Targeting Y-box binding protein-1 (YB-1) in Her-2 over-expressing breast cancer cells induces apoptosis via the signal transducer and activator or transcription-3 (STAT3) pathway and suppresses tumor growth.
Bibliographic record
Abstract
AACR Centennial Conference: Translational Cancer Medicine-- July 20-23, 2008; Monterey, CA A14 Her-2 is over-expressed in 20-30% of breast carcinomas, where it is associated with poor outcome and very high rates of relapse. Unfortunately, inhibiting Her-2 itself has been met with limited success in many cases. Perhaps this is because additional molecular targets lay farther downstream of Her-2. The Y-box binding protein-1 (YB-1) is a transcription/translation factor co-expressed in primary breast tumors harboring amplified Her-2. It also induces Her2 itself along with its dimerization partner EGFR by directly binding to their promoters. Therefore we addressed whether tumors cells that over-express Her-2 are dependent upon YB-1 for sustained growth. Small interfering RNAs targeting YB-1 induced apoptosis in BT474-1 and Au565 breast cancer cells known to have Her-2 amplifications. To address the underlying mechanism for YB-1 mediated survival the potential role for STAT3 was pursued. We determined that inhibition of YB-1 decreased P-STAT3S727 and therefore MCL-1. This was accompanied by decreased P-mTORS2448, total mTOR, and P-ERK1/2T202/Y204. Alternatively, constitutively active STAT3 rescued YB-1 induced apoptosis correlative with increased MCL-1. Furthering the role of STAT3 in these cells, we demonstrate that knocking it down recapitulated the induction of apoptosis. Finally, targeting YB-1 with two different siRNA's remarkably suppressed tumor cell growth in soft agar by >90% and delayed tumorigenesis in nude mice. We therefore conclude that Her-2 over-expressing breast cancer cells dependent upon YB-1 for survival suggesting a new therapeutic target.
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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) | 0.002 | 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".