Targeting cancer stem‐like cells in glioblastoma and colorectal cancer through metabolic pathways
Bibliographic record
Abstract
Cancer stem-like cells (CSCs) are thought to be the main cause of tumor occurrence, progression and therapeutic resistance. Strong research efforts in the last decade have led to the development of several tailored approaches to target CSCs with some very promising clinical trials underway; however, until now no anti-CSC therapy has been approved for clinical use. Given the recent improvement in our understanding of how onco-proteins can manipulate cellular metabolic networks to promote tumorigenesis, cancer metabolism research may well lead to innovative strategies to identify novel regulators and downstream mediators of CSC maintenance. Interfering with distinct stages of CSC-associated metabolics may elucidate novel, more efficient strategies to target this highly malignant cell population. Here recent discoveries regarding the metabolic properties attributed to CSCs in glioblastoma (GBM) and malignant colorectal cancer (CRC) were summarized. The association between stem cell markers, the response to hypoxia and other environmental stresses including therapeutic insults as well as developmentally conserved signaling pathways with alterations in cellular bioenergetic networks were also discussed. The recent developments in metabolic imaging to identify CSCs were also summarized. This summary should comprehensively update basic and clinical scientists on the metabolic traits of CSCs in GBM and malignant CRC.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".