TRAF1 Expression and c-Rel Activation Are Useful Adjuncts in Distinguishing Classical Hodgkin Lymphoma From a Subset of Morphologically or Immunophenotypically Similar Lymphomas
Bibliographic record
Abstract
We demonstrate that the expression of TRAF1 and activated c-Rel, two proteins that function in signaling events downstream of activated CD30 in Reed-Sternberg cells, reliably distinguish classical Hodgkin lymphoma from anaplastic large cell lymphoma, nodular lymphocyte predominant Hodgkin lymphoma, and nonmediastinal diffuse large B-cell lymphoma. By immunohistochemistry, we found strong TRAF1 staining in 21 of 25 cases of classical Hodgkin lymphoma. In contrast, strong TRAF1 staining was present in only 1 of 17 cases of anaplastic large cell lymphoma, 0 of 15 cases of lymphocyte predominant Hodgkin lymphoma, and 2 of 36 cases of nonmediastinal diffuse large B-cell lymphoma. Nuclear staining for c-Rel, a pattern consistent with NFkappaB activation, was observed in the Reed-Sternberg cells in 23 of 25 cases of classical Hodgkin lymphoma but only in 1 of 15 cases of anaplastic large cell lymphoma and 3 of 15 cases of nodular lymphocyte predominant Hodgkin lymphoma. A heterogeneous pattern of subcellular c-Rel localization was found in nonmediastinal diffuse large B-cell lymphoma. Taken together, the combination of strong cytoplasmic TRAF1 expression and nuclear c-Rel was present in 80% of cases of classical Hodgkin lymphoma (n = 25) but in only 3% of cases of the other malignant lymphomas tested (n = 62). Thus, the differential expression patterns of downstream components in the CD30 signaling pathway may prove a useful adjunct in distinguishing cases of classical Hodgkin lymphoma from other malignant lymphomas in routine clinical practice.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".