eLife assessment: Netrin signaling mediates survival of dormant epithelial ovarian cancer cells
Bibliographic record
Abstract
High-grade serous ovarian cancer (or HGSOC for short) is the fifth leading cause of cancer-related deaths in women. It is generally diagnosed at an advanced stage of disease when the cancer has already spread to other parts of the body. Surgical removal of tumors and subsequent treatment with chemotherapy often reduces the signs and symptoms of the disease for a time but some cancer cells tend to survive so that patients eventually relapse. The HGSOC cells typically spread from the ovaries by moving through the liquid surrounding organs in the abdomen. The cells clump together and enter an inactive state known as dormancy that allows them to survive chemotherapy and low-nutrient conditions. Understanding how to develop new drug therapies that target dormant cancer cells is thought to be an important step in prolonging the life of HGSOC patients. Cancer cells are hardwired to multiply and grow, so Perampalam et al. reasoned that becoming dormant poses challenges for HGSOC cells, which may create unique vulnerabilities not shared by proliferating cancer cells. To find out more, the researchers used HGSOC cells that had been isolated from patients and grown in the laboratory. The team used a gene editing technique to screen HGSOC cells for genes required by the cells to survive when they are dormant. The experiments found that genes involved in a cell signaling pathway, known as Netrin signaling, were critical for the cells to survive. Previous studies have shown that Netrin signaling helps the nervous system form in embryos and inhibits a program of controlled cell death in some cancers. Perampalam et al. discovered that Netrins were present in the environment immediately surrounding dormant HGSOC cells. Human HGSOC patients with higher levels of Netrin gene expression had poorer prognoses than patients with lower levels of Netrin gene expression. Further experiments demonstrated that Netrins help dormant HGSOC cells to spread around the body. These findings suggest that Netrin signalling may provide useful targets for future drug therapies against dormant cells in some ovarian cancers. This could include repurposing drugs already in development or creating new inhibitors of this pathway.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.015 |
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".