A small molecule screening strategy with validation on human leukemia stem cells uncovers the therapeutic efficacy of kinetin riboside
Bibliographic record
Abstract
Gene regulatory networks that govern hematopoietic stem cells (HSCs) and leukemia-initiating cells (L-ICs) are deeply entangled. Thus, the discovery of compounds that target L-ICs while sparing HSC is an attractive but difficult endeavor. Presently, most screening approaches fail to counter-screen compounds against normal hematopoietic stem/progenitor cells (HSPCs). Here, we present a multistep in vitro and in vivo approach to identify compounds that can target L-ICs in acute myeloid leukemia (AML). A high-throughput screen of 4000 compounds on novel leukemia cell lines derived from human experimental leukemogenesis models yielded 80 hits, of which 10 were less toxic to HSPC. We characterized a single compound, kinetin riboside (KR), on AML L-ICs and HSPCs. KR demonstrated comparable efficacy to standard therapies against blast cells in 63 primary leukemias. In vitro, KR targeted the L-IC-enriched CD34(+)CD38(-) AML fraction, while sparing HSPC-enriched fractions, although these effects were mitigated on HSC assayed in vivo. KR eliminated L-ICs in 2 of 4 primary AML samples when assayed in vivo and highlights the importance of in vivo L-IC and HSC assays to measure function. Overall, we provide a novel approach to screen large drug libraries for the discovery of anti-L-IC compounds for human leukemias.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".