FGL2 promotes tumour growth and attenuates infiltration of activated immune cells in melanoma and ovarian cancer models
Bibliographic record
Abstract
Abstract The tumour microenvironment is infiltrated by immunosuppressive cells, such as regulatory T cells (Tregs), which contribute to tumour escape and impede immunotherapy outcomes. Soluble fibrinogen-like protein 2 (sFGL2), a Treg effector protein, inhibits immune cell populations, via receptors FcγRIIB and FcγRIII, leading to downregulation of CD86 in antigen presenting cells and limiting T cell activation. Increased FGL2 expression is associated with tumour progression and poor survival in several different cancers, such as glioblastoma multiforme, lung, renal, liver, colorectal, and prostate cancer. Querying scRNA-seq human cancer data shows FGL2 is produced by cells in the tumour microenvironment (TME), particularly monocytes and macrophages as well as T cells and dendritic cells (DCs), while cancer cells have minimal expression of FGL2. We studied the role of FGL2 exclusively produced by cells in the TME, by leveraging Fgl2 knockout mice. We tested two murine models of cancer in which the role of FGL2 has not been previously studied: epithelial ovarian cancer and melanoma. We show that absence of FGL2 leads to a more activated TME, including activated DCs (CD86+, CD40+) and T cells (CD25+, TIGIT+), as well as demonstrating for the first time that the absence of FGL2 leads to more activated natural killer cells (DNAM-1+, NKG2D+) in the TME. Furthermore, the absence of FGL2 leads to prolonged survival in the B16F10 melanoma model, while the absence of FGL2 synergizes with oncolytic virus to prolong survival in the ID8-p53−/−Brca2−/− ovarian cancer model. In conclusion, targeting FGL2 is a promising cancer treatment strategy alone and in combination immunotherapies.
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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 | 0.001 |
| 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".