AKT-mediated phosphorylation of Sox9 induces Sox10 transcription in a murine model of HER2-positive breast cancer.
Bibliographic record
Abstract
Abstract Background: Approximately 5-10% of HER2-positive breast cancers can be defined by low expression of the Ste20-like kinase, SLK, and high expression of SOX10. Our lab has observed that genetic deletion of SLK results in the induction of Sox10 and significantly accelerates tumor initiation in a HER2-induced mammary tumor model. However, the mechanism responsible for the induction of SOX10 gene expression in this context remains unknown. Methods: Using tumor derived cell lines from MMTV-Neu mice lacking SLK and biochemical approaches, we have characterized the signaling mechanisms and relevant DNA elements driving Sox10 expression. Results: Biochemical and genetic analyses of the SOX10 regulatory region in SLK-deficient mammary tumor cells show that Sox10 expression is dependent on a novel -7kb enhancer that harbors three SoxE binding sites. ChIP analyses demonstrate that Sox9 is bound to those elements in vivo . Our data show that AKT can directly phosphorylate Sox9 in vitro at serine 181 and that AKT inhibition blocks Sox9 phosphorylation and Sox10 expression in SLK(-/-) tumor cells. AKT-mediated Sox9 phosphorylation increases its transcriptional activity on the Sox10 -7kb enhancer without altering its DNA binding activity. Interestingly, analysis of murine and human mammary tumors reveals a direct correlation between the levels of active phospho-Sox9 S181 and Sox10 expression. Conclusions: Our results have identified a novel Sox10 enhancer and validated Sox9 as direct target for AKT. As Sox10 is a biomarker for triple negative breast cancers (TNBC), these findings might have major implications in the targeting and treatment of those 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) | 0.001 | 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 | 0.001 |
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".