Transglutaminase 2 regulates ovarian cancer metastasis by modulating the immune microenvironment
Bibliographic record
Abstract
Introduction Ovarian cancer is the most lethal gynecological malignancy. Deepening our knowledge of the interactions within the tumor microenvironment (TME) is important for discovering new targeted treatment strategies. Transglutaminase 2 (TG2) is a protein implicated in many biological and pathophysiological processes, including promoting tumor progression in ovarian cancer. Its role in disease progression has been studied in ovarian cancer cells; however, its role in the ovarian TME is less understood. Methods In this study, for the first time, we assessed the therapeutic potential of novel covalent irreversible small molecule TG2 inhibitors in xenograft models of ovarian cancer. We further elucidated the role of TG2 in ovarian cancer cells and syngeneic tumors by immune phenotyping using flow cytometry, RNA sequencing, and immunohistochemistry to characterize the contribution of TG2 in the TME to the metastatic process of ovarian cancer. Results To investigate the transamidation catalytic and GTP binding activities of TG2 in cancer cells, we used several TG2 inhibitors, some of which decreased invasiveness of human ovarian cancer cell lines in vitro and lengthened survival of the SKOV3 xenograft model. Using the ID8 Trp53-/- Brca1-/- and KPCA.B syngeneic mouse models of ovarian cancer, we defined the contribution of TG2 in the TME to the metastatic process. Lack of TG2 in the TME prolonged survival in the ID8 Trp53-/- Brca1-/- metastatic model, but it did not affect survival in the non-metastatic KPCA.B model. Through extensive analysis of the immune composition in both the primary tumor and metastatic ascites in the ID8 Trp53-/- Brca1-/- model, we discovered that the lack of host TG2 resulted in decreased frequency of immunosuppressive tumor-associated macrophages, and increased frequency of T cells, NK cells, and B cells. RNA sequencing of the primary tumors with or without TG2 present in the TME, revealed an enrichment of pathways related to B cell activation and regulation. Discussion These findings highlight the importance of TG2 in the TME for ovarian cancer metastasis, potentially by activation of humoral immunity and specifically highlight a crucial role for TG2 in modulating B cells to prolong survival in mouse models of ovarian cancer.
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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".