Mechanisms of Bcl-2 Protein Expression in Diffuse Large B-Cell Lymphoma (DLBCL).
Bibliographic record
Abstract
Abstract Background: Bcl-2 protein expression is an important biomarker in DLBCL. Gene expression profiling also has prognostic relevance in DLBCL, establishing cell-of-origin (GCB vs non-GCB) as an important predictor of survival. Using tissue microarrays (TMA), immunohistochemical staining can be used to provide biomarker data that correlates closely with the results of gene expression profiling in predicting outcome (Hans et al, Blood 103; 275–82: 2004). The t(14;18) can be detected in DLBCL, with frequencies varying between 5–40%. The aim of this study was to clarify the different mechanisms leading to Bcl-2 expression in a cohort of patients with DLBCL. Methods: The study group consisted of 94 patients with de novo DLBCL, all with a clonal karyotype at diagnosis and treated with curative intent. A TMA was constructed with duplicate 0.6mm cores and stained for Bcl-2, CD10, Bcl-6, MUM1 and FOXP1. Cases were called positive if more than 30% of the tumor cells expressed a given protein. Cases were defined as GCB if they were CD10+. Non-GCB was defined as CD10−, MUM1+ and/or FOXP1+. Cytogenetic studies were performed routinely and locus-specific FISH was performed using commercially available Vysis probes (dual-color LSI IGH/BCL2) to detect the t(14;18). Unbalanced increases in BCL2 gene copy number were determined by comparison of BCL2 and IGH signals and correlation with the karyotype. Results: The IPI was highly predictive of overall survival (OS) (p < 0.00001). The t(14;18) was detected by both routine cytogenetics and FISH in 24 (25%) cases, but did not predict survival (p = 0.78). None of the non-GCB cases harbored a t(14;18). The t(14;18) and isolated BCL2 copy number gain were mutually exclusive. Expression of Bcl-2 protein and GCB-type immunostaining profile each predicted OS (p = 0.008 and 0.03, respectively). Expression of Bcl-2 was imperfectly correlated with either the t(14;18) or a non-GCB immunostaining profile, but was highly correlated with cases harboring an increased gene copy number for BCL2 (see Table, χ2 p=0.005). Increased BCL2 gene copy number did not predict OS (p = 0.43), a not unexpected finding as it accounts for only a proportion of Bcl-2 protein-positive cases. Cases lacking both the t(14;18) and increased BCL2 copy number were deemed cytogenetically “normal”, accounting for 47 cases. These were distributed between the GCB (42%) and non-GCB cases (62%). Of the cytogenetically “normal” cases, 35% of the GCB and 63% of the non-GCB expressed Bcl-2 protein. BCL2 Probe Result GCB(55) Non-GCB(39) n Bcl2 protein + n Bcl2 protein + t(14;18)(q32;21) 24 18 0 0 ⇑ Gene Copy# 8 6 15 14 “Normal” 23 8 24 15 Conclusions: We conclude that multiple mechanisms are responsible for Bcl-2 expression in DLBCL. Non-GCB cases do not harbor the t(14;18), more commonly have isolated BCL2 gene copy number gain and have a higher percentage of cytogenetically “normal” BCL2 protein-positive cases. The latter finding suggests a prominent role for transcriptional up-regulation of the BCL2 gene resulting from constitutive activation of NF- κB. DLBCL cases with a t(14;18) are always GCB and never show isolated BCL2 gene copy number gains, suggesting that these two events are mutually exclusive. The impact of other mechanisms (e.g. epigenetic changes, promoter hypomethylation) deregulating BCL2 expression requires further study. Bcl-2 protein expression and cell-of-origin (GCB vs non-GCB) are predictive biomarkers in 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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".