Cancer Stem Cells and Novel Targets for Antitumor Strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cancer stem cells (CSCs) were identified in human leukemias in landmark studies of John Dick and his colleagues. Subsequently, similar cancer stem-like cells were identified in solid tumors of the breast, colon, brain and other sites. CSCs have distinct markers and are highly tumorigenic compared to other subsets. They can differentiate into all the cell phenotypes of the parental tumor. Other key features include activation of pluripotency genes (Oct4, Sox2, Nanog), self-renewal, formation of tumor spheres in low-adherence cultures, and multi-drug resistance. Clinically, drug resistance is probably the most important feature, because CSCs resist conventional cancer therapies and are likely to play a major role in cancer relapse. Based on their properties, several molecules have been targeted for therapy with drugs as follows. 1) The self-renewal pathways Wnt/β-catenin, Hedgehog and Notch. 2) The aryl hydrocarbon receptor (AHR), with tranilast and other AHR agonists. 3) Cytokines and inflammatory pathways (e.g., IL-6, IL-8, NF-κB). 4) TGF-β and epithelial- to-mesenchymal transition (EMT) pathways. 5) Homing molecules involved in metastasis; most notably CXCR4 or its ligand CXCL12. 6) Growth factors, their receptors and coreceptors (such as neuropilin-1), and signaling components (e.g., tyrosine kinases). 7) Cell-surface markers (CD44 and integrins). Several drugs have been identified by screening or other observations (salinomycin, metformin, tesmilifene, sulforaphane, curcumin, piperine and others). Some of these drugs are at preclinical or early clinical phases of development, and it remains to be seen how many will progress to clinical application. This review focuses on some promising new developments in anti-CSC drug therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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 it