Targeting hypoxic tumour cells to overcome metastasis
Bibliographic record
Abstract
The microenvironment within solid tumours can influence the metastatic dissemination of tumour cells, and recent evidence suggests that poorly oxygenated (hypoxic) cells in primary tumours can also affect the survival and proliferation of metastatic tumour cells in distant organs. Hypoxic tumour cells have been historically targeted during radiation therapy in attempts to improve loco-regional control rates of primary tumours since hypoxic cells are known to be resistant to ionizing radiation-induced DNA damage. There are, therefore, a number of therapeutic strategies to directly target hypoxic cells in primary (and metastatic) tumours, and several compounds are becoming available to functionally inhibit hypoxia-induced proteins that are known to promote metastasis. This mini-review summarizes several established and emerging experimental strategies to target hypoxic cells in primary tumours with potential clinical application to the treatment of patients with tumour metastases or patients at high risk of developing metastatic disease. Targeting hypoxic tumour cells to reduce metastatic disease represents an important advance in the way scientists and clinicians view the influence of tumour hypoxia on therapeutic outcome.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".