Analysis of predictive factors for POD24 in patients with previously untreated mantle cell lymphoma receiving bendamustine-rituximab with or without acalabrutinib in the phase 3 ECHO trial
Bibliographic record
Abstract
Abstract Introduction In mantle cell lymphoma (MCL), clinical progression of disease within 24 months of treatment initiation (POD24) is associated with poorer outcomes. Acalabrutinib plus bendamustine-rituximab (ABR) has been approved for patients with treatment-naive (TN) MCL based on the results of the phase 3 ECHO trial (NCT02972840), in which ABR significantly improved progression-free survival (PFS) versus placebo plus BR (PBR; hazard ratio [HR] 0.73; 95% confidence interval [CI] 0.57–0.94; P=0.0160). Here, we searched for clinical and biological factors predicting POD24 in the ECHO trial. Methods Patients aged ≥65 years with TN MCL and Eastern Cooperative Oncology Group (ECOG) performance status ≤2 were randomly assigned 1:1 to receive ABR or PBR. Randomization was stratified according to geographic region and simplified MCL International Prognostic Index (sMIPI) score. BR was given for 6 cycles followed by rituximab maintenance for 2 years in patients achieving a partial or complete response (CR). Acalabrutinib (100 mg twice daily) or placebo was administered at study start until progressive disease (PD) or unacceptable toxicity. Crossover to acalabrutinib was permitted at PD. POD24 was defined as patients who progressed within the first 24 months after randomization. Patients who withdrew from the study or were lost to follow-up before 24 months were excluded. Individual and multivariate logistic regression models were used to evaluate the association between POD24 and clinical or biological factors at baseline, including treatment modality. The initial saturated multivariate logistic regression model included parameters that were significant based on individual logistic regression models and were of clinical interest. A backwards selection process with threshold P<0.05 was used to generate the final multivariate logistic regression model. TP53 mutation status was excluded from multivariate logistic regression analyses due to a high proportion of missing data. Results At the data cutoff used in this analysis (February 15, 2024), median time on study was 44.9 (range, 0.03–81.3) months. Of 598 total patients, 493 were assessed for POD24, of which 152 (30.8%) had POD <24 months. At baseline, a higher proportion of patients with POD <24 months (vs POD >24 months) were aged ≥75 years (35.5% vs 22.9%), were male (81.6% vs 66.3%), had Ann Arbor stage IV disease (92.1% vs 82.7%), had blastoid/pleomorphic histology (22.4% vs 9.4%), had Ki-67 ≥30% (60.5% vs 43.4%), had lactate dehydrogenase (LDH) > upper limit of normal (ULN; 36.2% vs 11.4%), and had TP53 mutation (16.4% vs 5.6%). A higher proportion of patients had early progression with PBR (36%; n=90/249) versus ABR (25%; n=62/244). As determined by individual logistic regression analyses (P<0.05), the odds of POD <24 months were increased with baseline covariates of age ≥75, male sex, ECOG performance status ≥1, bulky disease ≥5 cm, Ann Arbor stage IV, extranodal disease, blastoid/pleomorphic histology, Ki-67 ≥30%, high-risk sMIPI score (6–11), LDH >ULN, decreasing hemoglobin concentration, increasing absolute neutrophil count, increasing absolute lymphocyte count, and TP53 mutation. Treatment with acalabrutinib reduced odds of early progression by 40%. Parameters that retained their predictive role (increased odds of early progression) after performing multivariate logistic regression analyses (P<0.05) included age ≥75 (odds ratio [OR] 1.95; 95% confidence interval [CI] 1.20–3.17), male sex (OR 2.78; 95% CI 1.59–4.88), Ann Arbor stage IV disease (OR 2.95; 95% CI 1.38–6.32), Ki-67 ≥30% (OR 1.77; 95% CI 1.09–2.86), LDH >ULN (OR 3.44; 95% CI 2.00–5.94), and blastoid/pleomorphic histology (OR 1.94; 95% CI 1.02–3.70). After adjustment for predictors of POD24, treatment with acalabrutinib significantly reduced the odds of early progression versus placebo (acalabrutinib vs placebo OR 0.58; 95% CI 0.37–0.91). Conclusion In this analysis of the ECHO trial, nonmodifiable predictive factors that increased odds of early progression (POD24) were age ≥75, male sex, Ann Arbor stage IV disease, Ki-67 ≥30%, LDH >ULN, and blastoid/pleomorphic histology. Treatment with acalabrutinib reduced odds of POD24.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".