Structural Profiles of p53 Gene Mutations Predict Clinical Outcome in Diffuse Large B-Cell Lymphoma: An International Collaborative Study.
Bibliographic record
Abstract
Abstract Mutations of the p53 tumor suppressor gene have been associated with a poor clinical outcome in some series of diffuse large B-cell lymphoma (DLBCL). However, conflicting results have been reported in other studies. The purpose of this study was to analyze the p53 mutations in DLBCL from twelve centers, and to correlate the structural profiles of the mutations with clinical outcome. The p53 mutations were identified in 102 of 477 cases of DLBCL for a frequency of 21.4%. These included 92 missense mutations, 5 nonsense mutations, 4 deletions, and 1 insertion. The presence of any p53 mutation correlated with poor overall survival (OS; P=0.002). Sixty-two of 102 cases (61%) had mutations in the DNA binding domains of the p53 gene, and the mutations in the DNA-binding domains were found to be the most important predictor of poor OS (P<0.001). In contrast, mutations in the non-DNA binding domains did not correlate with OS (P=0.158). The 5-year survival rate was 24% in patients with any p53 mutation (median survival=1.33 yr) and 19% of patients with the DNA-binding domain mutations (median survival=1.0 yr) compared to 41% for those with wild type p53 (wt-p53, median survival=4.5 yr). The complete remission rate was 57% in patients with any p53 mutation and 54% in patients with the DNA-binding domain mutations compared to 69% for those with wt-p53. Of the mutations in the DNA-binding domains, patients with mutations in the loop-sheet-helix motifs (Loop L1-S10-H2, 20% of all mutations) and DNA minor binding groove motif (Loop L3, 22% of all mutations) had significantly decreased OS (P=0.002). In contrast, OS was not significantly decreased for patients with mutations in Loop L2 (18% of all mutations). Multivariate analysis confirmed that the International Prognostic Index, age, tumor size, serum lactate dehydrogenase, and mutations in the DNA binding motifs were independent predictors of OS. The p53 mutation profile was also found to stratify germinal center B-cell-like DLBCL, but not activated B-cell-like DLBCL, into molecularly distinct subsets with different clinical outcomes. This study demonstrates the importance of the mutational profile in the DNA binding domains of the p53 gene for predicting clinical outcome and refining current prognostic models including gene expression profiling in patients with DLBCL.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".