Imaging α-GalCer–Activated iNKT Cells in a Hepatic Metastatic Environment
Bibliographic record
Abstract
Patients with colorectal cancer frequently develop liver metastases after, and perhaps as a consequence of, lifesaving surgical resection of the primary tumor. This creates a potential opportunity for prophylactic metastatic treatment with novel immunostimulatory molecules. Here, we used state-of-the-art intravital imaging of an experimental liver metastasis model to visualize the early behavior and function of invariant natural killer T (iNKT) cells stimulated with α-galactosylceramide (α-GalCer). Intravenous α-GalCer prior to tumor cell seeding in the liver significantly inhibited tumor growth. However, some seeding tumor cells survived. A multiple dosing regimen reduced tumor burden and prolonged the life of mice, whereas tumors returned within 5 days after a single dose of α-GalCer. With multiple doses of α-GalCer, iNKT cells increased in number and granularity (as did NK cells). As a result, the total number of contacts and time in contact with tumors increased substantially. In the absence of iNKT cells, the beneficial effect of α-GalCer was lost. Robust cytokine production dissipated over time. Repeated therapy, even after cytokine dissipation, led to reduced tumor burden and prolonged survival. Serial transplantation of tumors exposed to α-GalCer-activated iNKT cells did not induce greater resistance, suggesting no obvious epigenetic or genetic immunoediting in tumors exposed to activated iNKT cells. Very few tumor cells expressed CD1d in this model, and as such, adding monomers of CD1d-α-GalCer further reduced tumor growth. The data suggest early and repeated stimulation of iNKT cells with α-GalCer could have direct therapeutic benefit for patients with colorectal cancer who develop metastatic liver disease.
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) | 0.001 | 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".