Effectiveness and Safety of Immune Checkpoint Inhibitors in Colorectal Cancer: A Systematic Review of Real-World Studies
Bibliographic record
Abstract
PURPOSE OF REVIEW: Immune checkpoint inhibitors (ICIs) have demonstrated significant efficacy in the treatment of colorectal cancer (CRC). However, most evidence has come from clinical trials with strict eligibility criteria. Understanding real-world effectiveness and safety of ICIs in CRC is important to guide routine clinical practice across diverse populations. RECENT FINDINGS: A systematic review following PRISMA guidelines was conducted to identify observational studies evaluating ICI-based regimens compared to conventional or combination therapies in patients with CRC. Three databases (MEDLINE, Embase, and Scopus) were searched from inception through March 15, 2025. Eligible studies reported at least one efficacy outcome (e.g., progression-free survival [PFS], overall survival [OS], etc.) and/or safety outcome (e.g., adverse events) among real-world populations with CRC treated with ICIs. Study quality was assessed using the Newcastle-Ottawa Scale, and a narrative synthesis was performed to summarize the key findings. Eleven real-world studies met the inclusion criteria, encompassing data from 2,049 patients. In MSI-H/dMMR metastatic CRC, real-world findings aligned with the survival benefits observed in clinical trials, demonstrating improved PFS and OS compared to conventional therapies. For MSS/pMMR metastatic CRC, combining ICIs with other agents (e.g., tyrosine kinase inhibitors or chemotherapy) showed improvements but yielded conflicting results. Overall, the safety profiles were comparable to conventional therapies, with treatment-related adverse events occurring at similar rates. Real-world evidence supports the efficacy of ICI monotherapy in MSI-H/dMMR metastatic CRC and suggests potential benefits of ICI-based combination therapies in MSS/pMMR metastatic CRC. However, most of the data are derived from small, single-center cohorts, which limit their generalizability. Further multi-center studies are needed, especially to assess the efficacy of ICI-based combination therapies in the broader CRC population.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".