Targeting p90 Ribosomal S6 Kinase Eliminates Tumor-Initiating Cells by Inactivating Y-Box Binding Protein-1 in Triple-Negative Breast Cancers
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Bibliographic record
Abstract
Y-box binding protein-1 (YB-1) is the first reported oncogenic transcription factor to induce the tumor-initiating cell (TIC) surface marker CD44 in triple-negative breast cancer (TNBC) cells. In order for CD44 to be induced, YB-1 must be phosphorylated at S102 by p90 ribosomal S6 kinase (RSK). We therefore questioned whether RSK might be a tractable molecular target to eliminate TICs. In support of this idea, injection of MDA-MB-231 cells expressing Flag-YB-1 into mice increased tumor growth as well as enhanced CD44 expression. Despite enrichment for TICs, these cells were sensitive to RSK inhibition when treated ex vivo with BI-D1870. Targeting RSK2 with small interfering RNA (siRNA) or small molecule RSK kinase inhibitors (SL0101 and BI-D1870) blocked TNBC monolayer cell growth by ∼100%. In a diverse panel of breast tumor cell line models RSK2 siRNA predominantly targeted models of TNBC. RSK2 inhibition decreased CD44 promoter activity, CD44 mRNA, protein expression, and mammosphere formation. CD44(+) cells had higher P-RSK(S221/227) , P-YB-1(S102) , and mitotic activity relative to CD44(-) cells. Importantly, RSK2 inhibition specifically suppressed the growth of TICs and triggered cell death. Moreover, silencing RSK2 delayed tumor initiation in mice. In patients, RSK2 mRNA was associated with poor disease-free survival in a cohort of 244 women with breast cancer that had not received adjuvant treatment, and its expression was highest in the basal-like breast cancer subtype. Taking this further, we report that P-RSK(S221/227) is present in primary TNBCs and correlates with P-YB-1(S102) as well as CD44. In conclusion, RSK2 inhibition provides a novel therapeutic avenue for TNBC and holds the promise of eliminating TICs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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