Insights into targeting LKB1 in tumorigenesis
Bibliographic record
Abstract
Genetic alterations to serine-threonine kinase 11 ( STK11 ) have been implicated in Peutz-Jeghers syndrome and tumorigenesis. Further exploration of the context-specific roles of liver kinase B1 (LKB1; encoded by STK11 ) observed that it regulates AMP-activated protein kinase (AMPK) and AMPK-related kinases. Given that both migration and proliferation are enhanced with the loss of LKB1 activity combined with the prevalence of STK11 genetic alterations in cancer biopsies, LKB1 was marked as a tumor suppressor. However, the role of LKB1 in tumorigenesis is paradoxical as LKB1 activates autophagy and reactive oxygen species scavenging while dampening anoikis, which contribute to cancer cell survival. Due to the pro-tumorigenic properties of LKB1, targeting LKB1 pathways is now relevant for cancer treatment. With the recent successes of targeting LKB1 signaling in research and clinical settings, and enhanced cytotoxicity of chemical compounds in LKB1-deficient tumors, there is now a need for LKB1 inhibitors. However, validating LKB1 inhibitors is challenging as LKB1 adaptor proteins, nucleocytoplasmic shuttling, and splice variants all manipulate LKB1 activity. Furthermore, STE-20-related kinase adaptor protein (STRAD) and mouse protein 25 dictate LKB1 cellular localization and kinase activity. For these reasons, prior to assessing the efficacy and potency of pharmacological candidates, the functional status of LKB1 needs to be defined. Therefore, to improve the understanding of LKB1 in physiology and oncology, this review highlights the role of LKB1 in tumorigenesis and addresses the therapeutic relevancy of LKB1 inhibitors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.000 | 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 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".