Genetic instability of <i>RBM5/LUCA‐15/H37</i> in MCF‐7 breast carcinoma sublines may affect susceptibility to apoptosis
Bibliographic record
Abstract
The MCF-7 human breast carcinoma cell line is widely used as a model system by breast cancer researchers and cell biologists investigating apoptosis. Since its establishment 30 years ago, from a patient with metastatic breast cancer, the original MCF-7 cell population has undergone genetic drift to such an extent that numerous genetically diverse sublines now exist. For instance, it has been reported that MCF-7 cells have lost the region 3p21.3, to which the apoptosis regulatory protein and putative tumour suppressor LUCA-15 (also called RBM5 and H37) maps; however, LUCA-15 has been cloned from MCF-7 cells, and LUCA-15 expression analyses have been conducted using MCF-7 cells. To address this discrepancy, we characterized three MCF-7 sublines by Western blot, RT-PCR and finally genomic PCR analysis, and determined that one of the three had lost the LUCA-15 gene. Interestingly, loss of LUCA-15 was positively correlated with decreased susceptibility to the death-inducing ligand TNF-alpha. Subsequent overexpression of exogenous LUCA-15 was shown to enhance TNF-alpha-mediated apoptosis, suggesting that LUCA-15 may play a role in regulating the susceptibility of breast cancer cells to drug-induced apoptosis. These results not only reinforce the necessity of MCF-7 subline characterization, but provide the first evidence of an apoptotic modulatory role for LUCA-15 in a non-T cell line.
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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) | 0.001 | 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".