Functional specificity of Akt isoforms in cancer progression
Bibliographic record
Abstract
Akt/PKB kinases are central mediators of cell homeostasis. There are three highly homologous Akt isoforms, Akt1/PKBα, Akt2/PKBβ and Akt3/PKBγ. Hyperactivation of Akt signaling is a key node in the progression of a variety of human cancer, by modulating tumor growth, chemoresistance and cancer cell migration, invasion and metastasis. It is now clear that, to understand the mechanisms on how Akt affects specific cancer cells, it is necessary to consider the relative importance of each of the three Akt isoforms in the altered cells. Akt1 is involved in tumor growth, cancer cell invasion and chemoresistance and is the predominant altered isoform found in various carcinomas. Akt2 is related to cancer cell invasion, metastasis and survival more than tumor induction. Most of the Akt2 alterations are observed in breast, ovarian, pancreatic and colorectal carcinomas. As Akt3 expression is limited to some tissues, its implication in tumor growth and resistance to drugs mostly occurs in melanomas, gliomas and some breast carcinomas. To explain how Akt isoforms can play different or even opposed roles, three mechanisms have been proposed: tissue-specificity expression/activation of Akt isoforms, distinct effect on same substrate as well as specific localization through the cyto-skeleton network. It is becoming clear that to develop an effective anticancer Akt inhibitor drug, it is necessary to target the specific Akt isoform which promotes the progression of the specific tumor.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".