Effectiveness and Safety of Immune Checkpoint Inhibitors in Cancer Patients With Autoimmune Disease: A Retrospective Cohort Study
Bibliographic record
Abstract
Although immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, patients with pre-existing autoimmune diseases (PADs) have largely been excluded from clinical trials evaluating this drug class. This study evaluates the effectiveness and safety of ICI therapy in individuals with PAD in a real-world setting. A retrospective study of patients exposed to ICI therapy between 2012 and 2019 was conducted. Patients with PAD were identified and matched to an ICI-exposed group without PAD based on age, sex, and cancer type. Primary outcomes included toxicity, time to treatment failure, overall survival, and objective response rate. The association between PAD status and outcomes was determined using Cox and logistic regression modeling. A total of 813 patients exposed to ICI therapy were identified, of which 8.2% (N=67) had a PAD. When compared with a matched cohort without PAD (N=132), there was no significant difference in the rates of new immune-related adverse events (irAEs, 42.4% in the non-PAD group vs. 47.8% in the PAD group, P=0.474). After controlling for the type of ICI, there was no significant association between PAD status and irAE (odds ratio 1.67, 95% CI: 0.9-3.21 P=0.1). There was no significant association between overall survival and PAD status (hazard ratio 1.12, 95% CI: 0.76-1.66. P=0.56) or between time to treatment failure and PAD status (hazard ratio 0.82, 95% CI: 0.6-1.12, P=0.22). There was an association between PAD status and objective response rate (odds ratio 3.28, 95% CI: 1.28-8.38, P=0.013). In summary, PAD status was not associated with enhanced toxicity when compared with patients without PAD, with similar oncologic effectiveness between these 2 groups.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".