Retreatment, rechallenge, and escalation with subsequent immune checkpoint inhibitor therapies across cancers after initial failure
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors (ICIs) are used across many tumor types in perioperative and advanced settings. However, most patients discontinue treatment due to disease progression, adverse events, or other reasons. Clinical benefit of using ICIs following discontinuation is not well defined. METHODS: We analyzed the literature examining ICI treatment outcomes after progression or discontinuation in different tumor types. We extracted data from 51 studies, assessed the strength of the evidence, summarized treatment options, and identified gaps in our understanding. RESULTS: We proposed definitions for the different scenarios of subsequent treatment with ICIs. In melanoma, studies frequently reported complete response (CR), partial response (PR), and stable disease (SD) following subsequent ICI therapy. In renal cell carcinoma, discordant results have been reported following subsequent ICI treatment; some trials reported CR/PR cases, whereas others did not show any CR/PR. In non-small-cell lung cancer, we found frequent reports of PR or SD but not CR following subsequent ICI treatment, although studies had small patient cohorts. Subsequent ICI treatment showed efficacy in some patients with urothelial carcinoma, but the small cohort sizes limited the strength of the evidence. One cross-tumor study investigated subsequent ICI treatment after initial discontinuation and reported a few PR and SD without any CR. CONCLUSIONS: Evidence supporting the efficacy of subsequent ICI treatment is strongest in melanoma, but the level of evidence remains low overall. Prospective studies and improved reporting of subsequent ICI therapy in existing trials investigating long-term outcomes, standardized predictive factors, and treatment modalities are warranted.
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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.004 | 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".