<scp>MYC</scp> and <scp>BCL</scp>2 protein expression predicts survival in patients with diffuse large <scp>B</scp>‐cell lymphoma treated with rituximab
Bibliographic record
Abstract
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease and "double-hit" DLBCL, with both MYC and BCL2 translocations has a poor prognosis. In this study, we investigated whether MYC and BCL2 protein expression in tissue would predict survival in DLBCL. The study included 106 cases of de novo DLBCL treated with rituximab and cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) or CHOP-like regimens. The results were validated on an independent cohort of 205 DLBCL patients. Patients with low expression of BCL2 (≤30%) and MYC (≤50%) had the best prognosis, whereas those with high BCL2 (>30%) and MYC (>50%) had the worst outcome. In multivariate analysis, the combination of the BCL2 and MYC was an independent predictor of overall survival (OS) and event-free survival (EFS) (P = 0·015 and P = 0·005, respectively). The risk of death was nine times greater for patients with high BCL2 and MYC compared to those with low expression. High BCL2 and MYC was a strong predictor of poor OS (P < 0·001) and EFS (P = 0·0017) in patients with the germinal centre B-cell (GCB) type, but not in the non-GCB type. In DLBCL, high co-expression of MYC and BCL2 was an independent predictor of poor survival, and could be used to stratify patients for risk-adapted therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".