Circulating immunoregulatory B cell and autoreactive antibody profiles predict lack of toxicity to anti-PD-1 checkpoint inhibitor treatment in advanced melanoma
Bibliographic record
Abstract
BACKGROUND: The majority of patients with melanoma develop immune-related adverse events (irAEs), and over half do not respond to anti-PD-1 (Programmed cell death protein 1) checkpoint inhibitor (CPI) immunotherapy. Accurate predictive biomarkers for both response to therapy and development of irAEs are currently lacking in clinical practice. Here, we conduct deep immunophenotyping of circulating regulatory and class-switched B cell and antibody immune states in patients with advanced stage III/IV melanoma prior to and longitudinally during CPI. METHODS: Mass cytometry, serum antibody isotyping and immuno-mass spectrometry proteome-wide screening evaluations to identify autoreactive antibodies were undertaken to profile circulating humoral immunity features in patients and healthy subjects and interrogate pretreatment B cell and antibody signatures that predict toxicity and response to anti-PD-1 therapy. In paired blood samples pretreatment and post-treatment, these humoral immune response profiles were monitored and correlated with the onset of toxicity. RESULTS: We found increased circulating IL-10+ (Interleukin-10+) plasmablasts and double-negative (DN) B cell frequencies, higher PD-L1 (programmed death ligand 1), TGFβ (Transforming Growth Factorβ) and CD95 expression by B cells, alongside higher IgG4 and IgE serum levels in patients with stage III/IV melanoma. This suggests enhanced B regulatory and Th2 (Thelper2)-driven responses in advanced disease. Increased baseline frequency of DN2 B cells, plasmablasts, and serum IgE, IgA and antibody autoreactivity were observed in patients who did not develop irAE. During treatment, higher IL-10+class-switched memory B cell, plasmablast and IgG1, IgG3 and IgE, alongside reduced IgG2, IgG4, IgA and IgM levels, were observed. A reduction in autoantibodies targeting tubulins was observed during treatment. Increased frequency of class-switched memory B cells predicted improved survival, while reduced transitional and PD-L1+TGFβ+ naive B cell frequencies and higher IgG4 and IgE levels predicted lower survival, on anti-PD-1 therapy. CONCLUSIONS: Distinct B cell and antibody reactivities in patients with advanced melanoma share features with extrafollicular B cell responses in autoimmune diseases, may be protective from irAE and help predict outcomes to anti-PD-1.
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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 | 0.000 |
| 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) | 0.001 | 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".