Monoclonal IgG in MGUS and multiple myeloma targets infectious pathogens
Bibliographic record
Abstract
Subsets of mature B cell neoplasms are linked to infection with intracellular pathogens such as Epstein-Barr virus (EBV), hepatitis C virus (HCV), or Helicobacter pylori. However, the association between infection and the immunoglobulin-secreting (Ig-secreting) B proliferative disorders remains largely unresolved. We investigated whether the monoclonal IgG (mc IgG) produced by patients diagnosed with monoclonal gammopathy of undetermined significance (MGUS) or multiple myeloma (MM) targets infectious pathogens. Antigen specificity of purified mc IgG from a large patient cohort (n = 244) was determined using a multiplex infectious-antigen array (MIAA), which screens for reactivity to purified antigens or lysates from 9 pathogens. Purified mc IgG from 23.4% of patients (57 of 244) specifically recognized 1 pathogen in the MIAA. EBV was the most frequent target (15.6%), with 36 of 38 mc IgGs recognizing EBV nuclear antigen-1 (EBNA-1). MM patients with EBNA-1-specific mc IgG (14.0%) showed substantially greater bone marrow plasma cell infiltration and higher β2-microglobulin and inflammation/infection-linked cytokine levels compared with other smoldering myeloma/MM patients. Five other pathogens were the targets of mc IgG: herpes virus simplex-1 (2.9%), varicella zoster virus (1.6%), cytomegalovirus (0.8%), hepatitis C virus (1.2%), and H. pylori (1.2%). We conclude that a dysregulated immune response to infection may underlie disease onset and/or progression of MGUS and MM for subsets of patients.
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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 | 0.000 |
| 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".