Repression of LKB1 by <i>miR-17∼92</i> sensitizes <i>MYC</i> -dependent lymphoma to biguanide treatment
Bibliographic record
Abstract
Abstract Cancer cells display metabolic plasticity to survive metabolic and energetic stresses in the tumor microenvironment, prompting the need for tools to target tumor metabolism. Cellular adaptation to energetic stress is coordinated in part by signaling through the Liver Kinase B1 (LKB1)-AMP-activated protein kinase (AMPK) pathway. Reducing LKB1-AMPK signaling exposes metabolic vulnerabilities in tumor cells with potential for therapeutic targeting. Here we describe that miRNA-mediated silencing of LKB1 (mediated by the oncogenic miRNA cluster miR-17∼92 ) confers sensitivity of lymphoma cells to mitochondrial inhibition by biguanides. Using both classic (phenformin) and novel (IM156) biguanides, we demonstrate that Myc + lymphoma cells with elevated miR-17∼92 expression display increased sensitivity to biguanide treatment both in cell viability assays in vitro and tumor growth assays in vivo . This increased biguanide sensitivity is driven by miR-17 -dependent silencing of LKB1, which results in reduced AMPK activation in response to bioenergetic stress. Mechanistically, biguanide treatment inhibits TCA cycle metabolism and mitochondrial respiration in miR-17∼92 -expressing tumor cells, targeting their metabolic vulnerability. Finally, we demonstrate a direct correlation between miR-17∼92 expression and biguanide sensitivity in human cancer cells. Our results identify miR-17∼92 expression as a potential biomarker for biguanide sensitivity in hematological malignancies and solid tumors. One Sentence Summary miR-17∼92 expression in Myc + tumors sensitizes cancer cells to biguanide treatment by disrupting bioenergetic stability in lymphoma cells.
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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) | 0.000 | 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) | 0.001 | 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 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".