The Hedgehog Pathway as a Therapeutic Target in Chronic Myeloid Leukemia
Bibliographic record
Abstract
Despite the development of therapeutic agents that selectively target cancer cells, relapse driven by acquired drug resistance and resulting treatment failure remains a significant issue. The highly conserved Hedgehog (HH) signaling pathway performs multiple roles in both development and tissue homeostasis, and its aberrant regulation is known to drive the pathogenesis of numerous human malignancies. However, the role of HH signaling in mediating disease progression and drug resistance remains unclear. This is especially true for myeloid malignancies. The HH pathway, and in particular the protein Smoothened (SMO), has been shown to be essential for regulating stem cell fate in chronic myeloid leukemia (CML). Evidence suggests that HH pathway activity is critical for maintaining the drug-resistant properties and survival of CML leukemic stem cells (LSCs), and that dual inhibition of BCR-ABL1 and SMO may comprise an effective therapeutic strategy for the eradication of these cells in patients. This review will explore the evolutionary origins of HH signaling, highlighting its roles in development and disease, which are mediated by canonical and non-canonical HH signaling. Development of small molecule inhibitors of HH signaling and clinical trials using these inhibitors as therapeutic agents in cancer and their potential resistance mechanisms, are also discussed, with a focus on CML.
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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 | 0.001 | 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) | 0.003 | 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".