Immunological Visibility: Posttranscriptional Regulation of Human NKG2D Ligands by the EGF Receptor Pathway
Bibliographic record
Abstract
Human cytolytic T lymphocytes and natural killer cells can limit tumor growth and are being increasingly harnessed for tumor immunotherapy. One way cytolytic lymphocytes recognize tumor cells is by engagement of their activating receptor, NKG2D, by stress antigens of the MICA/B and ULBP families. This study shows that surface up-regulation of NKG2D ligands by human epithelial cells in response to ultraviolet irradiation, osmotic shock, oxidative stress, and growth factor provision is attributable to activation of the epidermal growth factor receptor (EGFR). EGFR activation causes intracellular relocalization of AUF1 proteins that ordinarily destabilize NKG2D ligand mRNAs by targeting an AU-rich element conserved within the 3' ends of most human, but not murine, NKG2D ligand genes. Consistent with these findings, NKG2D ligand expression by primary human carcinomas positively correlated with EGFR expression, which is commonly hyperactivated in such tumors, and was reduced by clinical EGFR inhibitors. Therefore, stress-induced activation of EGFR not only regulates cell growth but also concomitantly regulates the cells' immunological visibility. Thus, therapeutics designed to limit cancer cell growth should also be considered in terms of their impact on immunosurveillance.
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 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) | 0.002 | 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".