Loss of Akt1 or Akt2 delays mammary tumor onset and suppresses tumor growth rate in MTB-IGFIR transgenic mice
Bibliographic record
Abstract
BACKGROUND: Akt is a serine/threonine kinase that mediates signaling downstream of tyrosine kinase receptors like the type I insulin-like growth factor receptor (IGF-IR). In fact, we have previously shown that mammary tumors induced by elevated expression of the IGF-IR are associated with hyperactivation of Akt. However, there are three mammalian isoforms of Akt (Akt1, Akt2 and Akt3) and these isoforms regulate distinct physiologic properties within cells. In this manuscript, the impact of disrupting Akt1 or Akt2 in mammary tumors induced by IGF-IR overexpression were examined to determine whether specific Akt isoforms regulate different aspects of mammary tumorigenesis. METHODS: Akt1 and Akt2 levels were stably ablated in mammary tumors of MTB-IGFIR transgenic mice by crossing MTB-IGFIR transgenic mice with either Akt1(-/-) or Akt2(-/-) mice. Tumor onset, growth rate, and metastasis were determined. RESULTS: Ablation of Akt1 or Akt2 significantly delayed tumor onset and tumor growth rate but did not significantly alter lung metastasis. Despite the absence of Akt1 or Akt2, mammary tumors that developed in the MTB-IGFIR mice maintained detectable levels of phosphorylated Akt. Disruption of Akt1 or Akt2 did not affect cell morphology or the expression of luminal or basal cytokeratins in mammary tumors. CONCLUSIONS: Although loss of Akt1 or Akt2 significantly inhibited mammary tumor onset and growth rates the effects were less dramatic than anticipated. Despite the complete loss of Akt1 or Akt2, the level of total phosphorylated Akt remained largely unaffected in the mammary tumors suggesting that loss of one Akt isoform is compensated by enhanced activation of the remaining Akt isoforms. These findings indicate that therapeutic strategies targeting the activation of individual Akt isoforms will prove less effective than simultaneously inhibiting the activity of all three Akt isoforms for the treatment of breast cancer.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".