Genomic and microenvironmental landscape of stage I follicular lymphoma, compared with stage III/IV
Bibliographic record
Abstract
Although the genomic and immune microenvironmental landscape of follicular lymphoma (FL) has been extensively investigated, little is known about the potential biological differences between stage I and stage III/IV disease. Using next-generation sequencing and immunohistochemistry, 82 FL nodal stage I cases were analyzed and compared with 139 FL stage III/IV nodal cases. Many similarities in mutations, chromosomal copy number aberrations, and microenvironmental cell populations were detected. However, there were also significant differences in microenvironmental and genomic features. CD8+ T cells (P = .02) and STAT6 mutations (false discovery rate [FDR] <0.001) were more frequent in stage I FL. In contrast, programmed cell death protein 1-positive T cells, CD68+/CD163+ macrophages (P < .001), BCL2 translocation (BCL2trl+) (P < .0001), and KMT2D (FDR = 0.003) and CREBBP (FDR = 0.04) mutations were found more frequently in stage III/IV FL. Using clustering, we identified 3 clusters within stage I, and 2 clusters within stage III/IV. The BLC2trl+ stage I cluster was comparable to the BCL2trl+ cluster in stage III/IV. The two BCL2trl- stage I clusters were unique for stage I. One was enriched for CREBBP (95%) and STAT6 (64%) mutations, without BLC6 translocation (BCL6trl), whereas the BCL2trl- stage III/IV cluster contained BCL6trl (64%) with fewer CREBBP (45%) and STAT6 (9%) mutations. The other BCL2trl- stage I cluster was relatively heterogeneous with more copy number aberrations and linker histone mutations. This exploratory study shows that stage I FL is genetically heterogeneous with different underlying oncogenic pathways. Stage I FL BCL2trl- is likely STAT6 driven, whereas BCL2trl- stage III/IV appears to be more BCL6trl driven.
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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 | 0.001 | 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) | 0.002 | 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".