High-throughput screening for compounds toxic to human leukemic cells derived from lineage-depleted cord blood cells
Bibliographic record
Abstract
A98 Over the past 10 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, indicating that 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. The novelty of this proposal is to use populations enriched for LSC and HSC, instead of cancer cell lines, in high-throughput screening of small chemical libraries. Recently, we generated human leukemia cell lines by direct transformation of normal human primitive hematopoietic cells (Lin(-) CB cells) with leukemogenic fusion oncogenes, TLS-ERG and MLL-ENL1. These cells exhibit features of LSC, such as hierarchical organization, engraftment of NOD/SCID mice, and a differentiation block, and have been stably maintained in culture for over a year. As a proof of principle, we screened two of the leukemia cell lines, TEX and M9-ENL1, using a simple cell-growth inhibition assay. We used three libraries of small chemicals comprising over 4000 unique known bioactive, off-patent, and natural compounds. 200 compounds (5%) were scored as positive against either TEX or M9-ENL1 cells. For further IC50 studies, we concentrated on the top 80 compounds that inhibited cell growth to 25% of controls. Numerous anti-cancer therapeutics (paclitaxel, etoposide, and vincristine), general cytotoxic agents (brefeldin), and the digitalis family of ion pump inhibitors (digoxin, ouabain) were identified. At this point, we removed 25 redundant compounds and screened 55 compounds on Lin(-) CB cells to identify compounds more specific for leukemia cells than normal cells. Half of the compounds were toxic to Lin(-) CB cells; Lin(-) CB cells were more sensitive than the leukemias for a third of the compounds; and 10 compounds had more activity on both leukemias than Lin(-) CB cells. Currently, we are testing these top 10 compounds using in vitro progenitor assays and will validate the compounds using the NOD/SCID xenotransplantation model. These approaches will rapidly identify potential agents for the development of therapies directly targeting LSC.
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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 | 0.000 |
| 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".