Safety and efficacy of immune checkpoint inhibitors in patients with cancer and pre-existing autoimmune disease: A systematic review and meta-analysis in non-small cell lung cancer.
Bibliographic record
Abstract
9053 Background: Cancer patients with pre-existing autoimmune diseases (AID) have been traditionally excluded from clinical trials of immune checkpoint inhibitors (ICI) due to concerns for immune activation leading to toxicity. As indications for ICI expand, there is a need for robust data on safety and efficacy of ICI in cancer patients with AID. Existing studies examined a heterogenous group of cancers and do not include a comparison to cancer patients without pre-existing AID. Therefore, data are lacking concerning how safety and efficacy of ICI in this group of patients may differ from the general population and whether the results are generalizable to non-small cell lung cancer (NSCLC). Methods: We searched for studies consisting of NSCLC, AID, ICI, treatment response, and adverse events using database-controlled vocabulary terms. We systematically searched Medline (PubMed), EMBASE, Scopus, CINAHL, and Web of Science. We selected studies that included NSCLC and excluded abstracts, case reports, review articles, and articles lacking outcomes or populations of interest. Three authors (WA, CL, NS) independently reviewed all abstracts to determine study eligibility and quality. Study quality was assessed using the Newcastle-Ottawa Scale. Study data were pooled using random-effects meta-analysis. Results: Data were extracted from 24 cohort studies, consisting of 10,924 cancer patients, of which 4353 were NSCLC patients. Studies examined a broad spectrum of AID consisting of 1157 patients, of which 291 were NSCLC patients. Pooled analysis revealed an AID flare incidence of 36% (95%CI 27%-46%) in all cancers and 23% (95%CI 9%-40%) in NSCLC. Tests of subgroup difference revealed no significant difference in AID flares by organ system in all cancers and NSCLC (p = 0.602 and p = 0.19, respectively). Pre-existing AID was associated with a higher risk of de novo iRAE in all cancer patients (RR 1.38, 95%CI 1.16-1.65) and in NSCLC patients (RR 1.51, 95%CI 1.12-2.03). There was no difference in de novo grade 3-4 iRAE and tumor response between cancer patients with and without AID. However, in NSCLC patients, pre-existing AID was associated with a 2-fold increased risk of de novo grade 3-4 iRAE (RR 1.95, 95%CI 1.01-3.75) but also better tumor response in achieving a complete or partial response (RR 1.56, 95%CI 1.19-2.04). Conclusions: Pre-existing AID confers an increased risk of toxicity during ICI therapy. NSCLC patients with AID are at a higher risk of de novo grade 3-4 iRAE but are also more likely to achieve treatment response than those without AID. Multidisciplinary collaboration is paramount when considering ICI therapy in this patient population with careful calculation of risk and benefit as well as close monitoring for toxicity.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".