High-Throughput Chemical Screen on Acute Myeloid Leukemia Stem Cells Identifies Novel Anti-LSC Compounds
Bibliographic record
Abstract
Abstract Acute myeloid leukemia (AML) is an aggressive form of blood cancer defined by the uncontrolled proliferation and clonal expansion of immature myeloblast cells in the blood and bone marrow, leading to hematopoietic failure. Despite the use of aggressive and cytotoxic standard-of-care drugs, patients often relapse and succumb to the disease partially due to the inability of medically unfit patients to withstand the cytotoxic treatments, regrowth from minimal residual disease and the chemo-resistant nature of leukemic stem cells (LSCs) which can remain in a quiescent state and reside in a protective bone marrow niche. Hence, novel therapies targeting unique leukemic stem cell biology are highly needed to eliminate and avoid reoccurrence. High-throughput screens of human AML LSCs are not performed due to technical issues such as low LSC frequency within primary samples, an inability to purify LSCs, and the difficulty maintaining and expanding primary patient samples and LSCs in vitro. We were able to optimize conditions for a 4-week in vitro large-scale expansion (>600 million bulk) of the primary human AML sample 8227 (OCI-AML-8227), functionally validated to be enriched for LSCs in long-term xenotransplant assays (Eppert et al., 2011). These optimized conditions enabled the isolation and maintenance of the LSC-containing fraction for a chemical screen. We isolated the CD34+ LSC-containing fraction (>90% purity) and performed a high-throughput screen of 11,166 chemical molecules using a CellTiter Glo assay followed by a counter screen against normal CD34+ cord blood (CB) hematopoietic stem and progenitor cells. From this HT screen, a total of 61 hits had >70% inhibition on CD34+ 8227 cells and <30% inhibition on CD34+ CB cells. We also identified glucocorticoids, which were also identified in our prior small-scale anti-LSC screen where they were found to specifically drive human LSCs to terminally differentiate (Laverdière & Boileau, et al., 2018). We then performed dose response assays for each candidate compounds and confirmed 35 potent anti-LSC compounds with IC 50 < 1 μM. This refined the types of compounds to including anti-apoptotic inhibitors, GSK inhibitors, protease inhibitors, metabolism inhibitors, HDAC inhibitors, BET inhibitors, nucleic acid synthesis inhibitors, cell cycle inhibitors and Wnt/β-catenin inhibitors. This is interesting as some of the classes of these compounds (inhibitors of GSK, BET, nucleic acid synthesis, Wnt/β-catenin and metabolism) have been shown to target bulk and leukemic stem cells in AML in vitro and in vivo. We now aim to examine LSC eradication in a panel of genetically defined primary AMLs confirmed through in vitro and in vivo assays. Our goal is to be able to understand and establish the molecular mechanisms and biomarkers on primary functional LSCs. Disclosures No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".