Mogamulizumab and Concomitant Hypofractionated Low-Dose Total Skin Electron Beam Therapy (2 × 4 Gy) in Cutaneous T-Cell Lymphoma: Proof of Principle, Report of Two Cases
Bibliographic record
Abstract
Patients with advanced-stage mycosis fungoides (MF IIB-IVB) and Sézary syndrome (SS) have poor prognoses, with survival ranging from 4.7 to 1.4 years depending on the disease stage. There is a need for therapeutic approaches that lead to long-lasting responses and improved quality of life and survival. Mogamulizumab, a humanized antibody against the CCR4 molecule, and low-dose total skin electron beam therapy (TSEBT) are two known established treatments for MF and SS as a monotherapy. However, little is known about the potential additive effect on the combination of both treatments. We report here for the first time the concurrent use of low-dose hypofractionated TSEBT (2 × 4 Gy) with mogamulizumab. Based on two relapsed/refractory and advanced-stage CTCL patients, we show that this combination may be well tolerated in advanced-stage MF or SS and may potentially lead to an additive treatment effect on response times, particularly in the skin and blood within two weeks. We propose that this combination may be a treatment option for patients with SS. Further research is needed to understand the efficacy and tolerability profile of this therapeutic combination and to determine if there is an additive effect of the combination on the response rates when compared with the monotherapy.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".