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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".