Tumor-microenvironment and molecular biology of classic Hodgkin lymphoma in children, adolescents, and young adults
Bibliographic record
Abstract
Classic Hodgkin lymphoma (cHL) exhibits a bimodal age distribution with incidence peaks in adolescents and young adults (AYAs) aged 15-39 years and in older adults over 50 years. The unique biology of cHL, characterized by a tumor microenvironment (TME) composed predominantly of non-malignant immune and stromal cells, plays a pivotal role in supporting Hodgkin and Reed-Sternberg (HRS) cells, the malignant cells of cHL. Understanding the role of the TME in cHL and its age-related differences is crucial for deciphering differential disease etiologies and developing biomarker-driven targeted therapies. Recent technical advances in single-cell sequencing and multiplexed spatial imaging have revealed age-related differences in TME composition and function, including key cellular interactions, leading to the development of age-specific prognostic indicators. In addition, advances in our ability to isolate nucleic acids from HRS cells have accelerated our understanding of the molecular alterations in cHL, many of which drive interactions within the TME. Molecular differences in cHL between pediatric/AYA and older adult patients have also emerged. This review summarizes the unique biology of cHL and its TME in children, adolescents, and young adults, highlighting recent breakthroughs in our understanding of cHL biology, differences across the age spectrum, and advances in biomarker development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".