Role of the Microenvironment in Liver Metastasis: From Pre- to Prometastatic Niches
Bibliographic record
Abstract
Liver metastases remain a major barrier to successful management of malignant disease, particularly for cancers of the gastrointestinal tract but also for other malignancies, such as breast carcinoma and melanoma. The ability of metastatic cells to survive and proliferate in the liver is determined by the outcome of complex, reciprocal interactions between tumor cells and different local resident subpopulations, including the sinusoidal endothelium, stellate, Kupffer, and inflammatory cells that are mediated through cell-cell and cell-extracellular matrix adhesion and the release of soluble factors. Cross-communication between different hepatic resident cells in response to local tissue damage and inflammation and the recruitment of bone marrow cells further enhance this intercellular communication network. Both resident and recruited cells can play opposing roles in the progression of metastasis, and the balance of these divergent effects determines whether the tumor cells will die, proliferate, and colonize the new site or enter a state of dormancy. Moreover, this delicate balance can be tilted in favor of metastasis, if factors produced by the primary tumor precondition the microenvironment to form niches of activated resident cells that promote tumor expansion. This review aims to summarize current knowledge on these diverse interactions and the impact they can have on the clinical management of hepatic metastases. Clin Cancer Res; 22(24); 5971-82. ©2016 AACR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".