Array comparative genomic hybridization reveals similarities between nodular lymphocyte predominant Hodgkin lymphoma and T cell/histiocyte rich large B cell lymphoma
Bibliographic record
Abstract
Nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) and T cell/histiocyte rich large B cell lymphoma (THRLBCL) usually affect middle-aged men, show tumour cells with a B cell phenotype and a low tumour cell content. Whereas the clinical behaviour of NLPHL is indolent, THRLBCL presents with advanced stage disease and an aggressive behaviour. In the present study, array comparative genomic hybridization was performed in seven typical NLPHL, four THRLBCL-like NLPHL variants, six THRLBCL and four diffuse large B cell lymphomas (DLBCL) derived from NLPHL. The number of genomic aberrations was higher in THRLBCL compared with typical and THRLBCL-like variant of NLPHL. Gains of 2p16.1 and losses of 2p11.2 and 9p11.2 were commonly observed in typical and THRLBCL-like variants of NLPHL as well as THRLBCL. Gains of 2p16.1, affecting the REL locus were confirmed in an independent cohort. Expression of the REL protein was observed at similar frequencies in typical and THRLBCL-like variant of NLPHL as well as THRLBCL (33-38%). In conclusion, the present study reveals further similarities between NLPHL and THRLBCL on the genomic level, confirming that these entities are part of a pathobiological spectrum with common molecular features, but varying clinical presentations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".