Delayed immune-related adverse events in patients with advanced melanoma treated with immune checkpoint inhibitors.
Bibliographic record
Abstract
12047 Background: Immune checkpoint inhibitors (ICIs) have changed the treatment landscape of melanoma but can have serious or life-altering immune-related adverse events (irAEs). Most toxicities associated with ICIs occur within the first few months (mo) of treatment initiation, but delayed irAEs occurring 12 mo after ICI start are not well characterized. Further, clinical trials often have limited follow-up of AEs and thus, real world data is valuable. Methods: Patients (pts) ≥18 years with advanced melanoma who received ≥1 cycle of an ICI at BC Cancer from 2012-2019 with ≥12 mo of follow-up from time of ICI initiation were identified using the BC Cancer Registry and Pharmacy databases. Data on baseline clinicopathologic factors prior to the first cycle of ICI, and delayed irAE history and management were extracted. Delayed irAEs were graded using CTCAEv5. Results: In total, 530 pts treated with ≥1 cycle of an ICI were identified of which 309 pts had ≥12 mo of follow-up. Median follow-up for alive pts was 65.3 mo (13.0-143.5 mo). 96 (31%), 147 (48%), 19 (6%), and 47 (15%) pts received combination ICI (nivolumab/ipilimumab ± nivolumab maintenance), PD-1 inhibitor monotherapy (nivolumab or pembrolizumab), CTLA-4 inhibitor monotherapy (ipilimumab), and sequential CTLA-4 and PD-1 inhibitors, respectively. A total of 142 delayed irAEs were recorded in 94 (30%) pts. Median time to onset of delayed irAEs was 20.0 mo (12.2-80.6 mo). The most common delayed irAEs were dermatologic (27%), gastrointestinal (23%), and rheumatologic (20%). Most delayed irAEs were grade ≤2 (84%), but some were grade ≥3 (16%). Of individual delayed irAEs, 75 (51%) occurred on ICI treatment at time of onset, 39 (26%) after ≤3 mo of the last ICI cycle, and 34 (23%) after >3 mo of the last ICI cycle. 79 (56%) and 20 (14%) delayed irAEs were treated with systemic steroids and immunomodulators, respectively. Further, 12 (8%) delayed irAEs led to hospitalization, and 64 (45%) required subspecialty consultation. Pts with a history of autoimmune disease were more likely to have a delayed irAE (p=0.02), as were pts who achieved a partial or complete response (p=0.02). There was a trend towards a greater frequency of delayed irAEs in pts treated with PD-1 (37%) compared with combination (28%), CTLA-4 (11%), or sequential CTLA-4 and PD-1 (28%) (p=0.068). Conclusions: Delayed irAEs occurred in almost a third of pts with advanced melanoma treated with ICIs and followed for at least 1 year. Extended follow-up to monitor for delayed irAEs should be considered as many events required immunosuppressive therapy and rarely, may be life threatening. [Table: see text]
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".