Safety and Clinical Outcomes of Immune Checkpoint Inhibitors in Patients With Cancer and Preexisting Autoimmune Diseases
Bibliographic record
Abstract
Immunotherapy has revolutionized treatment outcomes in numerous cancers. However, clinical trials have largely excluded patients with autoimmune diseases (ADs) due to the risk of AD flares or predilection for developing organ-specific inflammation. The objective of this study was to evaluate the safety and efficacy of immunotherapy in patients with cancer and preexisting ADs. A retrospective, single-center study of patients with cancer initiated on immune checkpoint inhibitors between 2012 and 2019 was conducted. The primary outcome was the development of immune-related adverse events (irAEs) with respect to the presence of AD at baseline. Associations were assessed using Kaplan-Meier curves, bivariate and multivariable analyses. Of the 417 patients included in this study, 63 patients (15%) had preexisting ADs. A total of 218 patients (53%) developed at least 1 irAE. There was no association between the presence of baseline AD on the development, grade, or number of irAEs; time to irAE or irAE recovery; systemic corticosteroid or additional immunosuppressant treatment for irAEs; permanent treatment discontinuation; or overall response rate. Two smaller cohorts were studied, melanoma and non-small cell lung cancer, and there was no effect of baseline AD on overall survival on either cohort. However, a greater proportion of patients with baseline ADs had full recovery from their irAE (P=0.037). Furthermore, age below 65, baseline steroid use, and single-agent immunotherapy regimens were protective in terms of the development of irAEs. Our study suggests that immune checkpoint inhibitors have similar safety and efficacy profiles in patients with preexisting ADs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".