Genomic alterations underlying immune privilege in malignant lymphomas
Bibliographic record
Abstract
PURPOSE OF REVIEW: Malignant lymphomas represent a remarkably heterogeneous group of cancers with respect to their oncogenome, phenotype and clinical presentation. Lymphoma cells benefit from limited immune surveillance and have developed various mechanisms to alter antitumor immune responses. This article summarizes our current knowledge about genomic alterations underlying acquired immune privilege in lymphoid cancers. RECENT FINDINGS: The implementation and broad application of next-generation sequencing techniques have significantly expanded our knowledge about genetic alterations and perturbed cellular pathways underlying lymphomagenesis. Based on key discoveries in the past decade, the purview of subsequent studies expanded beyond the biology of the lymphoma cells to include the pathogenic contribution of immune cells, stromal components and associated crosstalk between malignant and nonmalignant cells in the tumor microenvironment. A number of genetic mechanisms have been described that elucidate how lymphoma cells are selected for evading immune recognition and reprogramming immune responses. These prominently include structural genomic changes of the CIITA and programmed death ligand (CD274/PDCD1LG2) loci, alterations affecting antigen presentation and mutations in JAK-STAT and NFκB signaling pathways. SUMMARY: Further investigations will foster our understanding about synergy of immune escape mechanisms, and lay the foundation for the development of predictive biomarkers in the context of conceptually novel therapies targeting microenvironment-related biology, such as immunological checkpoint inhibition.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".