Unbalanced <i>MYC</i> break-apart FISH patterns indicate the presence of a <i>MYC</i> rearrangement in HGBCL-DH-<i>BCL2</i>
Bibliographic record
Abstract
ABSTRACT: Fluorescence in situ hybridization (FISH) using break-apart probes is recommended for identifying high-grade B-cell lymphoma with MYC and BCL2 rearrangements (HGBCL-DH-BCL2). Unbalanced MYC break-apart patterns, in which the red or green signal is lost, are commonly reported as an equivocal result by clinical laboratories. In a cohort of 297 HGBCL-DH-BCL2, 13% of tumors had unbalanced MYC break-apart patterns with loss of red (LR; 2%) or loss of green (LG; 11%) signal. To determine the significance of these patterns, MYC rearrangements were characterized by sequencing in 130 HGBCL-DH-BCL2, including 3 LR and 14 LG tumors. A MYC rearrangement was identified for 71% of tumors with LR or LG patterns, with the majority involving immunoglobulin loci or other recurrent MYC rearrangement partners. The architecture of these rearrangements consistently preserved the rearranged MYC allele, with the MYC gene predicted to be on the derivative chromosome containing the signal that is still present in nearly all cases. MYC protein expression, MYC messenger RNA expression, and the proportion of tumors expressing the dark-zone signature was not significantly different between balanced and unbalanced groups. These results support a recommendation that unbalanced MYC break-apart FISH patterns be reported as positive for MYC rearrangement in the context of diagnosing HGBCL-DH-BCL2.
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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) | 0.004 | 0.001 |
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".