Co-inhibition of mTORC1, HDAC and ESR1α retards the growth of triple-negative breast cancer and suppresses cancer stem cells
Bibliographic record
Abstract
Abstract Triple-negative breast cancer (TNBC) is the most refractory subtype of breast cancer. It causes the majority of breast cancer-related deaths, which has been largely associated with the plasticity of tumor cells and persistence of cancer stem cells (CSCs). Conventional chemotherapeutics enrich CSCs and lead to drug resistance and disease relapse. Development of a strategy capable of inhibiting both bulk and CSC populations is an unmet medical need. Inhibitors against estrogen receptor 1, HDACs, or mTOR have been studied in the treatment of TNBC; however, the results are inconsistent. In this work, we found that patient TNBC samples expressed high levels of mTORC1 and HDAC genes in comparison to luminal breast cancer samples. Furthermore, co-inhibition of mTORC1 and HDAC with rapamycin and valproic acid, but neither alone, reproducibly promoted ESR1 expression in TNBC cells. In combination with tamoxifen (inhibiting ESR1), both S6RP phosphorylation and rapamycin-induced 4E-BP1 upregulation in TNBC bulk cells was inhibited. We further showed that fractionated CSCs expressed higher levels of mTORC1 and HDAC than non-CSCs. As a result, co-inhibition of mTORC1, HDAC, and ESR1 was capable of reducing both bulk and CSC subpopulations as well as the conversion of fractionated non-CSC to CSCs in TNBC cells. These observations were partially recapitulated with the cultured tumor fragments from TNBC patients. Furthermore, co-administration of rapamycin, valproic acid, and tamoxifen retarded tumor growth and reduced CD44 high/+ /CD24 low/ − CSCs in a human TNBC xenograft model and hampered tumorigenesis after secondary transplantation. Since the drugs tested are commonly used in clinic, this study provides a new therapeutic strategy and a strong rationale for clinical evaluation of these combinations for the treatment of patients with TNBC.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".