Differential prognostic values of the three AKT isoforms in acute myeloid leukemia
Bibliographic record
Abstract
The PI3K-AKT-mTOR pathway lies at the confluence of signaling pathways in which various components are subjected to activating genetic alterations in acute myeloid leukemia (AML), thus contributing to oncogenesis. Three AKT isoforms exist in humans. However, whether one isoform predominates in AML remains unknown. This study reveals that AKT3 behaves very distinctly than AKT1 or AKT2 in both normal myeloid differentiation and AML. During normal differentiation, AKT3 is preferentially expressed in hematopoietic stem cells whilst AKT1 becomes preferentially expressed as cells differentiate into granulocytes or monocytes. AKT2 expression remains unchanged. In AML, AKT3 expression varies widely among patient samples and is counterintuitively high in mature/monocytic leukemia. Furthermore, a low level of AKT3 expression is strongly correlated to genetic alterations associated with a better outcome (NPM1 mutations and RUNX1-RUNX1T1 translocation), while a high level is correlated to alterations associated to a bad outcome (RUNX1 mutations; and SRSF2, U2AF1, SF3B1, ASXL1 and BCOR mutations occurring frequently in MDS and MPN). Consistently, a high AKT3 expression level appears as a very strong predictor of poor survival. Curiously, although modestly varying among AML samples, a high AKT1 expression shows in contrast as a strong predictor of a better patient outcome. These data suggest that AKT3 and AKT1 expressions have strong, yet opposite, prognostic values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".