Prediction of Survival In Diffuse Large B-Cell Lymphoma Based On the Expression of Two Genes Reflecting Tumor and Microenvironment
Bibliographic record
Abstract
Abstract Abstract 2006 Background: Several gene expression signatures predict survival in diffuse large B cell lymphoma (DLBCL), but the lack of practical methods for genome scale analysis has limited translation to clinical practice. Methods: We examined the power of individual genes to predict survival across different therapeutic eras. In studying 787 patients with DLBCL, we built and validated a simple model employing one gene expressed by tumor cells and another expressed by host immune cells, assessing added prognostic value to the clinical International Prognostic Index (IPI). We validated models in an independent cohort using diagnostic formalin-fixed specimens. Results: We verified expression of LMO2 as an independent predictor of survival and ‘Germinal Center B-cell’ subtype. We identified expression of TNFRSF9 from the tumor microenvironment, as the best in bivariate combination with LMO2. We studied distribution of TNFRSF9 tissue expression in 95 patients. A model integrating these two genes (TGS) was independent of ‘cell of origin’ classification, ‘stromal signatures’, IPI, and added to the predictive power of the IPI. This bivariate model and a composite score integrating the IPI (TGS-IPI) performed well in three independent cohorts of 545 previously described patients. Both models robustly stratified outcomes in a simple assay of routine specimens from 147 newly diagnosed patients as depicted. Conclusion: Measurement of a single gene expressed by tumor cells (LMO2) and a single gene expressed by the immune microenvironment (TNFRSF9) powerfully predicts overall survival in patients with DLBCL. A simple test integrating these two genes with the IPI can readily be used to select patients of different risk groups for clinical trials. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".