Models of Functional Heterogeneity and Targeting Strategies for Cancer Stem Cells
Bibliographic record
Abstract
Introduction: Functional heterogeneity, defined as variations between and within tumours, is the underlying cause for malignant tumour processes such as tumour progression, metastasis and treatment resistance. In particular, cancer stem cells (CSCs) could be important contributors to functional heterogeneity within tumours, as CSCs can differentiate into different tumorous cells. This study aims to identify models for the genesis of functional heterogeneity among cancer cells and strategies for targeting CSCs. Methods: Using an integrated review process, various models for functional heterogeneity genesis in cancer and cancer stem cell treatments were explored. Papers that explicitly focused on either explaining a model for the genesis of functional heterogeneity in cancer or on describing targeting strategies for CSCs were included. To conduct our search the following databases were used: PubMed, OVID (Medline), and Web of Science. Results: Several prominent models for genesis of cancer functional heterogeneity were identified, including the hierarchy model, stochastic model, and plasticity model. There is no definitive model as different types of cancer may follow different models of functional heterogeneity. However, multiple models suggest that CSCs, tumor cells with acquired or innate multipotency, are responsible for enhancing tumour progression. Hence, many therapeutic methods have been explored to target CSCs including: interfering with signalling pathways, targeting biomarkers, exerting transcriptional control, damaging quiescence, disrupting the microenvironment and immunotherapy. Discussion: This study identified a gap in current literature to be the lack of clinical studies, with the majority of experiments being conducted on mice models or in vitro. As such the applicability of the findings on a human in vivo level are unclear. Strengths of this paper include the extensive scope of literature reviewed, while limitations include the lack of a quality assessment stage. Conclusion: This study suggests that CSCs are involved in the development of functional heterogeneity in tumours and identifies some preliminary strategies to target them. However, more clinical trials are needed to further validate current proposed treatments. By developing CSC-specific therapies, functional heterogeneity amongst cancer cells can be decreased, which will prevent cancer cells from continuing to progress. As a result, these treatments will be more likely to effectively treat 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".