Unique roles of<i>Akt1</i>and<i>Akt2</i>in IGF-IR mediated lung tumorigenesis
Bibliographic record
Abstract
// S. Elizabeth Franks 1 , Ritesh Briah 1 , Robert A. Jones 1 , Roger A. Moorehead 1 1 Department of Biomedical Science, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada Correspondence to: Roger A. Moorehead, e-mail: rmoorehe@uoguelph.ca Keywords: Akt, lung cancer, IGF-IR, transgenic mice, tumor development Received: July 27, 2015 Accepted: November 16, 2015 Published: December 07, 2015 ABSTRACT AKT is a serine-threonine kinase that becomes hyperactivated in a number of cancers including lung cancer. Based on AKT's association with malignancy, molecules targeting AKT have entered clinical trials for solid tumors including lung cancer. However, the AKT inhibitors being evaluated in clinical trials indiscriminately inhibit all three AKT isoforms (AKT1–3) and it remains unclear whether AKT isoforms have overlapping or divergent functions. Using a transgenic mouse model where IGF-IR overexpression drives lung tumorigenesis, we found that loss of Akt1 inhibited while loss of Akt2 enhanced lung tumor development. Lung tumors that developed in the absence of Akt2 were less likely to appear as discrete nodules and more frequently displayed a dispersed growth pattern. RNA sequencing revealed a number of genes differentially expressed in lung tumors lacking Akt2 and five of these genes, Actc1 , Bpifa1, Mmp2, Ntrk2 , and Scgb3a2 have been implicated in human lung cancer. Using 2 human lung cancer cell lines, we observed that a selective AKT1 inhibitor, A-674563, was a more potent regulator of cell survival than the pan-AKT inhibitor, MK-2206. This study suggests that compounds selectively targeting AKT1 may prove more effective than compounds that inhibit all three AKT isoforms at least in the treatment of lung adenocarcinoma.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".