Existing and Emerging Therapies for Cutaneous T-Cell Lymphoma
Bibliographic record
Abstract
Cutaneous T-cell lymphomas (CTCL) are a heterogeneous group of non-Hodgkin lymphomas characterized by an infiltration of malignant monoclonal T lymphocytes into the skin. Mycosis fungoides (MF), the most common subtype, and the rarer Sézary syndrome (SS), are considered the classical forms of CTCL, which, because of a varying presentation and lack of genetic and immunophenotypical markers, can often have a delayed diagnosis. With skin-directed topical treatment being the mainstay of therapy in the early stages, there is an absence of long-term curative therapies for advanced disease. Recent insight into the pathogenesis of CTCL has identified new potential therapeutic targets including the monoclonal antibody therapies, brentuximab vedotin and mogamulizumab. Brentuximab vedotin, an anti-CD30 antibody-drug conjugate, received extended approval by the US FDA in 2017 to include primary cutaneous anaplastic large-cell lymphoma and CD30-expressing MF. Mogamulizumab, an anti-CCR4 antibody, received FDA approval in 2018 for relapsed or refractory MF and SS. Further targets and therapies continue to be investigated, including the monoclonal antibody therapy alemtuzumab, an anti-CD52 antibody, and the immune checkpoint blockade therapies, pembrolizumab and nivolumab. These new and emerging targets and therapies may lead to a promising broadening of CTCL treatment options in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".