The classical Hodgkin lymphoma tumor microenvironment: macrophages and gene expression-based modeling
Bibliographic record
Abstract
Despite the high cure rate in classical Hodgkin lymphoma (CHL), more accurate tailoring of upfront treatment is required to maximize cure while avoiding unnecessary short- and long-term treatment side effects. To this end, the unique tumor microenvironment of CHL has been searched extensively for prognostic biomarkers. Beyond targeted immunohistochemistry (IHC) studies, gene expression profiling (GEP) of diagnostic whole tissue biopsies has allowed a de novo approach to biomarker discovery. Among numerous candidate biomarkers, an association between the number of tumor-associated macrophages in the microenvironment and outcomes after ABVD (doxorubicin + bleomycin + vinblastine + dacarbazine) chemotherapy emerged, and multiple subsequent studies have validated this biological relationship using IHC. These studies have also defined key aspects for macrophage interrogation, including the characteristics of the CD68 and CD163 antibodies, appropriate scoring methodologies, and the identification of specific patient populations in which macrophage IHC may not be prognostic. The GEP studies also led to the development of gene expression-based prognostic models for advanced-stage CHL, with new technologies allowing reliable gene expression quantitation using RNA from routinely produced formalin-fixed paraffin-embedded biopsies. The bridge to predictive biomarkers that can be used reliably to inform upfront treatment selection requires further studies to demonstrate that these biomarkers can identify robustly, at diagnosis, patients at high risk of treatment failure with ABVD and that this risk may be overcome using alternative treatments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".