Baseline peripheral T-cell composition in relation to radiographic phenotypes of immune-related pneumonitis.
Bibliographic record
Abstract
2545 Background: Pneumonitis is one of the most morbid complications from immune checkpoint inhibitor (ICI) treatment, but pathogenic mechanisms are unclear and no biomarkers permit pre-treatment risk assessment. We sought to characterize peripheral T cell subsets of pneumonitis patients on the single cell level. Methods: Blood was collected before and during ICI treatment in 24 patients. Cells were processed for single cell RNA sequencing (scRNAseq) employing CITEseq methodology using multiplexed cell surface markers labelled with a cocktail of oligonucleotide-tagged Total-Seq anti-human antibodies against CD4, CD8, CD45RA and CD27 followed by Chromium 10X sequencing. Principal Component Analysis was performed with iCellR, K-nearest-neighbor-based Network graph drawing Layout, and PhenoGraph clustering to assign cell types. CT scans were performed per standard of care and were reviewed by an experienced thoracic radiologist. Results: Seven of 24 patients developed pneumonitis; 9 did not experience an immune-related adverse event, and the remainder experienced arthritis (4), thyroiditis (3), or neurotoxicity (1). Pneumonitis patients had expanded proportions of TH2 TCF7+ T cells at baseline as compared to the other patients. Radiographically, two patients’ pneumonitis manifested as Chronic Hypersensitivity Pneumonitis (CHP), and four had Organized Pneumonia (OP). At baseline, CHP patients had significantly lower levels of CD8+ TCM cells (CXCR3+), double-positive T cells, gamma-delta T cells, and higher levels of naïve-like CD4+ TN TCF7+LEF1+ and CD4+ TH1/2 CXCR3+GATA3+ cells compared to OP. Gene expression levels also distinguished between these radiographic phenotypes. Conclusions: The peripheral T cell composition of patients who developed pneumonitis was distinct from those who did not in our cohort and unique by radiographic manifestation, suggesting potential pathogenic mechanisms and a prelude to circulating predictive markers of ICI 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".