Enhancement of taxol, doxorubicin and zoledronate anti-proliferation action on triple-negative breast cancer cells by a PTHrP blocking monoclonal antibody.
Bibliographic record
Abstract
Triple-negative breast cancers (TNBCs) are heterogeneous cancers that present tumors without the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Because of the absence of these receptors, there are currently no known specific molecular targets for treatment, and although TNBC tumors are chemosensitive, prognosis is poor because this type of cancer relapses more frequently and more aggressively than hormone receptor-positive cancers. The mechanisms by which TNBCs escape control by chemotherapy are not clear, and it is crucial to identify novel molecular drivers that can be targeted in order to develop more efficient therapeutic approaches. We recently highlighted a pleiotropic role for parathyroid hormone-related protein (PTHrP) in all stages of breast cancer, and used our neutralizing anti-PTHrP monoclonal antibody (mAb M158) to efficiently inhibit progression and metastasis of human breast cancer xenografts in athymic mice. In the present study, we present evidence for a strong in vitro anti-proliferative effect of our blocking anti-PTHrP mAb M158 as a single agent on TNBC lines of various subtypes that are known to express PTHrP (MDA-MB-231, BT-549, MDA-MB-435). The same mAb is inactive in a TNBC line without detectable PTHrP expression (MDA-MB-468). In in vitro combination studies, the mAb enhances the effect of the chemotherapeutic drugs taxol and doxorubicin in PTHrP-positive TNBC cells in an additive manner. When combined with the bisphosphonate zoledronate, M158 can act in additive or antagonistic fashion in vitro depending on the cell line. Our observations identify PTHrP as a novel target against TNBC cell proliferation, and suggest that combination therapies that include an anti-PTHrP approach might increase treatment efficacy in patients with PTHrP-positive 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".