Customized autoantibodies (autoAbs) profiling to predict and monitor immune-related adverse events (irAEs) in patients receiving immune checkpoint inhibitors (ICI).
Bibliographic record
Abstract
2528 Background: Using a customized microarray, we previously reported that patients (pts) who develop irAEs grade (G)≥2 and those who do not, have different median fluorescent intensity (MFI) levels of specific autoAbs at baseline (pre-ICI). Leveraging a larger dataset, we evaluated whether overall baseline autoAbs elevation and early increases in autoAbs after ICI can predict irAEs as well as if steroid treatment can reduce autoAbs. Methods: Plasma was obtained from pts receiving ICI in two clinical trials (MET4-IO, NCT03686202 and INSPIRE, NCT02644369) and from healthy controls (hc). Collection time points in MET4-IO and INSPIRE studies included: baseline, 3-4 weeks (w), 6-8 w, 24 w and at the end of treatment and baseline and 6 w respectively. Arrays with 162 autoAg customized for frequent irAEs were incubated with plasma and probed with Abs to detect IgG and IgM reactivity. AutoAbs with MFI >500 per individual were compared between hc and pts with and without irAEs G≥2 by the Student-t test. Results: Samples from 114 pts and 14 hc were analyzed (pts characteristics are summarized in the Table). G≥2 irAEs included: hypothyroidism (13), pneumonitis (10), colitis (7), hepatitis (4), skin toxicity (7), infusion reaction (2), pancreatitis (2), meningitis (1), hypophysitis (1), corneal ulcer (1), high creatinine (1), myocarditis (1), myositis (1), diabetes (1), mucositis (1), myasthenia (1) and adrenal failure (1). Hc had less autoAbs with MFI >500 as compared to pts at baseline (median 32 vs 62 p<0.001). IgG with MFI >500 were higher at baseline in pts who developed G≥2 irAEs vs those without G≥2 irAEs (median 39 vs 33, p=0.03). In 23 pts with plasma collected at the time of irAEs, we observed a significant increase of autoAbs with MFI>500 from baseline (median 84 vs 77 p= 0.009). Paired samples at the time of irAEs and after steroids were available for 9/23 pts, showing lower autoAbs after steroid treatment (54 vs 79 p=0.006). No differences in autoAbs with MFI>500 pre and post ICI were seen in pts without G≥2 irAEs (baseline vs first post ICI collection median 58 vs 60, p=0.13). Conclusions: We observed a higher number of IgG with MFI >500 at baseline and a greater increase after ICI administration in individuals with irAEs compared to those without irAEs. Steroid treatment resulted in a decrease in autoAbs. A prospective study is ongoing to validate the potential role of autoAbs for risk stratification and monitoring of irAEs. [Table: see text]
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".