Bmi1 – A Path to Targeting Cancer Stem Cells
Bibliographic record
Abstract
The Polycomb group (PcG) genes encode for proteins comprising two multiprotein complexes, Polycomb repressive complex 1 (PRC1) and Polycomb repressive complex 2 (PRC2). Although the initial discovery of PcG genes was made in Drosophila, as transcriptional repressors of homeotic (HOX) genes. Polycomb repressive complexes have been since implicated in regulating a wide range of cellular processes, including differentiation and self-renewal in normal and cancer stem cells. Bmi1, a subunit of PRC1, has been long implicated in driving self-renewal, the key property of stem cells. Subsequent studies showing upregulation of Bmi1 in several cancers correlated with increased aggressiveness, radioresistance and metastatic potential, provided rationale for development of targeted therapies against Bmi1. Although Bmi1 activity can be reduced through transcriptional, post-transcriptional and post-translational regulation, to date, the most promising approach has been through small molecule inhibitors targeting Bmi1 activity. The post-translational targeting of Bmi1 in colorectal carcinoma, lung adenocarcinoma, multiple myeloma and medulloblastoma have led to significant reduction of self-renewal capacity of cancer stem cells, leading to slower tumour progression and reduced extent of metastatic spread. Further value of Bmi1 targeting in cancer can be established through trials evaluating the combinatorial effect of Bmi1 inhibition with current ‘gold standard’ therapies.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".