Gene expression predicts overall survival in paraffin-embedded tissues of diffuse large B-cell lymphoma treated with R-CHOP
Bibliographic record
Abstract
Gene expression profiling (GEP) on frozen tissues has identified genes predicting outcome in patients with diffuse large B-cell lymphoma (DLBCL). Confirmation of results in current patients is limited by availability of frozen samples and addition of monoclonal antibodies to treatment regimens. We used a quantitative nuclease protection assay (qNPA) to analyze formalin-fixed, paraffin-embedded tissue blocks for 36 previously identified genes (N = 209, 93 chemotherapy; 116 rituximab + chemotherapy). By qNPA, 208 cases were successfully analyzed (99.5%). In addition, 15 of 36 and 11 of 36 genes, representing each functional group previously identified by GEP, were associated with survival (P < .05) in the 2 treatment groups, respectively. In addition, 30 of 36 hazard ratios of death trended in the same direction versus the original studies. Multivariate and variable cut-off point analysis identified low levels of HLA-DRB (< 20%) and high levels of MYC (> 80%) as independent indicators of survival, together distinguishing cases with the worst prognosis. Our results solve a clinical research problem by demonstrating that prognostic genes can be meaningfully quantified using qNPA technology on formalin-fixed, paraffin-embedded tissues; previous GEP findings in DLBCL are relevant with current treatments; and 2 genes, representing immune escape and proliferation, are the common features of the most aggressive 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 | 0.001 |
| 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) | 0.001 | 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".