Coagulome and tumor microenvironment: impact of oncogenes, cellular heterogeneity and extracellular vesicles
Bibliographic record
Abstract
Cancer-associated thrombosis (CAT) results from the hemostatic system being dysregulated by the progression of cancer. Despite common clinical manifestations, the mechanisms of CAT may vary greatly because cancers develop along distinct biological trajectories that are imposed by the interaction between the tumor cell genome, the epigenome, the surrounding microenvironment, and the tissue of origin. The coagulome, or repertoire of coagulation effectors, expressed by stromal, inflammatory, and cancer cells at the tumor-vascular interface and systemically, reflects this biological variability. Complex landscapes of coagulant and non-coagulant cellular populations are revealed by single-cell RNA sequencing analyses conducted on unperturbed human cancer tissues. Additionally, through mediators of cell-cell interactions, soluble coagulants, and extracellular vesicles containing tissue factor, podoplanin, and other effectors, coagulomes are projected into the pericellular milieu and systemic circulation. As this complexity is currently outside of the clinical paradigm, one could argue that better CAT management could result from a more individualized analysis of coagulomes in cancer patients.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".