Unveiling the proteome-wide autoreactome enables enhanced evaluation of emerging CAR T cell therapies in autoimmunity
Bibliographic record
Abstract
Given the global surge in autoimmune diseases, it is critical to evaluate emerging therapeutic interventions. Despite numerous new targeted immunomodulatory therapies, comprehensive approaches to apply and evaluate the effects of these treatments longitudinally are lacking. Here, we leveraged advances in programmable-phage immunoprecipitation methodology to explore the modulation, or lack thereof, of autoantibody profiles, proteome-wide, in both health and disease. Using a custom set of over 730,000 human-derived peptides, we demonstrated that each individual, regardless of disease state, possesses a distinct and complex constellation of autoreactive antibodies. For each individual, the set of resulting autoreactivites constituted a unique immunological fingerprint, or "autoreactome," that was remarkably stable over years. Using the autoreactome as a primary output, we evaluated the relative effectiveness of various immunomodulatory therapies in altering autoantibody repertoires. We found that therapies targeting B cell maturation antigen (BCMA) profoundly altered an individual's autoreactome, while anti-CD19 and anti-CD20 therapies had minimal effects. These data both confirm that the autoreactome comprises autoantibodies secreted by plasma cells and strongly suggest that BCMA or other plasma cell-targeting therapies may be highly effective in treating currently refractory autoantibody-mediated diseases.
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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 | 0.001 | 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".