Randomized, Double-Blind, Phase III Trial of Enzastaurin Versus Placebo in Patients Achieving Remission After First-Line Therapy for High-Risk Diffuse Large B-Cell Lymphoma
Bibliographic record
Abstract
PURPOSE: To compare disease-free survival (DFS) after maintenance therapy with the selective protein kinase C β (PKCβ) inhibitor, enzastaurin, versus placebo in patients with diffuse large B-cell lymphoma (DLBCL) in complete remission and with a high risk of relapse after first-line therapy. PATIENTS AND METHODS: This multicenter, phase III, randomized, double-blind, placebo-controlled trial enrolled patients who were at high risk of recurrence after rituximab-cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). Patients (N = 758) with stage II bulky or stage III to IV DLBCL, three or more International Prognostic Index risk factors at diagnosis, and a complete response or unconfirmed complete response after 6 to 8 cycles of R-CHOP were assigned 2:1 to receive oral enzastaurin 500 mg daily or placebo for 3 years or until disease progression or unacceptable toxicity. Primary end point was DFS 3 years after the last patient entered treatment. Correlative analyses of biomarkers, including cell of origin by immunohistochemistry and PKCβ expression, with efficacy outcomes were exploratory objectives. RESULTS: After a median follow-up of 48 months, DFS hazard ratio for enzastaurin versus placebo was 0.92 (95% CI, 0.689 to 1.216; two-sided log-rank P = .541; 4-year DFS, 70% v 71%, respectively). Independent of treatment, no significant associations were observed between PKCβ protein expression or cell of origin and DFS or overall survival. CONCLUSION: Enzastaurin did not significantly improve DFS in patients with high-risk DLBCL after achieving complete response to R-CHOP. Achievement of a complete response may have abrogated the prognostic significance of cell of origin by immunohistochemistry.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".