Chromosomal Instability and mTORC1 Activation through PTEN Loss Contribute to Proteotoxic Stress in Ovarian Carcinoma
Bibliographic record
Abstract
Abstract High-grade serous ovarian carcinoma commonly arises from fallopian tube secretory epithelium and is characterized by a high level of chromosomal instability. To model the acquisition of aneuploidy during early carcinogenesis, chromosome missegregation was induced in immortalized tubal epithelial cells, which proved acutely detrimental to cellular fitness. The phenotype was characterized by accumulation of misfolded proteins, activation of the unfolded protein response (UPR), decreased protein synthesis, and enhanced vulnerability to proteasome inhibition. However, chromosome missegregation also resulted in heightened transformation potential, assessed by colony formation in soft agar. Ovarian cancer cells retained intrinsic sensitivity to proteasome inhibitors under adherent culture conditions, but acquired resistance as spheroids (recapitulating their native configuration in ascites) by downregulating protein synthesis via mTORC1 suppression. Loss of PTEN drove constitutive mTORC1 activity, enhanced proteotoxic stress, as evidenced by UPR induction, and resensitized tumor spheroids to proteasome inhibition both in vitro and in vivo. In cohorts of primary ovarian carcinomas, mTORC1 and UPR signaling pathways were closely associated. These results implicate attenuation of protein synthesis as a protective mechanism in tumor spheroids, which may explain the overall poor response to bortezomib in clinical trials of patients with advanced ovarian cancer. However, patients with PTEN-deficient tumors may represent a subpopulation potentially amenable to treatment with proteasome inhibitors or other therapeutic agents that disrupt protein homeostasis. Significance: Chromosome instability and protein synthesis are important factors that determine the efficacy of proteotoxic stress-inducing agents, such as proteasome inhibitors, in the treatment of ovarian cancer.
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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".