Tumor-Associated Macrophages and Survival in Classic Hodgkin's Lymphoma
Bibliographic record
Abstract
BACKGROUND: Despite advances in treatments for Hodgkin's lymphoma, about 20% of patients still die from progressive disease. Current prognostic models predict the outcome of treatment with imperfect accuracy, and clinically relevant biomarkers have not been established to improve on the International Prognostic Score. METHODS: Using gene-expression profiling, we analyzed 130 frozen samples obtained from patients with classic Hodgkin's lymphoma during diagnostic lymph-node biopsy to determine which cellular signatures were correlated with treatment outcome. We confirmed our findings in an independent cohort of 166 patients, using immunohistochemical analysis. RESULTS: Gene-expression profiling identified a gene signature of tumor-associated macrophages that was significantly associated with primary treatment failure (P=0.02). In an independent cohort of patients, we found that an increased number of CD68+ macrophages was correlated with a shortened progression-free survival (P=0.03) and with an increased likelihood of relapse after autologous hematopoietic stem-cell transplantation (P=0.008), resulting in shortened disease-specific survival (P=0.003). In multivariate analysis, this adverse prognostic factor outperformed the International Prognostic Score for disease-specific survival (P=0.003 vs. P=0.03). The absence of an elevated number of CD68+ cells in patients with limited-stage disease defined a subgroup of patients with a long-term disease-specific survival of 100% with the use of current treatment strategies. CONCLUSIONS: An increased number of tumor-associated macrophages was strongly associated with shortened survival in patients with classic Hodgkin's lymphoma and provides a new biomarker for risk stratification.
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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".