WU-NK-101 (W-NK), a Memory-like (ML) NK Cell, Naturally Overcomes Tumor Microenvironment (TME) Metabolic Challenges, Retaining Anti-Tumor Potency
Bibliographic record
Abstract
Cellular engineering revolutionized adoptive cell therapy (ACT) through improved antigen recognition and cellular activation. Cancer cells are metabolically active and extensively consume nutrients from the TME, rendering it nutrient-deprived, acidic, and hypoxic, and resulting in intratumoral metabolic demands which negatively impact ACT antitumor activity. This is central to T cells, however, some of these challenges can be overcome through engineering, an ideal solution would be to have a cellular product that is inherently adept at overcoming these challenges. W-NK is a cytokine-reprogrammed, expanded, cryopreserved, ML-NK cell product derived from healthy human peripheral blood mononuclear cells; it comes with naturally effective potency towards tumor cell killing, and importantly can overcome metabolic challenges present in the TME. We aim at deciphering what metabolic features make W-NK particularly potent in adverse TME. W-NK characterization of metabolic fitness/adaptability was with transcriptomics (sc-RNA-seq; 10X Genomics) flow cytometry, bioenergetics (Seahorse Real-Time Cell Metabolic Analysis; Agilent), and proteomic (Sciex Zeno TOF 7600 tandem-mass spectrometry). The aforementioned assays evaluated the effect of differing media/TME on W-NK: 1) conventional (N) media (pH 7, glucose 11 mM); 2) TME-aligned media (pH 5.9, glucose 6 mM, immune suppressive agents, e.g., PGE2, TGFb1); and 3) ascites derived from patients with malignancy. Media composition was analyzed biochemically using clinical assays (Core Lab Clinical Studies at Washington University, St. Louis, USA), and by immune secretome profiling (Nomic Bio, Quebec, CA). At baseline, W-NK cells have a unique metabolic phenotype, with higher expression of cell surface nutrient transporters, e.g., GLUT1, CD98, ASCT2, MCT1 and PiT1, compared to conventional NK cells (cNK); additionally, W-NK metabolism was consistent with aerobic (“Warburg”) glycolysis, with ~80% of ATP coming from glycolysis vs. ~30% for cNK. In TME/ascites media, W-NK surface nutrient receptor expression adapted to meet metabolic demand. For example, transporters for amino acids, lactate and pyruvate are upregulated in these hypoglycemic medias. This coincided with a 50% shift in ATP manufacturing from glycolysis to mitochondria, suggesting that nutrients bypassed glycolysis to enter tricarboxylic acid cycle and oxidative phosphorylation. Proteomic analyses of W-NK cells cultured with either TME-aligned media or ascites supported this hypothesis, with up-regulation of metabolic pathways (ShinyGO) capturing amino-acid and lipid metabolism, and mitochondrial function. Overall, W-NK retained cytotoxic function in TME and ascites, as well as in native TME-aligned 3D assays from primary surgical tumor samples. Conversely, cNKs did not adapt to TME media, whether in nutrient transporter expression or ATP manufacturing, and demonstrated reduced survival and function, including cytotoxicity. W-NK has enhanced metabolic fitness, flexibility, and plasticity. This enables W-NK to utilize diverse macronutrients, engage different metabolic pathways, and maintain optimal ATP manufacturing. Overall, W-NK inherently survives and maintains function in the TME, a limiting factor for immune cell-based ACT. These data herald the promise of NK cell therapy; a Phase 1 clinical study of W-NK in acute myeloid leukemia is currently open and enrolling patients (NCT# 05470140).
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".