From Chronic Lymphocytic Leukemia to Plasmablastic Myeloma: Beyond the Usual Richter Transformation
Bibliographic record
Abstract
Background: Richter transformation (RT) is defined as the histologic transformation of Chronic Lymphocytic Leukemia (CLL) to either diffuse large B-cell lymphoma or Hodgkin lymphoma. Transformation into lymphoproliferative neoplasms with plasmablastic differentiation is exceptionally rare and poorly characterized. Case Presentation: We present the first case of a patient with CLL evolving into plasmablastic myeloma (PBM). A 62-year-old man with previously treated CLL developed thrombocytopenia and rapidly progressive acute kidney injury. Serum electrophoresis showed new IgA-λ protein (2.2 g/L) with λ and κ light chains at 3445.4 and 7.3 mg/L. Bone marrow examination showed extensive infiltration (>95%) by plasmablasts and mature plasma cells, with a consistent immunophenotype (CD38+, CD138+, MUM1+, CD19−, CD20−). In situ hybridization with EBER was negative. Mutation assessment by NGS demonstrated a TP53 mutation and FISH prob panel revealed a new del17p. Clonal relatedness was confirmed by shared IGHV somatic hypermutation using NGS. The patient was primary refractory to frontline myeloma therapy with Dara-VRd and succumbed rapidly to his disease. Discussion: This case illustrates an exceptionally rare form of RT. Recognition and incorporation in new classifications of plasmablastic RT as a distinct entity is critical, as its biology and resistance profile differ from classical RT.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".