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Enregistrement W2187901936 · doi:10.1182/blood.v114.22.2745.2745

Distinct Signaling Profiles of Gemtuzumab Ozogamicin Responsiveness and Refractoriness in Acute Myeloid Leukemia.

2009· article· en· W2187901936 sur OpenAlexaff
David B. Rosen, James Cordeiro, David M. Soper, Ying-Wen Huang, Donna E. Hogge, Roland B. Walter, Alessandra Cesano, Wendy J. Fantl

Notice bibliographique

RevueBlood · 2009
Typearticle
Langueen
DomaineChemistry
ThématiqueClick Chemistry and Applications
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésGemtuzumab ozogamicinCalicheamicinMyeloid leukemiaCD33PharmacologyMedicineLeukemiaCytarabineCancer researchImmunologyBiologyStem cellCD34Cell biology

Résumé

récupéré en direct d'OpenAlex

Abstract Abstract 2745 Poster Board II-721 Background: Gemtuzumab Ozogamicin (GO, Mylotarg), a humanized CD33 monoclonal antibody linked to calicheamicin was approved by the US FDA for use as a monotherapy in patients older than 60 years with relapsed acute myeloid leukemia (AML) unfit to tolerate standard salvage therapy. GO is internalized rapidly after infusion, and calicheamicin, a potent enediyene, is subsequently released and acts as a cytotoxic agent by causing double strand DNA breaks. Currently GO is in multiple clinical trials as a single agent or in combination with other therapies for both induction and consolidation treatment of various clinical subgroups of AML. However, the mechanisms of action and resistance of GO are incompletely understood and it is unclear which patient subgroups benefit from GO-based therapy. Single cell network profiling (SCNP) has shown promise as a methodology wherein multiple signaling networks are measured after treatment with an exogenous modulator such as a growth factor, cytokine or therapeutic agent and the identified signaling profiles can be used as clinical and therapeutic enablement tools. Objectives: SCNP using multiparameter flow cytometry was used to identify intracellular pathways that were associated with responsiveness or refractoriness to in vitro GO exposure in both cancer cell lines and primary AML samples. Methods: Signaling pathways emphasizing DNA damage response, cell cycle, apoptosis and drug transporter activity were measured by SCNP after in vitro exposure of cell lines and AML primary samples to clinically relevant concentrations of GO. Samples were processed for cytometry by paraformaldehyde /methanol fixation and permeabilzation followed by incubation with fluorochrome-conjugated antibody cocktails that recognize cell surface proteins to delineate cell subsets and intracellular signaling molecules. Results: In cell lines, responsiveness to in vitro GO exposure was defined as a) induction of DNA Damage as measured by increased p-ATM, p-Chk2 and p-H2AX, b) cell cycle arrest at G2/M as measured by increased cyclin B1 and DNA content & c) induction of apoptosis as measured by cleaved PARP and viability dyes. Of note, inhibition of drug transporter activity in 2 MDR-1+ cell lines did not restore GO responsiveness, suggesting the presence of additional relevant resistance mechanisms in these cell lines. In primary AML diagnostic samples, DNA damage and apoptosis pathway readouts were able to identify responsiveness or refractoriness to GO exposure. In the GO responsive profile, induction of both DNA damage responses and apoptosis were seen. Within the refractory samples, two distinct profiles were observed: a) robust and early induction of DNA damage response without apoptosis and 2) delayed and attenuated DNA damage response without apoptosis. Conclusions: Characterization of intracellular Cell Cycle, DNA Damage, and Apoptosis networks in single cells after GO exposure distinguishes GO responsive from refractory AML cells. Further, these pathway signatures provide information about mechanisms of refractoriness. (e.g. a block between a successful DNA damage response and initiation of apoptosis versus a block in the initial induction of DNA damage after GO exposure). The ability of the same profiles to predict clinical responses to the drug will be tested in future studies. Disclosures: Rosen: Nodality, Inc.: Employment, Equity Ownership. Cordeiro:Nodality Inc.: Employment, Equity Ownership. Soper:Nodality Inc.: Employment, Equity Ownership. Huang:Nodality Inc.: Employment, Equity Ownership. Cesano:Nodality Inc.: Employment, Equity Ownership. Fantl: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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,570

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,012
Tête enseignante GPT0,258
Écart entre enseignants0,246 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2009
Routes d'admission1
Résumé présentoui

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