Liver metastasis and resistance to immunotherapy in microsatellite stable colorectal cancer. A literature review
Bibliographic record
Abstract
Background: Microsatellite stable (MSS) metastatic colorectal cancer (CRC) remains predominantly managed with chemotherapy. The use of immunotherapy, whether alone or in combination with other systemic or local treatments, displays limited success, especially in the context of active liver metastases (LM). The mechanisms responsible for this resistance are not fully understood. Methods: We conducted a comprehensive search across electronic databases such as Medline, PubMed, Google Scholar and ScienceDirect. This search targeted translational studies evaluating the liver tumour immune microenvironment and immune tolerance mechanisms in CRC with LM and prospective studies that assessed immunotherapy either as a standalone treatment or in combination with other systemic or local therapies for patients diagnosed with MSS CRC. Our primary objectives included elucidating the mechanisms of resistance originating from LM in a non-systematic literature review and presenting a summary of the outcomes observed in prospective trials utilising immune checkpoint inhibitors (ICIs), with a focus on the presence of LM. Findings: There were 16 prospective trials evaluating immunotherapy for metastatic CRC comprising 1,713 patients. Response rates to immunotherapy inpatients with colorectal liver metastases (CRLM) varied from 0% to 23%. Overall, reduced or null responses to immunotherapy in the presence of liver metastasis in comparison to patients without liver involvement were observed. Conclusion: Studies consistently show the resistance derived from classical ICI, both alone and in combination with other systemic treatments in patients with CRLM. The design of upcoming trials using immunotherapy should consider LM as a stratification factor or contemplate excluding patients with liver involvement.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".