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Enregistrement W2972591852 · doi:10.1182/blood.v130.suppl_1.105.105

Podoplanin Expression in the Bleeding Complications of Acute Promyelocytic Leukemias

2017· article· en· W2972591852 sur OpenAlexaff
Vincent‐Philippe Lavallée, Jalila Chagraoui, Tara MacRae, Miriam Marquis, Arnaud Bonnefoy, Jana Krošl, Sébastien Lemieux, Georges‐Étienne Rivard, Josée Hébert, Guy Sauvageau

Notice bibliographique

RevueBlood · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphatic System and Diseases
Établissements canadiensHôpital Maisonneuve-RosemontUniversité de MontréalInstitute for Research in Immunology and Cancer
Organismes subventionnairesnon disponible
Mots-clésPodoplaninAcute promyelocytic leukemiaMedicineHyperfibrinolysisDisseminated intravascular coagulationPlateletInternal medicineCancer researchImmunologyCoagulationBiologyLymphatic systemGene

Résumé

récupéré en direct d'OpenAlex

Abstract Background: Acute promyelocytic leukemia (APL), characterized by the t(15;17) translocation, represents a favorable-risk subgroup of AML patients when treated with ATRA-based regimens. The leading cause of early death and the major unmet medical need in this disease are the bleeding complications, which are mainly attributed to aberrant expression of Tissue Factor (TF) and Annexin A2 (ANXA2) on leukemic promyelocytes, leading to disseminated intravascular coagulation and to hyperfibrinolysis, respectively. APL patients are also at increased risk of venous and arterial thromboses. The mechanisms underlying APL-associated hemostatic complications are not fully elucidated. Aims and Methods: We analyzed the transcriptome of 30 APL specimens comprised in the Leucegene 430 AML cohort, as well as normal hematopoietic cell populations (n=63) aiming to better understand the hemostasis-related transcriptomic landscape of this subgroup. Results: We identified podoplanin (PDPN) as the most significantly and differentially overexpressed gene in APL (median 2.6 vs 0 RPKM for APL and AML, respectively, q-value = 7.3 x 10-29) and determined that PDPN gene expression correlates with protein surface expression as assessed by flow cytometry on primary APL cells (r=0.89). TF (6.6 vs 1.6, p=1.8x10-9) and ANXA2 (142.8 vs 69.1, p=1.9x10-5) are also more expressed in APL, but less specifically than PDPN. Podoplanin is a glycoprotein that physiologically binds to its receptor, CLEC2, on platelets to induce platelet aggregation causing the separation of blood and lymphatic vessels during embryogenesis (Uhrin et al Blood 2010). As anticipated, we found that PDPN is not expressed in all studied sorted cell subpopulations from normal blood or bone marrow specimens, including in promyelocytes (median of 0 RPKM for all), indicating that platelets are most likely not exposed to PDPN in the adult vasculature and that this protein is ectopically expressed on APL promyelocytes. Next, we used lentiviral gene transfer to engineer OCI-AML5 cells, which do not express PDPN, to ectopically express this protein (AML5PDPN). When mixed with platelet-rich plasma (PRP), we found that AML5PDPN cells have much greater platelet-binding and activating capacity than AML5CTRL cells. Using light transmission aggregometry, only AML5PDPN cells, not AML5CTRL cells, could induce platelet aggregation at tested concentrations. These results indicate that PDPN expression on leukemia cells is sufficient to induce platelet binding, activation and aggregation. Using patient-derived cells, we similarly found that PDPN-expressing primary APL cells (n=3 samples), but not PDPN-negative APL (n=2) or AML (n=5) cells, have the capacity to bind and activate platelets and to induce platelet aggregation. Our results suggest that PDPN could contribute to APL-related thromboses, as was recently reported in brain tumors (Riedl et al Blood 2017). We next tested the hypothesis that PDPN expression on human myeloid blasts leads to platelet consumption, thrombocytopenia and bleeding in vivo . NSG mice were transplanted with either AML5CTRL or AML5PDPN cells and were monitored until they showed clinical signs of leukemia. Platelet counts rapidly and significantly dropped in the AML5PDPN-cohort at day 25 and beyond when compared to control animals (median 343 vs 1005 x 109/L, p 15:00 vs 7:45, p = 0.0003). Finally, we hypothesized that ATRA reduces bleeding complications in patients, at least partially, by decreasing PDPN expression. We found that PDPN surface protein on primary APL promyelocytes is markedly decreased within the first 24 hours of ATRA treatment in vitro, and levels reached less than 10% on day 4 when compared to untreated cells (DMSO). Arsenic trioxide or dexamethasone treatment led to a modest or no reduction in PDPN expression, respectively. Conclusion: PDPN expression is specific to APL and, by inducing platelet binding, activation and aggregation, it contributes to platelet consumption and to bleeding complications. Our findings may also expose a new promising angle for the treatment of the early hemostatic complications found in APL patients. Disclosures No relevant conflicts of interest to declare.

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: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,139
Score d'incertitude au seuil0,130

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 Opus0,036
Tête enseignante GPT0,309
Écart entre enseignants0,273 · 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'étudeObservationnel
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

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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