Unraveling the relationship between cancer and life history traits in vertebrates
Bibliographic record
Abstract
ABSTRACT Identifying species with unusually low cancer prevalence can provide new insights into cancer resistance. Most studies have focused on mammals, but the genetic, physiological, and ecological diversity among vertebrates can influence cancer susceptibility. We used necropsies from over a thousand species of amphibians, birds, crocodilians, mammals, squamates, and turtles to investigate relationships between cancer prevalence, intrinsic cancer risk, body mass, and lifespan. Previous studies often relied on species averages, leading to inaccurate interpretations. Our innovative statistical approach uses raw cancer data and resampling to improve accuracy. We found remarkably low cancer prevalence in turtles, high prevalence in squamates and mammals, and lower-than-expected prevalence based on lifespan and body mass in multiple groups. Our results show lifespan influences neoplasia and malignancy transformation rates in mammals, while body mass affects neoplasia prevalence in amphibians and squamates. These data reveal a complex relationship between life history traits and cancer risk, identifying vertebrates with potential novel cancer resistance mechanisms. STATEMENT OF SIGNIFICANCE Biodiversity is an untapped natural resource for understanding cancer. Our study reveals a wide divergence in cancer prevalence among vertebrate groups, with notably low rates in turtles and high rates in squamates and mammals. These findings can lead to new breakthroughs in understanding the biology of cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".