Transfer of malignant traits as opposed to migration of cells: A novel concept to explain metastatic disease
Bibliographic record
Abstract
Metastatic disease is believed to develop following dissemination of cells to target organs. Inability of this theory to effectively explain certain phenomena such as patterns of metastatic spread, late metastasis formation, different gene patterns between primary cancer and metastasis have brought forward the need for alternative models. Recent discoveries have strengthened the validity of theories supporting a humoral transfer of malignant traits as opposed to migration of malignant cells to explain metastatic disease in cancer patients. In light of this new evidence, we would like to highlight a model that offers a new perspective to explain cancer metastasis. In the system that we theorize, genetic material released by cancer cells would travel, either free or packed in exosomes, through the blood. Target cells located in organs deriving from the same embryological layer might uptake this genetic material due to expression of specific receptors. Interplay with the immune system would determine the fate of these oncofactors and would regulate their ability to circulate in the blood, integrate in the genome and be transcribed. We also hypothesize that the expression of cell membrane receptors such as integrins, to which cancer exosomes ligate might be mediated by inherited or acquired oncosuppressor mutations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".