Safety of Immunotherapy Rechallenge After Immune-related Adverse Events in Patients With Advanced Cancer
Bibliographic record
Abstract
This retrospective study aimed to investigate the safety profile of continuing or rechallenging patients with advanced cancer who developed grade≥2 immune-related adverse events (irAEs) on immunotherapy-based regimens. Our study had 25, 20, and 40 patients (N=85) in the Treatment Continuation (TCG), Non-Rechallenge (NRG), and Rechallenge Groups (RG), respectively. Subsequent irAEs recurrence were more common in RG than TCG and NRG (78% vs. 56% vs. 25%, P<0.001). The same subsequent irAEs recurrences occurred on 42% of RG, 4% of TCG, and 15% of NRG (P<0.001). On the RG, there was a nonstatistical trend of shortening interval time between time from treatment rechallenge to subsequent irAEs when compared with time from first treatment to initial grade≥2 irAEs (5.86 vs. 8.86 wk, P=0.114). Patients who had cardiac irAEs were not rechallenged. Several high-risk features were identified to prognosticate risk of irAEs recurrences upon treatment rechallenge, including age 65 years and above (P=0.007), programmed cell death protein 1 inhibitors (P<0.001), grade 3 irAEs (P=0.003), pneumonitis type (P=0.048), any systemic corticosteroid use (P=0.001)/high-dose systemic corticosteroid use (P=0.007)/prolonged ≥4-week corticosteroid use (P=0.001) for irAEs management, and early development of irAEs (P=0.003). Our study concluded that it was relatively safe to continue or rechallenge patients with advanced cancers on immunotherapy-based regimens postdevelopment of certain grade≥2 irAEs, except for cardiac, neurological, or any grade 4 irAEs. Subsequent irAEs were common, no more severe, involved the same organ sites, and occurred more quickly than the original irAE. Close monitoring of all potential irAEs is required when rechallenging a patient on immunotherapy, especially for patients with high-risk features.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".