Detection of immunoglobulin IGH gene rearrangements on formalin-fixed, paraffin embedded tissue in lymphoid malignancies.
Bibliographic record
Abstract
Human lymphomas are aggressive malignant diseases, which can be categorized based on their B and T cell lineage. B-cell lymphomas form around 90% of the total lymphoma cases, the remnants of malignancies arise from the T cell branch. Lymphomas are mostly characterized as clonal proliferations of specific tumor cells. The detection of malignant lymphomas are extensively investigated by their morphological features, immunohistochemistry and flowcytometric immunophenotyping, but in some of cases remained unknown. The BIOMED-2 protocols were used to determine the clonality of IGH gene rearrangements in patients with lymphoma. PCR amplification was performed on FFPE of 50 patients with B-cell lymphoma, which consisted of 11 cases with HLs, 25 cases of B-NHLs and 14 cases of B-LPD (lymphoproliferative disorders) that diagnosed as unclassifiable lymphoma. The rate of positive clonality was detected in 96% (24/25) of B-NHLs, whereas in 4% (1/25) of cases clonality was showed in a polyclonal pattern. In B-HLs, 82% (9/11) of cases showed clonality and 18% (2/11) of the cases showed polyclonality. The rate of positive clonality observed in 64.3% (9/14) of cases with B-LPD and 35.7% (5/14) of cases clonality was not detected in any of immunoglobulin gene family (FR1, FR2, FR3). In groups with DLBCL, clonality was detected in 95% (19/20) of the cases. In patients diagnosed with FL and MALTs 100% cases showed clonality for complete IGH. Our study revealed that EuroClonality BIOMED-2 protocols could be considered as a valuable and reliable method for clonality detection, especially in IGH analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".