PIM kinases are progression markers and emerging therapeutic targets in diffuse large B-cell lymphoma
Bibliographic record
Abstract
BACKGROUND: PIM serine/threonine kinases are often highly expressed in haematological malignancies. We have shown that PIM inhibitors reduced the survival and migration of leukaemic cells. Here, we investigated PIM kinases in diffuse large B-cell lymphoma (DLBCL) biopsy samples and DLBCL cell lines. METHODS: Immunohistochemical staining for PIM kinases and CXCR4 was performed on tissue microarrays from a cohort of 101 DLBCL cases, and the effects of PIM inhibitors on the survival and migration of DLBCL cell lines were determined. RESULTS: PIM1 expression significantly correlated with the activation of signal transducer and activator of transcription (STAT) 3 and 5, P-glycoprotein expression, CXCR4-S339 phosphorylation, and cell proliferation. Whereas most cases exhibited cytoplasmic or cytoplasmic and nuclear PIM1 and PIM2 expression, 12 cases (10 of the non-germinal centre DLBCL type) expressed PIM1 predominately in the nucleus. Interestingly, nuclear expression of PIM1 significantly correlated with disease stage. Exposure of DLBCL cell lines to PIM inhibitors modestly impaired cellular proliferation and CXCR4-mediated migration. CONCLUSION: This work demonstrates that PIM expression in DLBCL is associated with activation of the JAK/STAT signalling pathway and with the proliferative activity. The correlation of nuclear PIM1 expression with disease stage and the modest response to small-molecule inhibitors suggests that PIM kinases are progression markers rather than primary therapeutic targets in DLBCL.
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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 | 0.001 |
| 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".