Cytogenetic abnormalities and clinical correlations in peripheral T‐cell lymphoma
Bibliographic record
Abstract
Cytogenetic correlations among most types of peripheral T-cell lymphoma (PTCL) have not been very informative to date. This study aimed to identify recurrent chromosomal abnormalities in angioimmunoblastic T-cell lymphoma (AITL), ALK-negative anaplastic large cell lymphoma (ALK-ALCL) and peripheral T-cell lymphoma, unspecified (PTCL-US), and to evaluate their prognostic value. We reviewed the cytogenetic findings of 90 previously-diagnosed cases of PTCL and correlated the cytogenetic findings with the specific histological subtype. The most common abnormalities for AITL were 5q (55%), 21 (41%) and 3q (36%) gains, concurrent trisomies of 5 and 21 (41%), and loss of 6q (23%). In ALK(-) ALCL, gains of 1q (50%) and 3p (30%), and losses of 16pter (50%), 6q13q21 (30%), 15 (30%), 16qter (30%) and 17p13 (30%) were frequent findings. In PTCL-US, frequent gains involved 7q22q31 (33%), 1q (24%), 3p (20%), 5p (20%), and 8q24qter (22%), and losses of 6q22q24 (26%) and 10p13pter (26%). We did not observe any association between specific chromosomal abnormalities and overall survival (OS). However, cases with complex karyotypes, most frequently observed in ALK(-) ALCL and PTCL-US, had a significantly shorter OS. Although, genetic differences were noted in these subtypes, further studies are needed to determine the key pathogenetic events in PTCL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".