Chronic Inhibition of STAT3/STAT5 in Treatment-Resistant Human Breast Cancer Cell Subtypes: Convergence on the ROS/SUMO Pathway and Its Effects on xCT Expression and System xc- Activity
Bibliographic record
Abstract
Pharmacologically targeting activated STAT3 and/or STAT5 has been an active area of cancer research. The cystine/glutamate antiporter, system xc-, contributes to redox balance and export of intracellularly produced glutamate in response to up-regulated glutaminolysis in cancer cells. We have previously shown that blocking STAT3/5 using the small molecule inhibitor, SH-4-54, which targets the SH2 domains of both proteins, increases xCT expression, thereby increasing system xc- activity in human breast cancer cells. The current investigation demonstrates that chronic SH-4-54 administration, followed by clonal selection of treatment-resistant MDA-MB-231 and T47D breast cancer cells, elicits distinct subtype-dependent effects. xCT mRNA and protein levels, glutamate release, and cystine uptake are decreased relative to untreated passage-matched controls in triple-negative MDA-MB-231 cells, with the inverse occurring in estrogen-responsive T47D cells. This "ying-yang" effect is linked with a shifted balance between the phosphorylation status of STAT3 and STAT5, intracellular ROS levels, and STAT5 SUMOylation/de-SUMOylation. STAT5 emerged as a definitive negative regulator of xCT at the transcriptional level, while STAT3 activation is coupled with increased system xc- activity. We propose that careful classification of a patient's breast cancer subtype is central to effectively targeting STAT3/5 as a therapeutic means of treating breast cancer, particularly given that xCT is emerging as an important biomarker of aggressive cancers.
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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".