CAMKKβ supports growth and viability of epithelial ovarian cancer in vitro and in vivo
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) metastasizes predominantly through multicellular aggregates known as spheroids, which disseminate within the peritoneal cavity and initiate secondary disease upon reattachment at distant sites. EOC spheroids resist detachment-induced cell death by upregulating stress responses including AMP-activated protein kinase (AMPK) signaling and AMPK-dependent macroautophagy (autophagy), highlighting these pathways as potential therapeutic targets. Previously, we used a pharmacological approach to putatively identify Ca 2+ /calmodulin-dependent protein kinase kinase 2 (CAMKKβ, encoded by CAMKK2 ) as the primary activator of AMPK in EOC spheroids. Herein we have generated CAMKK2 knockout EOC cell lines via CRISPR–Cas9 genome editing to confirm this function of CAMKKβ and explore the impacts of its loss using in vitro and in vivo models of metastatic EOC. CAMKK2 knockout spheroids exhibited decreased AMPK activation, autophagic flux, cell viability, and metastatic potential relative to parental spheroids, and intraperitoneal xenograft tumours lacking CAMKKβ grew slower than their CAMKKβ-intact counterparts. Effect magnitudes varied between cell line models, suggesting context-dependent roles for CAMKKβ in EOC and rationalizing further studies to characterize the underlying mechanisms. Altogether, our findings highlight CAMKKβ as an important contributor to metabolic reprogramming in EOC spheroids and as a potential therapeutic target in the setting of advanced disease.
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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".