Paraffin-based 6-gene model predicts outcome in diffuse large B-cell lymphoma patients treated with R-CHOP
Bibliographic record
Abstract
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease characterized by variable clinical outcomes. Outcome prediction at the time of diagnosis is of paramount importance. Previously, we constructed a 6-gene model for outcome prediction of DLBCL patients treated with anthracycline-based chemotherapies. However, the standard therapy has evolved into rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP). Herein, we evaluated the predictive power of a paraffin-based 6-gene model in R-CHOP-treated DLBCL patients. RNA was successfully extracted from 132 formalin-fixed paraffin-embedded (FFPE) specimens. Expression of the 6 genes comprising the model was measured and the mortality predictor score was calculated for each patient. The mortality predictor score divided patients into low-risk (below median) and high-risk (above median) subgroups with significantly different overall survival (OS; P = .002) and progression-free survival (PFS; P = .038). The model also predicted OS and PFS when the mortality predictor score was considered as a continuous variable (P = .002 and .010, respectively) and was independent of the IPI for prediction of OS (P = .008). These findings demonstrate that the prognostic value of the 6-gene model remains significant in the era of R-CHOP treatment and that the model can be applied to routine FFPE tissue from initial diagnostic biopsies.
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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) | 0.000 | 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 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".