of monocytic/macrophage maturation) and a significant change in cellular size and granularity. After being enrolled in a clinical trial (REB: 13-6962-C) the patient was treated based on the assay for 1 week (40 mg/day) with dexamethasone. Post-treatment samples from the peripheral blood and bone marrow of the patient exhibited the same morphological and cell surface marker changes predicted by the ex vivo assay. The CD163+ cells in the patient also gained additional markers of myeloid differentiation (CD11b, CD14, CD16). After additional cytarabine and fludarabine treatment, the patient remains in remission 4 months post-treatment. Conclusions: Following this initial study, we have continued to identify subgroups of both AML and Myelodysplastic Syndrome patients where blasts differentiate in response to dexamethasone, calcitriol, ATRA or other known differentiating agents using unique cell surface markers of monocytic and myeloid maturation. Flow cytometry expression changes correlated with changes in morphology as observed by May-Grunwald Giemsa staining. In the patient described above this included an increase in cytoplasm and vacuoles consistent with monocytic/macrophage differentiation, which positively correlates with CD163 expression. We aim to apply our assay towards the identification of subgroups of AML patients who respond to differentiation therapies and develop clinical trials to combine differentiating agents with chemotherapy. This approach has the potential to extend the clinical success of APL differentiation therapy to AML patients. Disclosures Prashad: Notable Labs: Employment, Equity Ownership. Western:Notable Labs: Consultancy. Biondi:Notable Labs: Employment. Shah:Notable Labs: Employment. Liu:Notable Labs: Employment, Equity Ownership. Nguyen:Notable Labs: Employment, Equity Ownership. Warnock:Notable Labs: Employment, Equity Ownership. Quinzio:Notable Labs: Employment, Equity Ownership. De Silva:Notable Labs: Employment, Equity Ownership. Schimmer:Novartis: Honoraria. Heiser:Notable Labs: 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 | 0,001 | 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) | 0,002 | 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 ».