Immunohistochemical prognostic markers in diffuse large B-cell lymphoma: validation of tissue microarray as a prerequisite for broad clinical applications--a study from the Lunenburg Lymphoma Biomarker Consortium.
Bibliographic record
Abstract
PURPOSE: The results of immunohistochemical class prediction and prognostic stratification of diffuse large B-cell lymphoma (DLBCL) have been remarkably various thus far. Apart from biologic variations, this may be caused by differences in laboratory techniques, scoring definitions, and inter- and intraobserver variations. In this study, an international collaboration of clinical lymphoma research groups from Europe, United States, and Canada concentrated on validation and standardization of immunohistochemistry of the currently potentially interesting prognostic markers in DLBCL. PATIENTS AND METHODS: Sections of a tissue microarray from 36 patients with DLBCL were stained in eight laboratories with antibodies to CD20, CD5, bcl-2, bcl-6, CD10, HLA-DR, MUM1, and MIB-1 according to local methods. The study was performed in two rounds firstly focused on the evaluation of laboratory staining variation and secondly on the scoring variation. RESULTS: Different laboratory staining techniques resulted in unexpectedly highly variable results and very poor reproducibility in scoring for almost all markers. No single laboratory stood out as uniformly poor or excellent. With elimination of variation due to staining, high agreement was found for CD20, HLA-DR, and CD10. Poor agreement was found for bcl-6 and Ki-67. Optimization of techniques and uniformly agreed on scoring criteria improved reproducibility. CONCLUSION: This study shows that semiquantitative immunohistochemistry for subclassification of DLBCL is feasible and reproducible, but exhibits varying rates of concordance for different markers. These findings may explain the wide variation of biomarker prognostic impact reported in the literature. Harmonization of techniques and centralized consensus review appears mandatory when using immunohistochemical biomarkers for treatment stratification.
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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 | 0.017 | 0.011 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".