Malignant cancer and invasive placentation: A case for positive pleiotropy between endometrial and malignancy phenotypes
Bibliographic record
Abstract
Cancer metastasis is an invasive process that involves the transplantation of cells into new environments. Since human placentation is also invasive, hypotheses about a relationship between invasive placentation in eutherian mammals and metastasis have been proposed. The relationship between metastatic cancer and invasive placentation is usually presented in terms of antagonistic pleiotropy. According to this hypothesis, evolution of invasive placentation also established the mechanisms for cancer metastasis. Here, in contrast, we argue that the secondary evolution of less invasive placentation in some mammalian lineages may have resulted in positive pleiotropic effects on cancer survival by lowering malignancy rates. These positive pleiotropic effects would manifest themselves as resistance to cancer cell invasion. To provide a preliminary test of this proposal, we re-analyze data from Priester and Mantel (Occurrence of tumors in domestic animals. Data from 12 United States and Canadian colleges of veterinary medicine. J Natl Cancer Inst 1971; 47: :1333-44) about malignancy rates in cows, horses, cats and dogs. From our analysis we found that equines and bovines, animals with less invasive placentation, have lower rates of metastatic cancer than felines and canines in skin and glandular epithelial cancers as well as connective tissue sarcomas. We conclude that a link between type of placentation and species-specific malignancy rates is more likely related to derived mechanisms that suppress invasion rather than different degrees of fetal placental aggressiveness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".