Loss of lkb1 Expression Reduces the Latency of ErbB2-Mediated Mammary Gland Tumorigenesis, Promoting Changes in Metabolic Pathways
Bibliographic record
Abstract
The tumor suppressor kinase LKB1 is mutated in a broad range of cancers however, the role of LKB1 mammary gland tumorigenesis is not fully understood. Evaluation of human breast cancer tissue microarrays, indicate that 31% of HER2 positive samples lacked LKB1 expression. To expand on these observations, we crossed STK11 (fl/fl) mice with mice genetically engineered to express activated Neu/HER2-MMTV-Cre (NIC) under the endogenous Erbb2 promoter, to generate STK11 (-/-/) NIC mice. In these mice, the loss of lkb1 expression reduced the latency of ErbB2-mediated tumorigenesis compared to the latency of tumorigenesis in NIC mice alone. Analysis of STK11(-/-/)NIC mammary tumors revealed hyperactivation of mammalian target of rapamycin (mTOR) through both mTORC1 and mTORC2 pathways as determined by the phosphorylation status of ribosomal protein S6 and AKT. Furthermore, STK11(-/-/)NIC mammary tumors had elevated ATP levels along with changes in metabolic enzymes and metabolites. The treatment of primary mammary tumor cells with specific mTOR inhibitors AZD8055 and Torin1, that target both mTOR complexes, attenuated mTOR activity and decreased expression of glycolytic enzymes. Our findings underscore the existence of a molecular interplay between LKB1-AMPK-mTORC1 and ErbB2-AKT-mTORC2 pathways with mTOR at its epicenter, suggestive that loss of LKB1 expression may serve as a marker for hyperactivated mTOR in HER2 positive breast cancer and warranting further investigation into therapeutics that target LKB1-AMPK-mTOR and glycolytic pathways.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".