Expression and function of immune ligand-receptor pairs in NK cells and cancer stem cells: therapeutic implications
Bibliographic record
Abstract
BACKGROUND: The interplay between the immune system and cancer cells has come to the forefront of cancer therapeutics, with novel immune blockade inhibitors being approved for the treatment of an increasing list of cancers. However, the majority of cancer patients still display or develop resistance to these promising drugs. It is possible that cancer stem cells (CSCs) are contributing to this therapeutic resistance. Although CSCs usually represent a small percentage of the total number of cancer cells, they are endowed with the ability of self-renewal and to produce differentiated progeny. Additionally, they have shown the capacity to establish tumors after transplantation to animals, even in small numbers. CSCs have also been found to be resistant to various anti-cancer therapies, including chemotherapy, radiation therapy and, more recently, immunotherapy. This is true despite the sensitivity of CSCs to lysis in vitro by natural killer (NK) cells, the main effector cells of the innate immune system. In this paper the expression of ligands specific for NK cells on CSCs, the intracellular network responsible for the expression of the NK cytotoxicity receptors, and the status of activation of NK cells in the tumor micro-environment are reviewed. The aim of this review is to highlight potential strategies for overcoming CSC immune resistance, thereby enhancing the efficacy of current and future anti-cancer therapies. THERAPEUTIC IMPLICATIONS: NK cell activation in the tumor micro-environment through drugs neutralizing inhibitory immune receptors, and combined with other drugs harnessing the potential of the adaptive immune system, could be the most effective approach for attacking both stem cell and non-stem cell cancer populations.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".