PIM serine/threonine kinases in the pathogenesis and therapy of hematologic malignancies and solid cancers
Bibliographic record
Abstract
The identification as cooperating targets of Proviral Integrations of Moloney virus in murine lymphomas suggested early on that PIM serine/threonine kinases play an important role in cancer biology. Whereas elevated levels of PIM1 and PIM2 were mostly found in hematologic malignancies and prostate cancer, increased PIM3 expression was observed in different solid tumors. PIM kinases are constitutively active and their activity supports in vitro and in vivo tumor cell growth and survival through modification of an increasing number of common as well as isoform-specific substrates including several cell cycle regulators and apoptosis mediators. PIM1 but not PIM2 seems also to mediate homing and migration of normal and malignant hematopoietic cells by regulating chemokine receptor surface expression. Knockdown experiments by RNA interference or dominant-negative acting mutants suggested that PIM kinases are important for maintenance of a transformed phenotype and therefore potential therapeutic targets. Determination of the protein structure facilitated identification of an increasing number of potent small molecule PIM kinase inhibitors with in vitro and in vivo anticancer activity. Ongoing efforts aim to identify isoform-specific PIM inhibitors that would not only help to dissect the kinase function but hopefully also provide targeted therapeutics. Here, we summarize the current knowledge about the role of PIM serine/threonine kinases for the pathogenesis and therapy of hematologic malignancies and solid cancers, and we highlight structural principles and recent progress on small molecule PIM kinase inhibitors that are on their way into first clinical trials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".