Hypoxia-Inducible Factor-1 α Expression Predicts Superior Survival in Patients With Diffuse Large B-Cell Lymphoma Treated With R-CHOP
Bibliographic record
Abstract
PURPOSE Hypoxia-inducible factor (HIF) controls the expression of genes in response to hypoxia, as well as a wide range of other cellular processes. We previously showed constitutive stabilization of HIF-1alpha in the majority of patients with diffuse large B-cell lymphoma (DLBCL). To our knowledge, the prognostic significance of HIF in lymphoma has never been investigated. PATIENTS AND METHODS We studied the immunohistochemical protein expression of HIF-1alpha on tissue microarrays from 153 patients with DLBCL treated in sequential cohorts with cyclophosphamide, doxorubicin, oncovin, and prednisone (CHOP) or rituximab-CHOP (R-CHOP) from 1999 to 2002. Results were correlated with patient outcome. Results Median follow-up for all patients was 80 months. Among all patients, HIF-1alpha was expressed in 62% of germinal center and 59% of non-germinal center patients. With HIF-1alpha analyzed as a dependent variable, there were no survival differences in CHOP-treated patients. In the R-CHOP group, however, HIF-1alpha protein expression correlated with significantly improved progression-free survival (PFS) and overall survival (OS). Five-year PFS for HIF-1alpha-positive patients was 71% v 43% for HIF-1alpha-negative patients (P = .0187), whereas 5-year OS was 75% and 54%, respectively (P = .025). In multivariate analysis with International Prognostic Index criteria, HIF-1alpha remained a significant predictor for PFS (P = .026) and OS (P = .043). Compared with other biomarkers, HIF-1alpha correlated only with BCL6 (P = .004). In terms of gene expression, we found several common gene associations of HIF-1alpha and the stromal-1 signature with genes predominantly involved in regulation of the extracellular matrix (eg, BGN, COL1A2, COL5A1, and PLOD2). CONCLUSION The expression of HIF-1alpha protein is an important independent favorable prognostic factor for survival in patients with DLBCL treated with R-CHOP.
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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 | 0.000 | 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) | 0.001 | 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".