The AMPK-Related Kinases SIK1 and SIK3 Mediate Key Tumor-Suppressive Effects of LKB1 in NSCLC
Bibliographic record
Abstract
Abstract Mutations in the LKB1 (also known as STK11) tumor suppressor are the third most frequent genetic alteration in non–small cell lung cancer (NSCLC). LKB1 encodes a serine/threonine kinase that directly phosphorylates and activates 14 AMPK family kinases (“AMPKRs”). The function of many of the AMPKRs remains obscure, and which are most critical to the tumor-suppressive function of LKB1 remains unknown. Here, we combine CRISPR and genetic analysis of the AMPKR family in NSCLC cell lines and mouse models, revealing a surprising critical role for the SIK subfamily. Conditional genetic loss of Sik1 revealed increased tumor growth in mouse models of Kras-dependent lung cancer, which was further enhanced by loss of the related kinase Sik3. As most known substrates of the SIKs control transcription, gene-expression analysis was performed, revealing upregulation of AP1 and IL6 signaling in common between LKB1- and SIK1/3-deficient tumors. The SIK substrate CRTC2 was required for this effect, as well as for proliferation benefits from SIK loss. Significance: The tumor suppressor LKB1/STK11 encodes a serine/threonine kinase frequently inactivated in NSCLC. LKB1 activates 14 downstream kinases in the AMPK family controlling growth and metabolism, although which kinases are critical for LKB1 tumor-suppressor function has remained an enigma. Here we unexpectedly found that two understudied kinases, SIK1 and SIK3, are critical targets in lung cancer. This article is highlighted in the In This Issue feature, p. 1469
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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".