Immune Checkpoint Inhibitors in Patients with Pre-Existing Rheumatic Disease and the Impact of Immunosuppression: Data from the Canadian Research Group of Rheumatology in Immuno-Oncology (CanRIO)
Bibliographic record
Abstract
Objectives Immune checkpoint inhibitors (ICI) have transformed oncology care, but their use is limited by immune-related adverse events (irAEs) and optimal use in patients with rheumatic pre-existing autoimmune disease (Rh-PAD) is unknown.[1] We report on ICI management, baseline immunosuppression, and irAE treatment strategies, using data from the CanRIO retrospective and prospective cohorts from 10 Canadian sites. Methods Patients with Rh-PAD recruited to the prospective cohort between Jan 2020 and April 2023 and retrospective cohort from Jan 2013 to June 2022 and who received at least 1 dose of CTLA-4, PD-1, or PDL-1 ICI therapy were included. Data on irAE treatment, baseline immunosuppression, and cancer outcomes are collected in a REDCap database as per standardized protocol. Results Eighty-three eligible patients (40 from prospective and 43 from retrospective cohort) were identified and stratified by baseline immunosuppression (IS), with 44 on baseline IS and 39 not on baseline IS. Baseline IS included corticosteroids, conventional DMARDs, and biologic DMARDs. Of those on baseline IS, 37 had inflammatory arthritis (48.65% active) and 7 had other rheumatic disease (57.14% active). Of those not on baseline IS, 26 had inflammatory arthritis (26.92% active) and 13 had other rheumatic disease (23.08% active). Patients on baseline IS were more likely to have a PAD flare (OR 4.19, P=0.003) and less likely to develop a new unrelated irAE (OR 0.36, P=0.027) compared to those not on baseline IS. In both cohorts, 4 patients experienced both a flare of their Rh-PAD and a de novo irAE. Those with de novo irAE generally responded to analgesics, corticosteroids or csDMARDs. There was a non-significant trend toward patients on baseline IS being more likely to require bDMARD compared to those not on baseline IS (P = 0.057). Grade 1-2 irAEs were more likely to be treated while continuing immunotherapy, while ICIs were either stopped or held in all patients with grade 3-5 events (Baseline IS P = 0.026; No Baseline IS P = 0.052; All Patients P < 0.001). Conclusion Our findings suggest those on baseline IS have higher rates of active PAD at start of ICI therapy. We found 1) Continuing baseline DMARDs may not protect from flares but may prevent de novo irAEs. If de-novo irAEs occur, bDMARD may be required for treatment 2) Continuing immunotherapy during irAE treatment can be considered for patients with low grade irAEs. Future analyses will explore the impact of baseline IS on cancer outcomes. [1.] Khalil DN. Nat Rev Clin 2016;13:273-90.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".