eLife Assessment: Netrin signaling mediates survival of dormant epithelial ovarian cancer cells
Bibliographic record
Abstract
Dormancy in cancer is a clinical state in which residual disease remains undetectable for a prolonged duration. At a cellular level, rare cancer cells cease proliferation and survive chemotherapy and disseminate disease. We utilized a suspension culture model of high grade serous ovarian cancer (HGSOC) cell dormancy and devised a novel CRISPR screening approach to identify genetic requirements for cell survival under growth arrested and spheroid culture conditions. In addition, multiple RNA-seq comparisons were used to identify genes whose expression correlates with survival in dormancy. Combined, these approaches discover the Netrin signaling pathway as critical to dormant HGSOC cell survival. We demonstrate that Netrin-1 and -3, UNC5H receptors, DCC and other fibronectin receptors induce low level ERK activation to promote survival in dormant conditions. Furthermore, we determine that Netrin-1 and -3 overexpression is associated with poor prognosis in HGSOC and demonstrate their overexpression elevates cell survival in dormant conditions. Lastly, Netrin-1 or -3 overexpression contributes to greater spread of disease in a xenograft model of abdominal dissemination. This study highlights Netrin signaling as a key mediator HGSOC cancer cell dormancy and metastasis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.013 |
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".