STING pathway mediated immunologic consequences of DNA damage repair gene losses in high-grade serous ovarian tumors
Bibliographic record
Abstract
Abstract High grade serous ovarian carcinoma (HGSC) is the most lethal gynecologic malignancy with high rates of chemotherapy resistance and poor outcomes. Our previous studies have demonstrated the variable tumor immune microenvironment states that associate with platinum chemotherapy response. We further showed the significance of the interferon induced chemokine CXCL10 as a key mediator of tumor infiltrating immune cell recruitment. Using the ID8-Trp53−/− murine model of HGSC, we demonstrated the potential of Stimulator of Interferon Genes (STING) pathway activation in enhancing response of HGSC tumors to carboplatin chemotherapy and sensitizing them to immune checkpoint blockade. Notably, CXCL10 production via IFN1 is also governed by genes that regulate cellular DNA damage repair pathways. Evolving evidence indicates a role of BRCA1 and PTEN genes in mediating cellular IFN1 responses. Losses in the function of these genes is widely prevalent in a large proportion of HGSC tumors, where tumors with BRCA1 mutations have higher CD8+ T cell infiltration in contrast to those with loss of PTEN. We hypothesized that HGSC tumors with loss of PTEN expression can be rendered susceptible to immune mediated killing via activating the STING pathway. Tumors generated from ID8-Trp53−/−; Brca1−/− cells and those from ID8-Trp53−/−; Pten−/− cells in C57BL6 mice showed significant immunologic differences. STING agonist treatment significantly increased chemosensitivity and improved overall response in mice implanted with ID8-Trp53−/−; Pten−/− cells compared to those treated with carboplatin alone, altering immune responses. This study is foundational to guide rationalistic combinations of STING pathway activating therapies in HGSC.
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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".