Comparison of protein-based cell-of-origin classification to the Lymph2Cx RNA assay in a cohort of diffuse large B-cell lymphomas in Malaysia
Bibliographic record
Abstract
AIMS: The cell of origin (COO) based molecular characterisation into germinal centre B-cell-like (GCB) and activated B-cell-like (ABC) subtypes are central to the pathogenesis and clinical course in diffuse large B-cell lymphoma (DLBCL). Globally, clinical laboratories employ pragmatic but less than ideal immunohistochemical (IHC) assay for COO classification. Novel RNA-based platforms using routine pathology samples are emerging as new gold standard and offer unique opportunities for assay standardisation for laboratories across the world. We evaluated our IHC protocols against RNA-based technologies to determine concordance; additionally, we gauged the impact of preanalytical variation on the performance of Lymph2Cx assay. METHODS: Diagnostic biopsies (n=104) were examined for COO classification, employing automated RNA digital quantification assay (Lymph2Cx). Results were equated against IHC-based COO categorisation. Assay performance was assessed through its impact on overall survival (OS). RESULTS: 96 (92%) informative samples were labelled as GCB (38/96; 40%) and non-GCB (58/96; 60%) by IHC evaluation. Lymph2Cx catalogued 36/96 (37%) samples as GCB, 45/96 (47%) as ABC and 15/96 (16%) as unclassified. Lymph2Cx being reference, IHC protocol revealed sensitivity of 81% for ABC and 75% for GCB categorisation and positive predictive value of 81% versus 82%, respectively. Lymph2Cx-based COO classification performed superior to Hans algorithm in predicting OS (log rank test, p=0.017 vs p=0.212). CONCLUSIONS: Our report show that current IHC-based protocols for COO classification of DLBCL at UKM Malaysia are in line with previously reported results and marked variation in preanalytical factors do not critically impact Lymph2Cx assay quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".