Cancer Prevention Strategies That Address the Evolutionary Dynamics of Neoplastic Cells: Simulating Benign Cell Boosters and Selection for Chemosensitivity
Bibliographic record
Abstract
Cells in neoplasms evolve by natural selection. Traditional cytotoxic chemotherapies add further selection pressure to the evolution of neoplastic cells, thereby selecting for cells resistant to the therapies. An alternative proposal is a benign cell booster. Rather than trying to kill the highly dysplastic or malignant cells directly, a benign cell booster increases the fitness of the more benign cells, which may be either normal or benign clones, so that they may outcompete more advanced or malignant cells in a neoplasm. In silico simulations of benign cell boosters in neoplasms with evolving clones show benign cell boosters to be effective at destroying advanced or malignant cells and preventing relapse even when applied late in progression. These results are conditional on the benign cell boosters giving a competitive advantage to the benign cells in the neoplasm. Furthermore, the benign cell boosters must be applied over a long period of time in order for the benign cells to drive the dysplastic cells to extinction or near extinction. Most importantly, benign cell boosters based on this strategy must target a characteristic of the benign cells that is causally related to the benign state to avoid relapse. Another promising strategy is to boost cells that are sensitive to a cytotoxin, thereby selecting for chemosensitive cells, and then apply the toxin. Effective therapeutic and prevention strategies will have to alter the competitive dynamics of a neoplasm to counter progression toward invasion, metastasis, and death.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".