Abstract PR5: Prediction of survival in diffuse large B-cell lymphoma based on the expression of two genes integrating tumor and microenvironment
Bibliographic record
Abstract
Abstract Several gene expression signatures are predictive of prognosis in diffuse large B cell lymphoma (DLBCL), but the lack of practical methods for a genome scale analysis has restricted their clinical applicability. Towards construction of a molecular predictor amenable to rapid testing on routinely obtained diagnostic clinical specimens, we studied genes previously reported to be associated with survival in DLBCL, testing and validating risk scoring models with robust survival associations in the current therapeutic era. We identified LMO2 expression as a robust univariate predictor of survival and cell of origin classification of DLBCL, with independent prognostic value. We examined bivariate models combining expression of LMO2 with other genes, and identified TNFRSF9 as a tumor microenvironment gene with independent prognostic influence. A combined model integrating both LMO2 and TNFRSF9 expression was independent of “cell of origin” classification, “stromal signatures,” International Prognostic Index (IPI), and added to the predictive power of IPI. A composite model was validated in multiple independent patient cohorts using public microarray data. Using routinely obtained formalin fixed, paraffin embedded diagnostic specimens from an independent cohort, we developed a simple assay validating the clinical utility of this 2-gene model, as well as a composite model integrating the IPI. In conclusion, measurement of a single gene expressed by tumor cells and a single gene expressed by the immune microenvironment is sufficient to predict overall survival in patients with DLBCL treated with R-CHOP. A combined model serves as a robust clinical risk assessment tool. This talk is also presented as Poster A27. Citation Information: Clin Cancer Res 2010;16(14 Suppl):PR5.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".