A 7-Gene Microrna Signature Characteristic of Mantle Cell Lymphoma Reveals Focal Adhesion and Integrin Signalling, Proteasome-Mediated Degradation, and the PI3K Signalling Cascade As Important to MCL Pathogenesis
Bibliographic record
Abstract
Abstract Abstract 1586 Background Mantle cell lymphoma (MCL) is a subtype of B-cell non-Hodgkin lymphoma (NHL) characterized by the t(11;14) translocation and concomitant over-expression of cyclin D1. MCL has a variable natural history; while some patients have prolonged survival similar to other indolent B-cell lymphomas, most follow an aggressive course with short survival. While the t(11;14) is pathognomonic of MCL, it is not necessary for disease pathogenesis, as a subset of MCL cases lack the translocation. Furthermore, in vivo models demonstrate that cyclin D1 over-expression alone is unable to bring about the disease, and that deregulation of additional cellular pathways is required for its pathogenesis. Assessment of microRNA (miR) expression in MCL may help determine mechanisms of gene deregulation and reveal pathways involved in disease pathogenesis. In this study we examined MCL in relation to both aggressive and indolent B-cell NHL to determine a miR signature that characterizes MCL. Design and Methods Total RNA from a training set of 36 B-cell NHL cases (19 indolent and 17 aggressive) and 32 MCL was applied to a high-throughput quantitative real-time PCR platform assessing the expression of 365 miRs [TaqMan Human MicroRNA Array v1.0 (Early Access) or TLDA]. miRs that were differentially expressed between MCL and aggressive NHL, and between MCL and indolent NHL were then validated using RNA from a second, independent, set of B-cell NHL cases (28 indolent and 28 aggressive) and 50 MCL cases. Validated miRs were determined and potential targets for each miR were examined. A map of targets common to the MCL miR signature was created, revealing important proteins involved in MCL pathogenesis. Results 66 miRs (11 over-expressed, 55 under-expressed) were differentially expressed between MCL and aggressive B-cell NHL and 8 miRs (7 over-expressed, 1 under-expressed) were differentially expressed between MCL and indolent B-cell NHL (false discovery rate = 0.2). 6 miRs from each group were chosen for validation in an independent set of MCL and NHL cases. Of these 12 miRs, 7 miRs validated (2 were under-expressed in MCL relative to aggressive B-cell NHL, and 5 were over-expressed in MCL relative to indolent B-cell NHL). Genes and pathways involved in disease pathogenesis are most likely targeted by multiple miRs. We thus determined a set of 123 genes predicted to be targets of this MCL miR signature, based on five miR target prediction databases from the mirDIP (microRNA data integration) portal. These genes were significantly enriched for focal adhesion and integrin signalling, proteasome-mediated degradation, and the PI3K signalling pathway. Conclusions Using the largest set of MCL cases evaluated to date, a 7-miR signature characteristic of MCL was discovered. The gene targets of these miRs are enriched for roles in proteasome-mediated protein degradation, consistent with the reported sensitivity of MCL to proteasome inhibitors. In addition, these miRs are predicted to be involved in regulation of PI3K/AKT signalling, confirming reports of the importance of this pathway in MCL pathogenesis. Enrichment of target genes involved in focal adhesion and integrin signalling indicate the importance of MCL-stromal interactions and motivates further study into the role of the tumor microenvironment in MCL pathogenesis. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.000 | 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) | 0.001 | 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".