LKB1 biology: assessing the therapeutic relevancy of LKB1 inhibitors
Bibliographic record
Abstract
Liver Kinase B1 (LKB1), encoded by Serine-Threonine Kinase 11 (STK11), is a master kinase that regulates cell migration, polarity, proliferation, and metabolism through downstream adenosine monophosphate-activated protein kinase (AMPK) and AMPK-related kinase signalling. Since genetic screens identified STK11 mutations in Peutz-Jeghers Syndrome, STK11 mutants have been implicated in tumourigenesis labelling it as a tumour suppressor. In support of this, several compounds reduce tumour burden through upregulating LKB1 signalling, and LKB1-AMPK agonists are cytotoxic to tumour cells. However, in certain contexts, its role in cancer is paradoxical as LKB1 promotes tumour cell survival by mediating resistance against metabolic and oxidative stressors. LKB1 deficiency has also enhanced the selectivity and cytotoxicity of several cancer therapies. Taken together, there is a need to develop LKB1-specific pharmacological compounds, but prior to developing LKB1 inhibitors, further work is needed to understand LKB1 activity and regulation. However, investigating LKB1 activity is strenuous as cell/tissue type, mutations to the LKB1 signalling pathway, STE-20-related kinase adaptor protein (STRAD) binding, Mouse protein 25-STRAD binding, splicing variants, nucleocytoplasmic shuttling, post-translational modifications, and kinase conformation impact the functional status of LKB1. For these reasons, guidelines to standardize experimental strategies to study LKB1 activity, associate proteins, spliced isoforms, post-translational modifications, and regulation are of upmost importance to the development of LKB1-specific therapies. Therefore, to assess the therapeutic relevancy of LKB1 inhibitors, this review summarizes the importance of LKB1 in cell physiology, highlights contributors to LKB1 activation, and outlines the benefits and risks associated with targeting LKB1.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".