Preventing surgery induced immune suppression and metastases by inhibiting PI3K-gamma signalling in Myeloid-Derived Suppressor Cells
Bibliographic record
Abstract
Abstract Myeloid derived suppressor cells (MDSCs) have a dominating presence in the postoperative period and mediate the suppression of Natural Killer (NK) cells and promotion of cancer metastases after surgery. However, their functional characteristics and effect on cellular immunity after surgery have not been comprehensively investigated. Here, we characterize the expansion of surgery-induced (sx) MDSCs via multi-colour flow cytometry, single-cell RNA sequencing, and functional ex vivo NK cell suppression assays. We then screened a small molecule library using our sx-MDSC:NK cell suppression assay to identify compounds that could inhibit sx-MDSCs. These studies provide evidence that PI3K-γ signalling is upregulated in sx-MDSCs and blockade with PI3K-γ specific inhibitors attenuates NK cell suppression in humans and mice and reduces postoperative metastases in murine models. Upregulated PI3K-γ in sx-MDSCs is a potential pathway amenable to therapeutic targeting in the postoperative period. One Sentence Summary The suppressive mechanisms of surgery-induced myeloid derived suppressor cells use PI3K signalling and are amenable to PI3K-gamma specific inhibitors.
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) | 0.006 | 0.002 |
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".