Chromosome abnormalities defined by conventional cytogenetics in plasma cell leukemia: what have we learned about its biology?
Bibliographic record
Abstract
Cancer cells are characterized by having chromosomal abnormalities. The number of aberrations and the specific chromosomes affected are likely correlated with tumor progression. In this study, we analyzed the karyotype of 126 plasma cell leukemia (PCL) patients to identify the most frequently occurring imbalances and to design a model of karyotypic evolution. The Mitelman database of chromosome was searched and abnormal karyotypes were assessed. The main clones were analyzed and chromosomal gains and losses were used to design a model of genetic acquisition based on the calculation of a variable called time to occurrence. Our comprehensive study of genetic abnormalities of a large number of PCL karyotypes suggests that PCL is mainly characterized by the presence of whole chromosome losses as well as IgH rearrangements which is similar to that observed in non-hyperdiploid multiple myeloma (MM). Temporal analysis suggests that most PCL have around 10 abnormalities at diagnosis. It is possible that accumulation of abnormalities such as 17p13 (TP53) and 1p losses may trigger the extramedullary features of PCL. Our study demonstrates that cytogenetics is a valuable tool to evaluate the role of genetic imbalances on karyotypic evolution by using a mathematical model.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".