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

Preclinical Activity of Novel MCL1 Inhibitor AZD5991 in Multiple Myeloma

2018· article· en· W2903132660 on 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

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsInstitute of Cancer ResearchUniversity of Calgary
Fundersnot available
KeywordsMCL1Cell cultureApoptosisVenetoclaxStromal cellAnnexin A5IC50Programmed cell deathMolecular biologyAnnexinChemistryCancer researchMedicineBiologyLeukemiaImmunologyBiochemistryDownregulation and upregulationGenetics

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.361
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations9
Published2018
Admission routes1
Has abstractyes

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