Immune Checkpoint Inhibitors Beyond Progression in Various Solid Tumors: A Systematic Review and Pooled Analysis
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors (ICIs) have transformed outcomes in advanced cancers; however, the value of continuing treatment after radiologic progression remains uncertain. We systematically assessed the efficacy and safety of ICI continuation beyond progression, focusing on the objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Methods: PubMed/MEDLINE, Embase, and the Cochrane Library were searched from inception to 31 March 2025. Eligible reports included retrospective cohorts, prospective trials, post hoc analyses, and pooled regulatory reviews that compared outcomes after ICI continuation versus discontinuation or historical controls. Quality was appraised with the Newcastle–Ottawa Scale (observational designs) and the Cochrane Risk-of-Bias tool (randomized trials). Results: Fifty studies involving 8989 patients met the inclusion criteria: 41 retrospective cohorts; 6 post hoc analyses; 2 randomized trials (1 phase III, 1 phase II); and 1 pooled FDA review. Continuing ICIs beyond progression produced ORRs of 9.3–39% in non-small cell lung cancer (n = 5102), 14–100% in melanoma (n = 669), and 8–33% in renal cell carcinoma (n = 458). Median OS ranged from 8.9 to 18.2 months in lung cancer, 12 to 29.9 months in melanoma, and up to 34.8 months in RCC. Modest but clinically meaningful benefits were reported in colorectal, head-and-neck, gastric, liver, and urothelial tumors. Conclusions: Select patients—particularly those with melanoma, lung cancer, RCC, or gastric cancer—may derive sustained benefit from ICI therapy after radiologic progression. Decisions should incorporate tumor biology, performance status, and emerging biomarkers. Prospective, biomarker-driven trials are needed to define optimal patient selection and the duration of post-progression immunotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.024 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".