Investigating the susceptibility of treatment-resistant oesophageal tumours to natural killer cell-mediated responses
Bibliographic record
Abstract
Abstract The majority of oesophageal adenocarcinoma (OAC) patients do not respond to multimodal treatment regimens and face dismal survival rates. Natural killer (NK) cells are crucial anti-tumour immune cells, and this study investigated the susceptibility of treatment-resistant OAC cells to these potent tumour killers. Natural killer receptor (NKR) ligand expression by OE33CisP (cisplatin-sensitive) and OE33CisR (cisplatin-resistant) cells was investigated. The immunomodulatory effects of OE33CisP and OE33CisR cells on NK cell phenotype and function were assessed. Finally, the impact of chemotherapy regimens on NKR ligand shedding was examined. Our data revealed significantly less surface expression of activating ligands B7-H6, MICA/B, ULBP-3 and activating/inhibitory ligands PVRL-1 and PVRL-4 by OE33CisR cells, compared to OE33CisP cells. Co-culture with OE33CisR cells reduced the frequencies of NKp30 + and NKp46 + NK cells and increased frequencies of TIGIT + , FasL + and TRAIL + NK cells. Frequencies of IFN- γ -producing NK cells increased while frequencies of TIM-3 + NK cells decreased after culture with OE33CisP and OE33CisR cells. Frequencies of circulating NKp30 + NK cells were significantly lower in OAC patients with the poorest treatment response and in patients who received FLOT chemotherapy, while B7-H6 shedding by OAC tumour cells was induced by FLOT. Overall, OE33CisR cells express less activating NKR ligands than OE33CisP cells and have differential effects on NKR expression by NK cells. However, neither cell line significantly dampened NK cell cytokine production, death receptor expression or degranulation. In addition, our data indicate that FLOT chemotherapy may promote B7-H6 shedding and immune evasion with detrimental consequences in OAC patients.
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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".