<i>BCL2</i><scp>mRNA</scp> or protein abundance is superior to gene rearrangement status in predicting clinical outcomes in patients with diffuse large <scp>B</scp>‐cell lymphoma
Bibliographic record
Abstract
Introduction: BCL2 is important in lymphomagenesis and represents a candidate biomarker in DLBCL. However, the optimal approach for ascertaining BCL2 status in biopsy specimens is uncertain. Here, we determined BCL2 rearrangement status by fluorescence in situ hybridization (FISH), mRNA abundance by NanoString, and protein abundance by immunohistochemistry (IHC) or quantitative immunofluorescence (IF) in pretreatment FFPE biopsy specimens to ascertain the most clinically meaningful measures. Methods: We identified 56 cases of de novo DLBCL treated with R-CHOP from which pretreatment FFPE biopsy samples were available. To quantify BCL2 expression by IF, digital images were created by scanning histology sections coimmunostained for BCL2 and CD20, then BCL2 fluorescence signals were quantified objectively in CD20-positive cells. Results: BCL2 rearrangement or, interestingly, increased copy number of BCL2 was associated with greater mRNA and protein abundance according to either IHC or IF (P < .005 for all comparisons). BCL2 mRNA abundance was associated with elevated protein abundance by IHC (P < .0001) and IF (rs = 0.65; P < .0001) (Figure 1). In contrast, MYC gene rearrangement, but not increased copy number, was associated with more MYC mRNA and more protein based on IHC (P < .005 for all comparisons). Elevated BCL2 expression was associated with a reduced complete response rate to R-CHOP (mRNA, P = .002; IHC, P = .03; IF, P = .03) and reduced overall survival (OS; mRNA, P = .045; Figure 1), whereas neither BCL2 rearrangement (P = .48), MYC rearrangement (P = .83), MYC mRNA (P = .08) or protein expression (P = .56), MYC/BCL2 “double-hit” (P = .75), nor “dual expression” status (P = .41) was associated with reduced OS. Keywords: “double-hit” lymphomas; BCL2; diffuse large B-cell lymphoma (DLBCL)
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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 | 0.000 |
| 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) | 0.001 | 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".