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Record W3095224165 · doi:10.1182/blood-2020-141740

A Stemness-Based Screen Identifies PLK1 Inhibitors for Targeting Leukemia Stem Cells in AML

2020· article· en· W3095224165 on OpenAlexaff
Qiang Liu, Amit Subedi, Samantha Yao, Changjiang Xu, Véronique Voisin, Gary D. Bader, Steven M. Chan, Jean Wang

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsStem cellCD38LeukemiaMyeloid leukemiaCancer researchCD34PLK1BiologyImmunologyCellCell cycleCell biologyGenetics

Abstract

fetched live from OpenAlex

The main barrier to curing acute myeloid leukemia (AML) is disease relapse, which occurs due to therapy resistance and persistence of leukemic stem cells (LSCs) after conventional induction chemotherapy. Thus drug discovery efforts must focus on identifying agents that effectively target LSCs and not just bulk blasts. To this end, we employed a multi-parametric stemness screen of 1200 bioactive small molecules to identify drugs targeting LSCs, based on reduction of the stem cell compartment of a functionally-characterized hierarchical AML model (OCI-AML-8227) assessed by flow cytometry. In this cell line, self-renewing LSCs are restricted to the CD34+CD38- fraction. The screen identified a number of compound classes with the potential to antagonize LSC properties, including those already in clinical use for AML as well as classes of compounds whose effects in AML have not been previously reported (Figure 1A). Top hits were further validated based on treatment-induced alteration of the expression profile of 104 LSC genes (LSC104) differentially expressed between LSC+ and LSC- fractions of primary AML, which captures stemness properties. The LSC17 score, which is strongly associated with survival and response to standard therapy in AML, was derived from the LSC104 gene set. Notably, all Polo-like kinase 1 (PLK1) inhibitors in the library were identified as top hits in the screen. In vitro treatment of OCI-AML-8227 cells with PLK1 inhibitors over 3 days selectively inhibited the CD34+CD38- fraction enriched in LSCs (Figure 1B), and decreased correlation of gene expression to the LSC104 signature in bulk cells (Figure 1C). Together, these data support a role for PLK1 in regulating leukemic stemness, and we prioritized this class of compounds for validation studies. PLK1 is an important regulator of cell cycle and its best studied role is in the regulation of mitotic entry. However, PLK1 is expressed in and likely plays an important role in all phases of the cell cycle. For instance, PLK1 has been described to regulate cilia disassembly at G0/1. The PLK1 inhibitor volasertib was previously tested in a Phase III trial against AML in combination with low-dose cytarabine (LDC) for elderly patients not eligible for induction chemotherapy. In this trial, although efficacy was observed, significant toxicity in the volasertib+LDC treatment arm resulted in poor survival outcomes for this group of patients. We evaluated the toxicity of volasertib treatment in vitro against two hierarchical AML cell lines (OCI-AML-8227 and OCI-AML-21) as well as normal cord blood (CB). Similar to CB, self-renewing stem cells for these two AML cell lines are restricted to the CD34+CD38- fraction. Treatment with volasertib at 20nM over three days resulted in significantly more cytotoxicity to the AML cell lines compared to CB (Figure 1D), especially in the CD34+CD38- compartment, suggesting that a therapeutic window exists. To evaluate the effects of PDK1 inhibitors against LSCs in vivo, we treated mice bearing AML patient xenografts with single-agent volasertib at a low dose (10mg/kg twice weekly for 4 weeks by oral gavage) starting 4 weeks post-transplant. The gene expression profile for 2 of 4 samples tested showed decreased correlation to the LSC104 signature after volasertib treatment, supporting an effect on stemness (Figure 1E). Volasertib treatment significantly reduced AML engraftment in 4 of 7 samples (Figure 1F). To evaluate the effect of volasertib on LSCs in primary treated mice, we performed secondary transplantation at limiting doses. Volasertib treatment significantly reduced LSC frequency in 2 of 4 samples tested (Figure 1G). Notably, sample AML5 showed a 31.8-fold reduction in LSC frequency compared to controls (p = 0.039) despite no significant reduction in bulk engraftment in primary treated mice, suggesting that volasertib may selectively target LSCs in this sample. In conclusion, our data indicate that the PLK1 inhibitor volasertib, identified as a top hit in a stemness-based drug screen, can target LSCs and decrease stemness properties in some primary AML samples. These findings support further studies of the potential of PLK1 inhibitors for the treatment of AML. Figure Disclosures Wang: Trilium Therapeutics: Patents & Royalties.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.280
Teacher spread0.250 · 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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Citations2
Published2020
Admission routes1
Has abstractyes

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