Mantle cell lymphoma: Toll-like receptors (TLRs) and B-cell receptor (BCR) gene expression analysis for identification of a distinct BCR-activated subset, with germinal center signature and indolent biology.
Bibliographic record
Abstract
8585 Background: Mantle cell lymphoma (MCL) is an aggressive disease with complex biology. Enhanced understanding of pathogenesis can result in better utilization of novel therapies. MCL is believed to arise from naïve B-lymphocytes; however, there are indications that at least a subset of MCL arises from antigen-experienced B cells; where Toll-like Receptors (TLRs) and B-cell receptor (BCR) play an intricate role. Methods: Here, we report Gene Expression Profile (GEP) data on TLRs/BCR related genes and associated down stream pathways (154 gene-set) in a cohort (n=81) of MCL patients (pts.). GEP was assessed by Nano string technology utilizing mRNA from diagnostic biopsy tissue. Results: Hierarchical clustering based on TLR/BCR genes (n= 18), revealed four distinct clusters (A, n=12; B, n=30; C, n=19 and D, n=20) with differential expression of CD79b, SYK, LYN; BTK (p<0.002) and TLRs (2,3,6,7,8,9,) (p<0.01). On further analysis, Cluster C revealed higher expression of Germinal Centre (GC) associated genes (GCET, LMO2 and HGAL) (p<0.001). This cluster (C) showed lower expression of PI3K/Akt pathway (p<0.005) and MAP/ERK pathway associated genes (p<0.009) as compared to cluster B. Patients in cluster C, had a trend toward better overall survival (OS), in comparison with cluster B &D (p=0.07)(Log Rank (Mantel-Cox). Conclusions: Our data suggest that MCL pts. can be stratified based on BCR activity and these pts. can benefit from array of BCR inhibitors as second line therapy. A subset of MCL shows exposure to GC microenvironment, low PI3K/Akt and MAP/ERK pathway activity and a trend towards a better overall survival.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".