pY-STAT3 and p53 expression predict outcome for poor prognosis diffuse large B-cell lymphoma treated with high dose chemotherapy and autologous stem cell transplantation
Bibliographic record
Abstract
The purpose of this study was to evaluate the ability of biomarkers to predict 5-year event-free survival (EFS) of poor prognosis patients with diffuse large B-cell lymphoma (DLBCL) who were treated on a prospective clinical trial with upfront high dose chemotherapy (HDCT) and autologous stem cell transplantation (ASCT). We previously reported 51 patients with DLBCL treated with one cycle each of cyclophosphamide, doxorubicin, vincristine, prednisone (CHOP), dose-intensive cyclophosphamide, etoposide, cisplatin (DICEP), and carmustine, etoposide, Ara-C, and melphalan (BEAM)/ASCT. Of these patients, 33 had DLBCL and suitable tissue for immunohistochemical (IHC) biomarker evaluation. We found no statistically significant association between EFS and age adjusted International Prognostic Index (IPI) score, bulk over 10 cm, germinal center B-cell phenotype, or expression of BCL-2 or BCL-6 biomarkers. However, the detection of pY-STAT3 expression was associated with improved 5-year EFS (93%versus 47%, p = 0.006), and p53 expression was associated with lower 5-year EFS (47%versus 83%, p = 0.025). Predictive ability was improved by combining pY-STAT3 and p53 expression. Specifically, 5-year EFS rates were 93% for pY-STAT3+, 77% for pY-STAT3-/p53-, and 20% for pY-STAT3-/p53+ patients (p = 0.0002). In conclusion, pY-STAT3 and p53 expression may help predict outcome of HDCT for DLBCL, and further study of these biomarkers is warranted.
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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) | 0.000 | 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".