Multicolour fluorescence <i>in situ</i> hybridization analysis of t(14;18)‐positive follicular lymphoma and correlation with gene expression data and clinical outcome
Bibliographic record
Abstract
In order fully to identify secondary chromosomal alterations, such as duplications, additions and marker chromosomes that remained unresolved by G banding, 60 cases of t(14;18)-positive follicular lymphoma (FL) were analysed by multicolour karyotyping techniques [multicolour fluorescence in situ hybridization (MFISH)/multicolour banding for chromosome 1 (MBAND1)]. A total of 165 additional structural chromosomal aberrations were delineated. An increased frequency of chromosomal gains involving X, 1q, 2, 3q27-q29, 5, 6p11-p21, 7, 8, 11, 12, 14q32, 17q, 18 and 21 and deletions of 1p36, 3q28-q29, 6q, 10q22-q24 and 17p11-p13 was revealed by the MFISH/MBAND1 analysis. Balanced translocations other than t(14;18) were uncommon, whereas unbalanced translocations were numerous. Deletion of 1p36 and duplication of 1p33-p35, 1p12-p21 and 1q21-q41 were regularly involved in chromosome 1 alterations, seen in 53% of the cases. A strong correlation was demonstrated between gains of individual chromosomal bands and increased gene expression, including 1q22/MNDA, 6p21/CDKN1A, 12q13-q14/SAS, 17q23/ZNF161, 18q21/BCL2 and Xq13/IL2RG. Unfavourable overall survival was associated with del(1)(p36) and dup(18q). These data support the notion that translocation events are primarily responsible for FL disease initiation, whereas the unbalanced chromosomal gains and losses that mirror the gene expression patterns characterize clonal evolution and disease progression, and thus provide further insights into the biology of FL.
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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) | 0.001 | 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".