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Enregistrement W2980002967 · doi:10.1182/blood.v128.22.2113.2113

Ex Vivo Modeling of Multiple Myeloma Provides Basis for Studying Treatment Combinations and Immunotherapy

2016· article· en· W2980002967 sur OpenAlexaff
Michael P. Chu, Christopher P. Venner, Irwindeep Sandhu, Eva Baigorri, Jitra Kriangkum, Joanne D Hewitt, Andrew Belch, Linda M. Pilarski

Notice bibliographique

RevueBlood · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésImmunotherapyMedicineIpilimumabPembrolizumabEx vivoCancer researchTumor microenvironmentNivolumabMultiple myelomaImmunologyIn vivoImmune systemBiology

Résumé

récupéré en direct d'OpenAlex

Abstract Background Multiple myeloma (MM) remains incurable despite treatment advances. While passive immunotherapy such as anti-CD38 antibodies is highly effective, active immunotherapy may provide long-lasting remissions by virtue of triggering memory. A phase 1 nivolumab study, an antibody targeting programmed death-1 (PD1), was unable to yield any responses in multiply relapsed MM patients. Conversely, preliminary trial data of lenalidomide combined with pembrolizumab, a different anti-PD1 antibody, found significantly higher response rates. These two differing outcomes reflect our limited understanding of checkpoint inhibition and immunotherapy in MM. There is a paucity of preclinical models to guide therapeutic studies. Cell lines and xenografted murine models are incapable of exploring active immunotherapy due to a lack of microenvironment and endogenous immune cell signals. Furthermore, malignant cells responsive to drugs in 2-dimensional (2D) cultures are known to display a more resistance in 3D. We have previously demonstrated that B-cell malignancies can be accurately studied using a 3D culture system of patient bone marrow mononuclear cells (BMCs) and can better inform translational trials. Herein we describe an ex vivo, 3D tissue culture model of patient-derived MM samples to more accurately test therapeutics including checkpoint inhibition using ipilimumab, a monoclonal antibody targeting cytotoxic T-lymphocyte antigen 4 (CTLA) which is crucial in co-stimulatory signaling of effector T-cells. Methods A 3D extracellular matrix was created using matrigel in 12-well plates. BMCs were isolated from marrow aspirates of 5 MM patients at time of diagnosis and individually cultured. Each patient sample was tested for sensitivity against increasing concentrations of ipilimumab (1X, 3X, and 10X clinical doses) added into supportive medium. Plates were monitored visually by microscopy followed by harvest on day 21 using enzymatic degradation. Unique clonotypic heavy chain immunoglobulin rearrangement (IgH VDJ) from each sample was sequenced, validated and used for semi-quantitative PCR. Semi-QT PCR with clone-specific primers estimated malignant cell survival after harvest. Flow cytometry was used to define cell populations present in culture and to correlate with clonotypic PCR data. T-cell mediated activity was examined by reverse transcription of trizol-extracted, T-cell RNA after harvest. Results All samples were successfully cultured, followed for 21 days and harvested. Flow cytometry confirmed presence of T-cell subsets, B-cells, NK cells and dendritic cells before and after culture in 3D. Minimal depletion of clonotypic cells was observed at 3x clinical levels of drug. At 10x simulated clinical therapeutic levels, 3 MM samples demonstrated >90% death of clonotypic MM cells while the other 2 demonstrated 62% and 72% death, respectively, compared to untreated control cultures. The extent to which the drug diffuses into the matrigel is as yet unknown. Flow cytometry of harvested cells suggest that T-cells demonstrate a modest shift toward CD4 and CD8 effector cells. Preliminary mechanistic data from one MM sample using trizol-extracted RNA and reverse transcriptase PCR harvested at 21 days from 3D culture suggests that anti-malignant, cytotoxic T-cell effect may be driven by granzyme B expression. Expanded data from the remaining samples will be presented. Conclusions We demonstrate that an ex vivo 3D tissue culture model of MM is both feasible and informative in studying immunotherapy. By culturing unselected BMCs which include stromal cells, immune cells and malignant populations, the 3D culture more closely mimics the tumor microenvironment with both the patient's immune system present as well as stromal supportive signals. In this study, we show that in the presence of active immune effector cells, ipilimumab has activity against patient-derived MM cells. The data suggests the importance of targeted cytotoxic T-cell activation as a primary mechanism of action. We have previously studied standard MM therapeutics such as cytotoxic chemotherapy, immunomodulatory drugs, and proteasome inhibitors in the same way. Consequently, this model is well positioned to study other immunotherapies such as other checkpoint inhibitors, cellular therapy, and combinations. Further testing with therapeutics targeting PD1/PDL1, and adenosine receptors are underway. Disclosures Venner: Takeda: Honoraria; Celgene: Honoraria, Research Funding; J+J: Research Funding; Janssen: Honoraria; Amgen: Honoraria. Belch:Celgene: Honoraria; Janssen: Honoraria; Amgen: Honoraria; Takeda: Honoraria.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut 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 enseignants0,002
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

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 savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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 Opus0,053
Tête enseignante GPT0,314
Écart entre enseignants0,260 · 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 source (Gemma direct ou Codex distillé), 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

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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