Enhancing Anti-Tumor Activity of Natural Killer Cells by Upregulating Amino Acid Transporters
Bibliographic record
Abstract
Abstract In spite of the superior efficacy of NK cells against hematological cancers such as acute myeloid leukemia (AML), the poor ability of NK cells to infiltrate solid tumors, such as breast cancer or colorectal carcinoma, limits the potential NK cells for cancer immunotherapy. Diverse approaches are now being undertaken to overcome this limitation by modifying NK cells for better recognition and effector function of NK cells. One of the efforts is to genetically engineer NK cells to express chimeric antigen receptors (CARs) for new tumor recognition and migration to the tumor, resulting in enhanced tumor killing. Here, we propose to enhance the anti-tumor activity of natural killer cells by upregulating amino acid transporters. Our laboratory recently showed that the upregulation of amino acid transporter is correlated with enhanced NK cell effector function. In particular, the upregulation of leucine amino acid transporter, CD98/LAT1, can induce leucine-driven mTORC1 activation and metabolic transformation, leading to enhanced proliferation and effector function of NK cells. Therefore, we will enhance the metabolism of NK cells by genetically modifying to upregulate the CD98/LAT1 nutrient transporter and make them more potent and sustained in vivo. In order to be the players in anti-tumor immunity, NK cells need to survive in the metabolically hostile conditions of the tumor microenvironment, where they have to compete for nutrients with metabolically active cancerous cells. We expect that combing our approach to augmenting the metabolism of NK cells with enhanced tumor recognition of CAR-NK cells could maximize the efficacy of cancer immunotherapy.
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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 | 0.000 |
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".