Multi-cohort gene expression model enhances prognostic stratification in diffuse large B-cell lymphoma
Bibliographic record
Abstract
Multi-cohort gene expression model enhances prognostic stratification in diffuse large B-cell lymphomaDear Editor, Diffuse large B-cell lymphoma (DLBCL), the most common type of lymphoma, in most cases is marked by significant heterogeneity and aggressive clinical behavior.While standard chemotherapy often achieves initial responses, these are short-lived, and resistance and relapse are frequent challenges. 1 Traditionally, risk stratification has relied on clinical tools, including the International Prognostic Index (IPI) and its variation.2 However, molecular stratification is promising to predict outcomes with greater accuracy, though gene-based approaches are still preliminary.3 Progress in this field is hindered by limited sample sizes and the substantial intra-and inter-regional variability of DLBCL.4,5 Consequently, large-scale studies are essential to refine risk stratification and optimize patient outcomes.This study aimed to establish a prognostic gene expression signature for patients with DLBCL based on tumor transcriptome patterns.To achieve this, we analyzed transcriptome and survival data from 11 diverse cohorts worldwide.Given the variability in RNA sequencing or microarray platforms across the 11 datasets, we focused on the genes common to all datasets, resulting in a panel of 11,425 genes.Detailed information regarding the datasets can be found in Supplementary Table 1.Due to platform-specific differences in scale, the gene expression values were transformed into z-scores.Datasets with fewer than 100 patients were combined into a cohort referred to as the Merged Cohort.In total, six cohorts were used in this study: the National Cancer Institute Cohort (GSE10846), University of York Cohort (GSE181063), University of York II Cohort (GSE32918), Univer-sit€ atsmedizin Berlin Cohort (GSE4475), University of Leeds Cohort (GSE69053), and the Merged Cohort (GSE69053, E_TABM_346, GSE11318, GSE21846, GSE23501, GSE57611, and TCGA-DLBC).For each cohort, a univariate Cox regression was performed employing all genes in the panel, identifying those with a p-value <0.05 as prognostic.Genes were defined as core prognostic genes (CPGs) if they consistently predicted either favorable prognosis in at least 5 out of 6 cohorts or unfavorable prognosis in at least 5 out of 6 cohorts, with no conflicting outcomes.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".