The association of pre-existing autoimmune disease and immune-related adverse events secondary to immune checkpoint inhibition therapy in a UK multicenter cohort.
Bibliographic record
Abstract
2522 Background: Pre-existing autoimmune disease (AID) potentially increases the propensity for the development of immune related adverse events (irAE) in response to oncological immune checkpoint inhibitors (ICIs) is biologically plausible and clinically observed. However, due to consistent clinical trial exclusion of those with pre-existing AID, the impact on the frequency and severity of irAEs is uncertain. Here we analyse this relationship in a large, real-world, UK multi-centre cohort. Methods: A retrospective analysis of 2049 patients treated with ICIs over a two year period was undertaken across 12 National Health Service centres by the UK National Oncology Trainees Collaborative for Healthcare Research (NOTCH). Patients received ICIs as standard of care for malignant melanoma, non-small cell lung cancer and renal cell carcinoma. The presence of pre-existing AIDs was assessed and classified as either autoantibody driven or autoinflammatory then correlated with clinically significant irAEs (i.e. ≥grade 2 or all-grade endocrinopathies). Statistical analyses included T-test, Mann-Whitney and Chi-squared. For overall survival (OS) Kaplan-Meier and log-rank tests were utilised. Results: Pre-existing AID were present in 13% (n = 257) of the overall cohort. Pre-existing endocrinopathies (30%; n = 76) were most common followed by rheumatological AIDs (18%; n = 46). In the pre-existing AID cohort there was a female predominance (48% vs 39%; p = 0.006) but no difference in smoking history (p = 0.074) or ethnicity (p = 0.12). There was no difference in ICI treatment between those with and without pre-existing AID (p = 0.2800). IrAEs occurred in 45% (n = 117) patients with pre-existing AID vs 33% (n = 583) without (p£0.001). The median time to onset of irAEs was similar. IrAEs with an increased incidence in the pre-existing AID cohort were colitis (p = < 0.001), arthralgia (p = 0.008) and dermatological irAEs (p = 0.014). There was no difference in the incidence of irAEs in patients with autoantibody driven vs autoinflammatory pre-existing AID (44.0 % vs 44.8%, p = 0.905). In the overall cohort, those with pre-existing AIDs had a median OS of 20.4 months (95% CI: 19.4-21.7) vs 14.1 months (95% CI: 12.8-16.3) in those without pre-existing AID (p = 0.004). Conclusions: This large multi-centre ICI-treated cohort demonstrates that pre-existing AID is a predisposing factor for the development of irAEs, however the incidence is lower than previously quoted. The pathological basis of pre-existing AID did not differentially affect irAE manifestation. Patients with pre-existing AID had improved OS compared to those without which has not been observed in previously reported studies. ICI treatment should be considered in those with pre-existing AID but further studies are needed to determine how best to optimise outcomes whilst mitigating the impact of irAEs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".