Leveraging selection for function in tumor evolution: System-level cancer therapies
Bibliographic record
Abstract
Current cancer therapies often fail due to tumor heterogeneity and rapid resistance evolution. A new evolutionary framework, 'selection for function,' proposes that tumor progression is driven by group phenotypic composition (GPC) and its interaction with the microenvironment, not by individual cell traits. This perspective opens new therapeutic avenues: targeting the tumor's functional networks rather than individual cells. Real-time tracking of GPC changes could inform adaptive treatments, delaying progression and resistance. By integrating evolutionary and ecological principles with conventional therapies, this strategy aims to transform cancer from a fatal to a manageable chronic disease. Crucially, it does not necessarily require new drugs but offers a way to repurpose existing therapies to impair a tumor's evolutionary potential. By steering tumor evolution toward less aggressive states, this approach could improve prognosis and long-term patient survival compared to current methods. We argue that leveraging GPC dynamics represents a critical, yet underexplored, opportunity in oncology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".