Immune signatures predict development of autoimmune toxicity in patients with cancer treated with immune checkpoint inhibitors
Bibliographic record
Abstract
Background Immune checkpoint inhibitors (ICIs) are among the most promising treatment options for melanoma and non-small cell lung cancer (NSCLC). While ICIs can induce effective anti-tumor responses, they may also drive serious immune-related adverse events (irAEs). Identifying biomarkers to predict which patients will suffer from irAEs would enable more accurate clinical risk-benefit analysis for ICI treatment and may also shed light on common or distinct mechanisms underpinning treatment success and irAEs. Methods In this prospective multi-center study, we combined a multi-omics approach including unbiased single-cell profiling of over 300 peripheral blood mononuclear cell (PBMC) samples and high-throughput proteomics analysis of over 500 serum samples to characterize the systemic immune compartment of patients with melanoma or NSCLC before and during treatment with ICIs. Findings When we combined the parameters obtained from the multi-omics profiling of patient blood and serum, we identified potential predictive biomarkers for ICI-induced irAEs. Specifically, an early increase in CXCL9/CXCL10/CXCL11 and interferon-γ (IFN-γ) 1 to 2 weeks after the start of therapy are likely indicators of heightened risk of developing irAEs. In addition, an early expansion of Ki-67 + regulatory T cells (Tregs) and Ki-67 + CD8 + T cells is also likely to be associated with increased risk of irAEs. Conclusions We suggest that the combination of these cellular and proteomic biomarkers may help to predict which patients are likely to benefit most from ICI therapy and those requiring intensive monitoring for irAEs. Funding This work was primarily funded by the European Research Council, the Swiss National Science Foundation, the Swiss Cancer League, and the Forschungsförderung of the Kantonsspital St. Gallen.
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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 | 0.001 | 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".