Bibliographic record
Abstract
Mantle cell lymphoma (MCL) is an aggressive disease with frequent relapse. Targeted therapies against B-cell receptor (BCR) molecules have demonstrated improved outcomes in relapsed cases. However, clinical responses are slow and selective, with failure to attain complete remission in a significant subset of patients. Complex interaction of BCR signal transduction with toll-like receptor (TLR) and other pathways in MCL remains unknown, thus averting progress in development of targeted therapies. We have performed detailed digital quantification of BCR/TLR signalling molecules and their effector pathways in a cohort (n = 81) of MCL patients and correlated these data with overall survival. Hierarchical clustering model based on BCR/TLR genes revealed two distinct (BCR<sup>high</sup> and BCR<sup>low</sup> ) subsets of patients (n = 32; 40%) with significant differences in expression (>1.5-fold change; p < 0.05). Higher levels of BTK/SYK/BLNK/CARD11/PLCG signalosome and lower expression of MALT1/BCL10 genes suggested tonic pattern of BCR activation. Amplified expression of TLR6/TLR7/TLR9 was noted in concert with hyper-responsiveness of BCR machinery. MYD88, a key TLR adaptor molecule, was not upregulated in any of these clusters, which may suggest a 'cross-talk' between BCR and TLR pathways. In sync with BCR/TLR signalling, we recorded significantly enhanced expression of genes associated with NF-kB pathway in BCR<sup>high</sup> subset of MCL patients. On univariate analysis, the BCR<sup>high</sup> patients showed a trend towards inferior clinical response to a standardized treatment protocol, compared with the BCR<sup>low</sup> group (log rank, p = 0.043). In conclusion, we have identified hyperactive BCR/TLR signalling pathways and their effector downstream targets in a subset of MCL patients and associated it with poor clinical outcomes. Our study provides quantitative evidence at RNA expression level of possible concomitant collaboration between TLR and BCR signalling molecules in MCL. These data will provide further insights for future functional studies and, hence, development of targeted therapies for MCL patients. Copyright © 2015 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".