Structural profiles of TP53 gene mutations predict clinical outcome in diffuse large B-cell lymphoma: an international collaborative study
Bibliographic record
Abstract
The purpose of this study is to correlate the presence of TP53 gene mutations with the clinical outcome of a cohort of patients with diffuse large B-cell lymphoma (DLBCL) assembled from 12 medical centers. TP53 mutations were identified in 102 of 477 patients, and the overall survival (OS) of patients with TP53 mutations was significantly worse than those with wild-type TP53 (P < .001). However, subsets of TP53 mutations were found to have different effects on OS. Mutations in the TP53 DNA-binding domains were the strongest predictors of poor OS (P < .001). Mutations in the Loop-Sheet-Helix and Loop-L3 were associated with significantly decreased OS (P = .002), but OS was not significantly affected by mutations in Loop-L2. A subset of missense mutations (His158, His175, Ser245, Gln248, His273, Arg280, and Arg282) in the DNA-binding domains had the worst prognosis. Multivariate analysis confirmed that the International Prognostic Index and mutations in the DNA-binding domains were independent predictors of OS. TP53 mutations also stratified patients with germinal center B cell-like DLBCL, but not nongerminal center B cell-like DLBCL, into molecularly distinct subsets with different survivals. This study shows the prognostic importance of mutations in the TP53 DNA-binding domains in patients with 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) | 0.000 | 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 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".