Elimination of dormant, autophagic ovarian cancer cells and xenografts through enhanced sensitivity to anaplastic lymphoma kinase inhibition
Bibliographic record
Abstract
BACKGROUND: Poor outcomes for patients with ovarian cancer relate to dormant, drug-resistant cancer cells that survive after primary surgery and chemotherapy. Ovarian cancer (OvCa) cells persist in poorly vascularized scars on the peritoneal surface and depend on autophagy to survive nutrient deprivation. The authors have sought drugs that target autophagic cancer cells selectively to eliminate residual disease. METHODS: By using unbiased small-interfering RNA (siRNA) screens, the authors observed that knockdown of anaplastic lymphoma kinase (ALK) reduced the survival of autophagic OvCa cells. Small-molecule ALK inhibitors were evaluated for their selective toxicity against autophagic OvCa cell lines and xenografts. Autophagy was induced by reexpression of GTP-binding protein Di-Ras3 (DIRAS3) or serum starvation and was evaluated with Western blot analysis, fluorescence imaging, and transmission electron microscopy. Signaling pathways required for crizotinib-induced apoptosis of autophagic cells were explored with flow cytometric analysis, Western blot analysis, short-hairpin RNA knockdown of autophagic proteins, and small-molecule inhibitors of STAT3 and BCL-2. RESULTS: Induction of autophagy by reexpression of DIRAS3 or serum starvation in multiple OvCa cell lines significantly reduced the 50% inhibitory concentration of crizotinib and other ALK inhibitors. In 2 human OvCa xenograft models, the DIRAS3-expressing tumors treated with crizotinib had significantly decreased tumor burden and long-term survival in 67% to 79% of mice. Crizotinib treatment of autophagic cancer cells further enhanced autophagy and induced autophagy-mediated apoptosis by decreasing phosphorylated STAT3 and BCL-2 signaling. CONCLUSIONS: Crizotinib may eliminate dormant, autophagic, drug-resistant OvCa cells that remain after conventional cytoreductive surgery and combination chemotherapy. A clinical trial of ALK inhibitors as maintenance therapy after second-look operations should be seriously considered.
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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) | 0.001 | 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".