Molecular Pathways: Targeting the Microenvironment of Liver Metastases
Bibliographic record
Abstract
Abstract Curative treatment for metastatic solid cancers remains elusive. The liver, which is nourished by a rich blood supply from both the arterial and portal venous systems, is the most common site of visceral metastases, particularly from cancers arising in the gastrointestinal tract, with colorectal cancer being the predominant primary site in Western countries. A mounting body of evidence suggests that the liver microenvironment (LME) provides autocrine and paracrine signals originating from both parenchymal and nonparenchymal cells that collectively create both pre- and prometastatic niches for the development of hepatic metastases. These resident cells and their molecular mediators represent potential therapeutic targets for the prevention and/or treatment of liver metastases (LM). This review summarizes: (i) the current therapeutic options for treating LM, with a particular focus on colorectal cancer LM; (ii) the role of the LME in LM at each of its phases; (iii) potential targets in the LME identified through preclinical and clinical investigations; and (iv) potential therapeutic approaches for targeting elements of the LME before and/or after the onset of LM as the basis for future clinical trials. Clin Cancer Res; 23(21); 6390–9. ©2017 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".