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Record W2322809144 · doi:10.1158/1535-7163.targ-09-a51

Abstract A51: Identification of compounds targeting human leukemia stem cells

2009· article· en· W2322809144 on OpenAlexaff
Sean McDermott, Kolja Eppert, Aaron D. Schimmer, Yanina Eberhard, John E. Dick

Bibliographic record

VenueMolecular Cancer Therapeutics · 2009
Typearticle
Languageen
FieldChemistry
TopicClick Chemistry and Applications
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsLeukemiaStem cellCancer researchCancer stem cellHaematopoiesisCancer cellEtoposideCancerBiologyPopulationCytotoxic T cellProgenitor cellImmunologyMedicineCell biologyChemotherapyBiochemistryGeneticsIn vitro

Abstract

fetched live from OpenAlex

Abstract Over the past 15 years, research has conclusively shown the existence of a rare population of cancer cells, termed cancer stem cells, in leukemia, brain, colon, and breast cancers. These cells are biologically distinct from bulk cells and are the only cells able to initiate and sustain the disease. Recent experiments indicate that standard chemotherapy for leukemia is less effective against leukemia stem cells (LSC) than bulk leukemia cells and typically does not spare normal hematopoietic stem cells (HSC) and progenitor cells leading to myelosuppression. Therefore, a new paradigm is needed to develop cancer therapeutics that effectively eradicate the disease by targeting the LSC. Here, we sought to identify compounds that selectively target LSC but not normal HSC by using populations enriched for LSC and HSC, instead of traditional cancer cell lines, in high-throughput screening of three libraries of small chemicals comprising over 4000 known bioactive, off-patent, and natural compounds. Recently, we generated human leukemia cells by direct transformation of normal human primitive hematopoietic cells (Lin- CB) with leukemogenic fusion oncogenes. These cells exhibit features of LSC, such as hierarchical organization, engraftment of NOD/SCID mice, and a differentiation block. As a proof of principle, we screened two leukemia cell lines using a simple cell-growth inhibition assay. 76 compounds were scored as positive against both lines. Numerous anti-cancer therapeutics (paclitaxel, etoposide, and vincristine), general cytotoxic agents (brefeldin), and the digitalis family of ion pump inhibitors (digoxin, ouabain) were identified. Since LSC share some pathways with normal HSC, we counter-screened potential hits on normal HSC and progenitor cells and identified only 10 compounds. Three of the 10 compounds targeted LSC, ciclopirox olamine, etoposide, and kinetin riboside (KR). KR was effective on primary AML and CML at levels similar to the AML chemotherapeutics cytarabine and mitoxantrone. Kinetin riboside induced apoptosis in phenotypic CD34+CD38- LSC, but not CD34+CD38- HSC similar to the anti-LSC compound parthenolide. In contrast to parthenolide, treatment of primary AML cells with kinetin riboside inhibited engraftment in NOD/SCID mice for 2 of 4 samples. The second compound, ciclopirox olamine, targeted LSC and not HSC, by chelating intracellular iron and inhibited the ribonucleotide reductase enzyme. Ciclopirox olamine is a clinically used antifungal and could be rapidly repurposed for treating leukemia. The third compound, etoposide, was effective in vitro on 29% (15 of 51) of primary AML and 67% (8 of 12) of CML patient cells. Etoposide inhibited NOD/SCID engraftment of three responsive AML samples, but not three non-responsive samples, indicating etoposide targeted the LSC in a subset of patients. Together, these screens have identified multiple anti-LSC compounds and represent a new paradigm for drug screening against LSC. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A51.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.007
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

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

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 head0.027
GPT teacher head0.298
Teacher spread0.271 · 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 teacher head, 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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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