Targeting YB-1 in HER-2 Overexpressing Breast Cancer Cells Induces Apoptosis via the mTOR/STAT3 Pathway and Suppresses Tumor Growth in Mice
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Bibliographic record
Abstract
The Y-box binding protein-1 (YB-1) is a transcription/translation factor that is highly expressed in primary breast tumors where it is consistently associated with poor survival. It induces human epidermal growth factor receptor (her-2) along with its dimerization partner egfr by directly binding to their promoters. In addition to promoting growth by inducing receptor tyrosine kinases, YB-1 also protects cells against apoptosis through mechanisms that have not been fully revealed. Given this, we addressed whether YB-1 might be an eventual therapeutic target for breast cancer by inhibiting it with small interfering RNAs in vitro and in vivo. Inhibiting YB-1 suppressed the growth of six of seven breast cancer cell lines that had amplified her-2 or were triple negative. Importantly, targeting YB-1 induced apoptosis in BT474-m1 and Au565 breast cancer cells known to have her-2 amplifications. The potential role of signal transducers and activators of transcription 3 (STAT3) was pursued to address the underlying mechanism for YB-1-mediated survival. Inhibition of YB-1 decreased P-STAT3(S727) but not P-STAT3(Y705) or total STAT3. This was accompanied by decreased P-ERK1/2(T202/Y204), P-mTOR(S2448), and total mammalian target of rapamycin mTOR. Furthering the role of STAT3 in these cells, we show that knocking it down recapitulated the induction of apoptosis. Alternatively, constitutively active P-STAT3 rescued YB-1-induced apoptosis. Finally, targeting YB-1 with 2 different siRNAs remarkably suppressed tumor cell growth in soft agar by >90% and delayed tumorigenesis in nude mice. We conclude that HER-2 overexpressing as well as triple-negative breast cancer cells are YB-1 dependent, suggesting it may be a good therapeutic target for these exceptionally aggressive tumors.
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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 | 0.001 |
| 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 | 0.000 |
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 it