Gene Expression Profiling, Frozen and Paraffin Section Immunohistochemistry, and In Situ Hybridization for Determination of Monoclonality in Diffuse Large B-Cell Lymphoma.
Bibliographic record
Abstract
Abstract Background: Determining monotypia of surface immunoglobulin (SIg) is often an important step in the diagnosis of B-cell lymphomas. Many competing methods have been developed and are commonly used in clinical practice. The role of gene expression profiling (GEP) in determining light chain restriction is yet to be clarified. The goal of this study was to compare 4 methods by which light chain restriction can be determined: frozen section immunohistochemistry (FS-IHC), paraffin section IHC (PS-IHC), in-situ hybridization (ISH), and GEP. Design: 40 cases of DLBCL, part of a previous GEP study of DLBCL (Rosenwald et al, NEJM 2002), were made into a tissue microarray (TMA). FS-IHC slides, previously stained using by-hand streptavidin and diaminobenzidine, were re-examined for κ:λ restriction. PS-IHC was done on the TMA using the Benchmark system according to manufacturer’s protocols for κ/λ staining (Ventana Medical Systems VMSI, Tucson, AZ). ISH was performed on the TMA with a newly developed sensitive ISH procedure from VMSI which uses a purified streptavidin reagent to reduce background and increase sensitivity. The FS-IHC, PS-IHC, and ISH arrays were reviewed and scored as κ/λ monotypic, SIg-negative or indeterminate by 3 pathologists. The averaged gene expression ratios for microarray elements probing for κ and λ on each case were plotted on a log2 scale. Monoclonality was determined using 2 different GEP criteria: (1) κ-monoclonal if κ:λ expression was >2log2 or λ-monoclonal if expression was λ>κ or (2) κ and λ light chain relative expression on either side of the median. These data from all 4 techniques were used to determine a consensus clonality in which the majority of the results agreed. The results for each technique were then compared to the consensus for that case. Results: 7 Cases had a 4/4 consensus, 9 cases a 3/4, 11 cases a 3/3, 7 cases a 2/3, and 1 case a 2/2. 5 cases were excluded from the study because there was no majority consensus. 19 cases (47.5%) were κ monoclonal, 10 (25%) λ monoclonal, 6 (15%) SIg-negative, and 5 cases (12.5%) indeterminate. Compared to the consensus clonality FS-IHC was accurate in 21/26 (81%), PS-IHC in 28/32 (87%), sensitive ISH in 29/29 (100%) cases. GEP was accurate in 31/35 (89%) cases using either criteria (1) or (2); in 27/35 (77%) using criterion (1) alone, in 28/35 (80%) using criterion (2) alone, and 24/35 (69%) of cases when both criteria (1) and (2) were met. 6 SIg-negative cases failed all GEP criterion, but were considered correctly classified as neitherκ nor λ monoclonal. Discussion: The technique with the highest accuracy compared to consensus was the sensitive ISH assay, in part because samples were eliminated if the mRNA control (polyT probe) staining was suboptimal. GEP was also accurate using either of our criteria. The SIg-negative cases were not classified as either κ or λ monoclonal using any of our criteria. Given the increasing role GEP is likely to play in hematopathology, the application of GEP to B cell clonality may have diganostic utility.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".