The Tumor Suppressor Kinase LKB1: Metabolic Nexus
Bibliographic record
Abstract
Liver kinase B1 (LKB1) is a multitasking tumor suppressor kinase that is implicated in multiple malignancies such as lung, gastrointestinal, pancreatic, and breast. LKB1 was first identified as the gene responsible for Peutz-Jeghers syndrome (PJS) characterized by hamartomatous polyps and oral mucotaneous pigmentation. LKB1 functions to activate AMP-activated protein kinase (AMPK) during energy stress to shift metabolic processes from active anabolic pathways to active catabolic pathways to generate ATP. Genetic loss or inactivation of LKB1 promotes metabolic reprogramming and metabolic adaptations of cancer cells that fuel increased growth and division rates. As a result, LKB1 loss is associated with increased aggressiveness and treatment options for patients with LKB1 mutant tumors are limited. Recently, there has been new insights into the role LKB1 has on metabolic regulation and the identification of potential vulnerabilities in LKB1 mutant tumors. In this review, we discuss the tumor suppressive role of LKB1 and the impact LKB1 loss has on metabolic reprograming in cancer cells, with a focus on lung cancer. We also discuss potential therapeutic avenues to treat malignancies associated with LKB1 loss by targeting aberrant metabolic pathways associated with LKB1 loss.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".