Aminosteroid RM-581 Decreases Cell Proliferation of All Breast Cancer Molecular Subtypes, Alone and in Combination with Breast Cancer Treatments
Bibliographic record
Abstract
Breast cancer (BC) is a heterogenous disease classified into four molecular subtypes (Luminal A, Luminal B, HER2 and triple-negative (TNBC)) depending on the expression of the estrogen receptor (ER), the progesterone receptor (PR) and the human epidermal receptor 2 (HER2). The development of effective treatments for BC, especially TNBC, remains a challenge. Aminosteroid derivative RM-581 has previously shown an antiproliferative effect in multiple cancers in vitro and in vivo. In this study, we evaluated its effect in BC cell lines representative of BC molecular subtypes, including metastatic TNBC. We found that RM-581 has an antiproliferative effect on all BC molecular subtypes, especially on Luminal A and TNBC, in 2D and 3D cultures. The combination of RM-581 and trastuzumab or trastuzumab-emtansine enhanced the anticancer effect of each drug for HER2-positive BC cell lines, and the combination of RM-581 and taxanes (docetaxel or paclitaxel) improved the antiproliferative effect of RM-581 in TNBC and metastatic TNBC cell lines. We also confirmed that RM-581 is an endoplasmic reticulum (EnR)-stress aggravator by inducing an increase in EnR-stress-induced apoptosis markers such as BIP/GRP78 and CHOP and disrupting lipid homeostasis. This study demonstrates that RM-581 could be effective for the treatment of BC, especially TNBC.
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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 | 0.001 |
| 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".