Differential sensitivities to lactate transport inhibitors of breast cancer cell lines
Bibliographic record
Abstract
The tumour microenvironment is known to be acidic due to high glycolytic rates of tumour cells. Monocarboxylate transporters (MCTs) play a role in extracellular acidification, which is widely known to be involved in tumour progression. Recently, we have described the upregulation of MCT1 in breast carcinomas and its association with poor prognostic variables. Thus, we aimed to evaluate the effect of lactate transport inhibition in human breast cancer cell lines. The effects of α-cyano-4-hydroxycinnamate, quercetin and lonidamine on cell viability, metabolism, proliferation, apoptosis, migration and invasion were assessed in a panel of different breast cancer cell lines. MCT1, MCT4 and CD147 were differently expressed among the breast cancer cell lines and, as expected, different sensitivities were observed for the three inhibitors. Interestingly, in the most sensitive cell lines, lactate transport inhibition induced a decrease in cell proliferation, migration and invasion, as well as an increase in cell death. Results were validated by silencing MCT1 expression using siRNA. The results obtained here support targeting of lactate transport as a strategy to treat breast cancer, with a special emphasis on the basal-like subtype, which so far does not have a specific molecular therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".