Targeting Liver Metastases to Potentiate Immunotherapy In MS-stable colorectal cancer- A Scoping Review of Literature
Bibliographic record
Abstract
Abstract While Immunotherapy has revolutionized the treatment of several cancers such as lung cancer, melanoma, and other cancers, most colorectal cancer patients remain resistant. This resistance to immunotherapy may partially stem from the fact that colorectal cancer very commonly metastasizes to the liver. The liver is known to play an immunotolerant role in in other contexts such as organ transplantation, viral disease, and autoimmune disease. Recent studies reveal the mechanisms in which liver metastases restrict the efficacy of immunotherapy. This effect was shown to be reversable in colorectal cancer mice models, when colorectal liver metastases were irradiated. It is possible that targeting liver metastases with locoregional therapies such as ablation, resection or irradiation may reverse the immunosuppressive effect of liver microenvironment and potentiate immunotherapy systemically. During the past decade, several clinical trials are trying to extrapolate the results achieved in animal model to clinical trials by combining immunotherapy with locoregional therapy. In this scoping review, the current clinical and translational literature was surveyed, to determine whether there is evidence to support the validity of this concept in human patients. If indeed immunotherapy can be potentiated for MS- Stable colorectal cancer utilizing locoregional interventions, a wide array of innovative protocols can be utilized to help cancer patients who have no other available treatment options and thus revolutionize the treatment of cancer patients with liver metastases.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".