Co-occurrence of myositis and neuropathy after anti-CD30 therapy in a late-adolescent Hodgkin lymphoma patient
Bibliographic record
Abstract
Abstract Objective Immune-related adverse events (irAEs) are recognized in oncology, particularly with immune checkpoint inhibitors and other targeted therapies. Brentuximab Vedotin (BV), is an anti-CD30 antibody–drug conjugate- its association with immune-mediated myositis remains unexplored. We report a case of an adolescent with Hodgkin lymphoma (HL) who developed neuropathy and myositis following BV therapy. Materials & methods The diagnostic work-up included MRI as well as microscopic analyses (histology, electron microscopy, and immunostainings including CD30 and MxA) of a gastrocnemius muscle biopsy. Proteomic analysis was also performed on the same biopsy, and paradigmatic protein dysregulations were validated through immunostaining. Serum NCAM1 levels were measured using ELISA. Results The patient, diagnosed with HL at 15 years, developed neuropathy after Vincristine treatment and was switched to BV. During BV therapy, she experienced progressive muscle weakness and foot drop, leading to discontinuation. MRI confirmed myositis, and biopsy revealed neurogenic and inflammatory changes with complement deposition and mitochondrial dysfunction. Proteomics showed upregulation of inflammatory relevant proteins, with HPRT1 (749.43-fold) being the most increased one. Intravenous immunoglobulin (IVIG) therapy improved muscle strength. Discussion Myositis following BV therapy has not been reported. Findings suggest an immune-mediated mechanism with B-cell involvement. Given the response to IVIG, B-cell-directed therapies may be beneficial. This case identifies BV-induced myositis as a novel irAE.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".