De novo evolution of transmissible tumours in hydra
Bibliographic record
Abstract
While most cancers are not transmissible, there are rare cases where cancer cells can spread between individuals and even across species, leading to epidemics. Despite their significance, the origins of such cancers remain elusive due to late detection in host populations. Using Hydra oligactis , which exhibits spontaneous tumour development that in some strains became vertically transmitted, this study presents the first experimental observation of the evolution of a transmissible tumour. Specifically, we assessed the initial vertical transmission rate of spontaneous tumours and explored the potential for optimizing this rate through artificial selection. One of the hydra strains, which evolved transmissible tumours over five generations, was characterized by analysis of cell type and bacteriome, and assessment of life-history traits. Our findings indicate that tumour transmission can be immediate for some strains and can be enhanced by selection. The resulting tumours are characterized by overproliferation of large interstitial stem cells and are not associated with a specific bacteriome. Furthermore, despite only five generations of transmission, these tumours induced notable alterations in host life-history traits, hinting at a compensatory response. This work, therefore, makes the first contribution to understanding the conditions of transmissible cancer emergence and their short-term consequences for the host.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".