Pathobiology of nodal peripheral T-cell lymphomas: current understanding and future directions
Bibliographic record
Abstract
Predominantly nodal is the most common clinical presentation of peripheral T- (and NK-) cell lymphomas (PTCL), which comprise three main groups of diseases: (i) systemic anaplastic large cell lymphomas (ALCL), whether positive or negative for anaplastic lymphoma kinase (ALK); (ii) follicular helper T-cell lymphomas (TFHL); and (iii) PTCL, not otherwise specified (NOS). Recent advances in the genomic and molecular characterization of PTCL, with enhanced understanding of pathobiology, have translated into significant updates in the latest 2022 classifications of lymphomas. ALK-negative ALCL is now recognized to be genetically heterogeneous, with identification of DUSP22 rearrangements in approximately 20-30% of cases, correlated with distinctive pathological and biological features. The notion of cell-of-origin as an important determinant of the classification of nodal PTCL is best exemplified by TFHL, considered as one disease or a group of related entities, sharing oncogenic pathways with frequent recurrent epigenetic mutations as well as a relationship to clonal hematopoiesis. Data are emerging to support that a similar cell-of-origin concept might be relevant to characterize meaningful subgroups within PTCL, NOS, based on cytotoxic and/or Th1 versus Th2 signatures. The small group of primary nodal Epstein-Barr virus-positive lymphomas of T- or NK-cell derivation, formerly considered PTCL, NOS, is now classified separately, due to distinctive features, and notably an aggressive course. This review summarizes current knowledge of the pathology and biology of nodal-based PTCL entities, with an emphasis on recent findings and underlying oncogenic mechanisms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".