<scp>STING</scp> pathway expression in low‐grade serous carcinoma of the ovary: an unexpected therapeutic opportunity?
Bibliographic record
Abstract
Ovarian carcinoma histotypes are distinct diseases with variable clinical outcomes and response to treatment. There is a need for new subtype-specific treatment modalities, especially for women with widespread and chemo-resistant disease. Stimulator of interferon genes (STING) is a part of the cGAS-STING pathway that mediates innate immune defence against infectious DNA-containing pathogens and also detects tumour-derived DNA and generates intrinsic antitumour immunity. The STING signalling pathway is suppressed by several mechanisms in a variety of malignant diseases and, in some cancers that may be a requirement for cellular transformation. The aim of this study was to use immunohistochemistry to evaluate STING protein expression across normal tissue, paratubal and ovarian cysts, and ovarian tumour histotypes including ovarian carcinomas. Herein, we show that the fallopian tube ciliated cells express STING protein, whereas the secretory cells are negative. STING expression differs among ovarian cancer histotypes; low-grade serous ovarian carcinomas and serous borderline tumours have uniform high STING expression, while high-grade serous and endometrioid carcinomas have heterogeneous expression, and clear cell and mucinous carcinomas show low expression. As low-grade serous carcinomas are known to be genomically stable and typically lack a prominent host immune response, the consistently high STING expression is unexpected. High STING expression may reflect pathway activation or histogenesis and the mechanisms may be different in different ovarian carcinoma histotypes. Further studies are needed to determine whether the STING signalling pathway is active and whether these tumours would be candidates for therapeutic interventions that trigger innate immunity activation.
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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) | 0.000 | 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".