BCL2 Expression in First-Line Diffuse Large B-Cell Lymphoma Identifies a Patient Population With Poor Prognosis
Bibliographic record
Abstract
INTRODUCTION: The prognostic value of B-cell lymphoma 2 (BCL2) expression in de novo diffuse large B-cell lymphoma (DLBCL) treated with immunochemotherapy is of interest to define a target patient population for clinical development of BCL2 inhibitors. We aimed to develop a reproducible immunohistochemistry algorithm and assay to determine BCL2 protein expression and assess the prognostic value of BCL2 in newly diagnosed DLBCL cohorts. PATIENTS AND METHODS: The prospectively defined algorithm incorporated BCL2 staining intensity and percentage of BCL2-positive cells. Functionally relevant cutoffs were based on the sensitivity of lymphoma cell lines to venetoclax. This assay was highly reproducible across laboratories. The prognostic impact of BCL2 expression was assessed in DLBCL patients from the phase 3 MAIN (n = 230) and GOYA (n = 366) trials, and a population-based registry (n = 310). RESULTS: Approximately 50% of tumors were BCL2 positive, with a higher frequency in high International Prognostic Index (IPI) and activated B-cell-like DLBCL subgroups. BCL2 expression was associated with poorer progression-free survival in the MAIN study (hazard ratio [HR], 1.66; 95% confidence interval [CI], 0.81-3.40; multivariate Cox regression adjusted for IPI and cell of origin). This trend was confirmed in the GOYA and registry cohorts in adjusted multivariate analyses (GOYA: HR, 1.72; 95% CI, 1.05-2.82; registry: HR, 1.89; 95% CI, 1.29-2.78). Patients with BCL2 immunohistochemistry-positive and IPI-high disease had the poorest prognosis: 3-year progression-free survival rates were 51% (GOYA) and 37% (registry). CONCLUSION: Findings support use of our BCL2 immunohistochemistry scoring system and assay to select patients with BCL2-positive tumors for future studies.
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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 | 0.001 | 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".