Targeting YB-1 in HER-2 Overexpressing Breast Cancer Cells Induces Apoptosis via the mTOR/STAT3 Pathway and Suppresses Tumor Growth in Mice
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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".