Homoharringtonine demonstrates a cytotoxic effect against triple-negative breast cancer cell lines and acts synergistically with paclitaxel
Bibliographic record
Abstract
The lack of targeted therapies for triple-negative breast cancer (TNBC) contributes to their high mortality rates and high risk of relapse compared to other subtypes of breast cancer. Most TNBCs (75%) have downregulated the expression of CREB3L1 (cAMP-responsive element binding protein 3 like 1), a transcription factor and metastasis suppressor that represses genes that promote cancer progression and metastasis. In this report, we screened an FDA-approved drug library and identified four drugs that were highly cytotoxic towards HCC1806 CREB3L1-deficient TNBC cells. These four drugs were: (1) palbociclib isethionate, a CDK4/6 inhibitor, (2) lanatocide C (also named isolanid), a Na+/K+-ATPase inhibitor, (3) cladribine, a nucleoside analog, and (4) homoharringtonine (also named omacetaxine mepesuccinate), a protein translation inhibitor. Homoharringtonine consistently showed the most cytotoxicity towards an additional six TNBC cell lines (BT549, HCC1395, HCC38, Hs578T, MDA-MB-157, MDA-MB-436), and several luminal A breast cancer cell lines (HCC1428, MCF7, T47D, ZR-75-1). All four drugs were then separately evaluated for possible synergy with the chemotherapy agents, doxorubicin (an anthracycline) and paclitaxel (a microtubule stabilizing agent). A strong synergy was observed using the combination of homoharringtonine and paclitaxel, with high cytotoxicity towards TNBC cells at lower concentrations than when each was used separately.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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.
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".