A Case Report Supporting the Use of Teclistamab in Multiple Myeloma With CNS Involvement
Bibliographic record
Abstract
Central nervous system involvement in multiple myeloma (MM-CNS) is a condition with poor prognosis and no clear treatment options. Standard regimens, including proteasome inhibitors (PIs) and immunomodulatory (IMiD) agents, provide minimal benefit in this setting, highlighting the need for novel therapies. Teclistamab, a bispecific T-cell engager (BiTE) targeting B-cell maturation agent (BCMA) and CD3, has demonstrated robust systemic activity in heavily pretreated MM but its role in CNS disease remains undefined, as patients with CNS involvement have been excluded from pivotal trials. We present the case of a 62-year-old female with high-risk MM who developed extensive leptomeningeal myelomatosis following multiple lines of therapy including autologous transplantation, PI- and IMiD-based regimens, and palliative radiotherapy. Upon presentation with confusion, aphasia, and ataxia, MRI revealed diffuse leptomeningeal enhancement. The patient elected to proceed with teclistamab therapy. Following two cycles, she achieved a very good partial serologic response and MRI demonstrated marked radiologic improvement with resolution of cerebellar nodularity and sulcal enhancement. However, functional recovery was not observed, and the treatment was discontinued after three cycles due to clinical decline and infectious complications. She subsequently transitioned to supportive care and passed away 1 month later. This case report documents one of the first reports of teclistamab demonstrating radiologic improvement in leptomeningeal disease in MM-CNS. While the patient's overall outcome was poor, the observed CNS response supports the biologic plausibility of BiTE penetration and activity in the CNS. These findings suggest the urgent need for prospective studies of BCMA-directed bispecific antibodies in MM-CNS, as well as earlier intervention prior to functional decline.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".