Real-time ex vivo monitoring of NK cell migration toward obesity-associated oesophageal adenocarcinoma following modulation of CX3CR1
Bibliographic record
Abstract
Oesophagogastric adenocarcinomas (OAC) are poor prognosis, obesity-associated cancers which may benefit from natural killer (NK) cell-based immunotherapies. Cellular immunotherapies encounter two key challenges to their success in OAC, namely recruitment to extratumoural tissues such as the omentum at the expense of the tumour and an immunosuppressive tumour microenvironment (TME) which can hamper NK cell function. Herein, we examined approaches to overcome the detrimental impact of obesity on NK cells and NK cell-based immunotherapies. We have demonstrated that NK cells migrate preferentially to the chemotactic signals of OAC patient-derived omentum over tumour in an ex vivo model of immune cell migration. We have identified CX3CR1 modulation and/or tumour chemokine profile remodelling as approaches to skew NK cell migration towards tumour. We also report targetable immunosuppressive facets of the obese OAC TME which dampen NK cell function, in particular cytotoxic capabilities. These data provide insights into approaches to therapeutically overcome key challenges presented by obesity and will inform superior design of NK cell-based immunotherapies for OAC.
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 | 0.001 |
| 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".