Use of Proton Pump Inhibitors as Adjunct Treatment for Triple-Negative Breast Cancers. An Introductory Study
Bibliographic record
Abstract
PURPOSE: Triple negative breast cancers (estrogen, progesterone and human epidermal growth factor 2 (HER2) receptor-negative) are among the most aggressive forms of cancers with limited treatment options. Doxorubicin is one of the agents found in many of the current cancer treatment protocols, although its use is limited by dose-dependent cardiotoxicity. This work investigates one of the ways to suppress cancer growth by inhibiting tumor cell ability to remove acid accumulated during its metabolism by proton pump inhibitor esomeprazole (a drug with extensive clinical use) which could serve as an addition to doxorubicin therapy. METHODS: In this work, we have investigated growth suppression of triple-negative breast cancer cells MDA-MB-468 by esomeprazole and doxorubicin by trypan blue exclusion assay. Measurement of acidification of treated cancer cells was performed using intracellular pH-sensitive probe, BCECF-AM. Finally, expression of gastric type proton pump (H+/K+ ATPase, a target for esomeprazole) on MDA-MB-468 cells was detected by immunofluorescence and Western blotting. RESULTS: We have found that esomeprazole suppresses growth of triple-negative breast cancer cell in vitro in a dose-dependent manner through increase in their intracellular acidification. In contrast, esomeprazole did not have significant effect on non-cancerous breast epithelial MCF-10A cells. Esomeprazole increases doxorubicin effects suggesting that dual treatments might be possible. In addition, response of MDA-MB-468 cells to esomeprazole could be mediated by gastric type proton pump (H+/K+ ATPase) in cancer cells contrary to previous beliefs that this proton pump expression is restricted to parietal cells of the stomach epithelia. CONCLUSION: This study provides first evidence that adjunct use of esomeprazole in breast cancer treatment might be a possible to combat adverse effects of doxorubicin and increase its effectiveness.
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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 | 0.001 | 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".