Targeting Cancer Stem Cells with Defined Compounds and Drugs
Bibliographic record
Abstract
Abstract : Cancer stem cells (CSCs) are a subpopulation of tumor cells that possess self-renewal and tumor initiation capacity and the ability to give rise to the heterogenous lineages of cancer cells that comprise the tumor. CSCs possess numerous intrinsic mechanisms of resistance to chemotherapeutic drugs, novel tumor-targeted drugs and radiation therapy, allowing them to survive current cancer therapies and to initiate tumor recurrence and metastasis. Recently, different pathways that confer resistance and survival of CSCs, but also compounds and drugs that selectively target some of these pathways in CSCs have been identified. Such compounds and drugs include antibiotics like salinomycin, phytochemicals such as parthenolide, cyclopamine, EGCG, resveratrol, curcumin, sulforaphane and oxymatrine, the small molecule inhibitors vismodegib and repertaxin, monoclonal antibodies and antibody constructs raised against cell surface proteins expressed by CSCs, and, surprisingly, some classical drugs such as metformin, tranilast and thioridazine. These agents exhibit significant anti-CSC activity, alone or in combination with cytostatic drugs or tumor-targeted drugs, as recently shown in vitro and in human xenograft mice. Since current cancer therapies fail to eliminate CSCs, leading to cancer recurrence and progression, selective targeting of CSCs with compounds and drugs introduced herein may represent a novel therapeutic strategy to eradicate cancer.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".