Metabolic adaptations determine whether natural killer cells fail or thrive within the tumor microenvironment
Bibliographic record
Abstract
Natural Killer (NK) cells are a top contender in the development of adoptive cell therapies for cancer due to their diverse antitumor functions and ability to restrict their activation against nonmalignant cells. Despite their success in hematologic malignancies, NK cell-based therapies have been limited in the context of solid tumors. Tumor cells undergo various metabolic adaptations to sustain the immense energy demands that are needed to support their rapid and uncontrolled proliferation. As a result, the tumor microenvironment (TME) is depleted of nutrients needed to fuel immune cell activity and contains several immunosuppressive metabolites that hinder NK cell antitumor functions. Further, we now know that NK cell metabolic status is a main determining factor of their effector functions. Hence, the ability of NK cells to withstand and adapt to these metabolically hostile conditions is imperative for effective and sustained antitumor activity in the TME. With this in mind, we review the consequences of metabolic hostility in the TME on NK cell metabolism and function. We also discuss tumor-like metabolic programs in NK cell induced by STAT3-mediated expansion that adapt NK cells to thrive in the TME. Finally, we examine how other approaches can be applied to enhance NK cell metabolism in tumors.
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.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.045 |
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; both teacher heads agree on what is shown here.
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".