Validation of a Modular Gene Expression Assay for Risk Stratification and Subtyping Lymphomas
Bibliographic record
Abstract
Gene expression signatures are important for classifying lymphoid malignancies, although routine diagnostic workflows predominantly use immunohistochemical staining and fluorescence in situ hybridization. These traditional methods are labor intensive and may misclassify the underlying oncogenic signatures, leading to inaccurate prognostication. To address this issue, an RNA expression panel was developed, the Lymphoma Expression Analysis (LExA120) 120 gene expression panel, using the NanoString platform for rapid, modular analysis of various lymphoma subtypes. The LExA120 panel targets 95 genes and 25 housekeeping genes to evaluate aggressive B-cell lymphomas, including: diffuse large B-cell lymphoma cell-of-origin, dark zone, and primary mediastinal large B-cell lymphoma signatures; Epstein-Barr virus (EBV) status; and a classical Hodgkin lymphoma posttransplant risk. Fifty-four formalin-fixed, paraffin-embedded tissue samples were tested with known diagnoses and 51 samples with known EBV status. The panel showed high concordance with previously validated methods according to Pearson correlation coefficients of the signature scores. The assay also displayed high reproducibility in repeated tests and across different clinical laboratories. This study confirmed the panel's ability to stratify EBV-positive and EBV-negative lymphomas with high diagnostic certainty. Although EBER in situ hybridization confirmation was needed in approximately 12% of cases, synergizing with traditional techniques may facilitate more rapid and cost-effective diagnoses. The LExA120 panel offers a multiplexed approach to lymphoma classification, enhancing the efficiency and accuracy for subtyping lymphomas.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".