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Enregistrement W2903132660 · doi:10.1182/blood-2018-99-113504

Preclinical Activity of Novel MCL1 Inhibitor AZD5991 in Multiple Myeloma

2018· article· en· W2903132660 sur OpenAlexaff
Shannon M. Matulis, Vikas A. Gupta, Izabelle Brown, Jonathan J. Keats, Paul Secrist, Justin Cidado, Adriana E. Tron, Paola Neri, Nizar J. Bahlis, Jonathan L. Kaufman, Leonard T. Heffner, Sagar Lonial, Ajay K. Nooka, Lawrence Boise

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensInstitute of Cancer ResearchUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMCL1Cell cultureApoptosisVenetoclaxStromal cellAnnexin A5IC50Programmed cell deathMolecular biologyAnnexinChemistryCancer researchMedicineBiologyLeukemiaImmunologyBiochemistryDownregulation and upregulationGenetics

Résumé

récupéré en direct d'OpenAlex

Abstract We and others have previously demonstrated that MM is often dependent on MCL1 or co-dependent on MCL1 and BCLXL or BCL2 for survival. Therefore, drug development targeting MCL1 has been a top priority. Here we report on AZD5991, a specific small molecule inhibitor of MCL1. We treated 17 MM cell lines with increasing concentrations of AZD5991 for 24 h and measured Annexin V staining to determine the IC50s. Nine of the cell lines tested were highly sensitive to AZD5991 with IC50 values below 100 nM, 6 lines exhibited an intermediate sensitivity (IC50 100-1000 nM), and only 2 cell lines tested were resistant (IC50 >1000 nM). Six of the highly sensitive lines are t(11;14) and sensitive to venetoclax suggesting co-dependence on BCL2 and MCL1 for survival. We also determined the effect of the bone marrow microenvironment on the response of MM cell lines to AZD5991. We reported that IL-6 protects MM cell lines and patient samples from apoptosis by making the cells more MCL1 dependent. Based on this, we predicted IL-6 would have little to no effect on AZD5991-induced cell death. We treated 12 cell lines with AZD5991 in the presence of 1 ng/mL IL-6 or 10% Hs5 conditioned medium (CM) for 24 h and found that only 3/12 and 2/12 lines were protected from apoptosis in the presence of IL-6 and CM, respectively. Interestingly, when co-cultured with the stromal cell line Hs5, 7/11 lines tested were protected from AZD5991-induced cell death, suggesting cell-cell contact is influencing the response. This is in contrast to ABT-737 and venetoclax where cell-cell contact provided no additional protection than CM. Mechanistically apoptosis induced via MCL1 inhibition is not dependent on BIM expression as is the case with BCLXL and BCL2 inhibition. KMS26 and LP1 MM cell lines contain a bi-allelic deletion of BIM and we have reported their resistance to ABT-737. However, both cell lines respond to AZD5991 with IC50 values in an intermediate sensitivity range. Co-immunoprecipitation (CoIP) studies were employed to determine the protein bound to MCL1 that could be promoting apoptosis upon release. We found NOXA and BAK bound in KMS26 and LP1 and both were released from MCL1 in response to AZD5991. Additionally, CoIPs performed on cell lines expressing BIM showed NOXA, BIM, and BAK bound to MCL1 and released following treatment. To further investigate we used CRISPR-cas9 to generate MM cell lines lacking expression of NOXA, BAK, BAX, or BIM. In KMS26 and LP1, deletion of NOXA and BAX had little effect on AZD5991-induced cell death while the BAK deletion significantly inhibited apoptosis in both cell lines. Similar results were observed in the BIM expressing cell line OCI-My5, with no protection from AZD5991-induced apoptosis in the NOXA and BAX edited lines, significant protection in the BAK-deleted line, and an intermediate degree of protection in the BIM knockout line. In KMS18, BIM deficiency had a minimal effect on apoptosis following MCL1 inhibition, however both BAX and BAK were required for AZD-induced cell death. Additionally, we have tested 41 samples from 37 patients for sensitivity to AZD5991. Samples were treated with increasing concentrations to determine IC50 values in the same manner as the MM cell lines. The samples segregated into 4 groups based on IC50. The most sensitive group (N=3) had an IC50 below 10 nM. The largest group had an IC50 range of 50-114 nM (N=26). The last two cohorts were more resistant with a range of 500-916 nM (N=10) and 2 samples with an IC50 over 1300 nM. Since MCL1 is on 1q21, a frequently amplified region in MM, we determined if 1q21 gain was associated with sensitivity. For the 35 samples where FISH data were available, 18 had 1q21 gains by FISH while 17 were negative. There is a trend for the 1q21 gain cohort to be more sensitive (P=0.0573), with only 2/18 having an IC50 above 109 nM. In contrast for the 1q21 negative 7/17 were in the resistant groups. Thus 1q21 may be a marker of sensitivity to MCL1 inhibitors. The data reported here demonstrate that AZD5991 is effective at inducing apoptosis in MM and can overcome soluble microenvironment resistance factors that influence the response to venetoclax. This appears to be due to differential requirements for pro-apoptotic factors for BCL2 and MCL1 inhibition and suggests an underappreciated complexity in the role of BCL2 and MCL1 in cell survival. Finally these findings also suggest that 1q21 gain may be a marker for AZD5991 sensitivity. A clinical trial is currently ongoing in myeloma. Disclosures Secrist: AstraZeneca: Employment. Cidado:AstraZeneca: Employment, Equity Ownership. Tron:AstraZeneca: Employment. Neri:Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Bahlis:Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding. Kaufman:Roche: Consultancy; Abbvie: Consultancy; Karyopharm: Other: data monitoring committee; Janssen: Consultancy; BMS: Consultancy. Heffner:Pharmacyclics: Research Funding; Genentech: Research Funding; ADC Therapeutics: Research Funding; Kite Pharma: Research Funding. Lonial:Amgen: Research Funding. Nooka:Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Adaptive technologies: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; Spectrum Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees. Boise:AstraZeneca: Honoraria; Abbvie: Consultancy.

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 seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,077
Tête enseignante GPT0,361
Écart entre enseignants0,285 · 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

Citations9
Publié2018
Routes d'admission1
Résumé présentoui

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