Interferon associated adaptive immune resistance represented by immune checkpoint expression and immune cell localization in high-grade serous ovarian cancer
Bibliographic record
Abstract
Abstract High-grade serous carcinoma (HGSC) is a deadly malignancy leading to ~70% of the 140,000 ovarian cancer deaths globally each year. Resistance to platinum chemotherapy followed by recurrence and an incurable disease occurs in most HGSC patients. Contemporary immune checkpoint blockade therapies have shown minimal efficacy in this cancer. Our previous investigations established that tumour interferon (IFN) activation status and CD8+ T cell density are predictors of chemotherapy response in HGSC. Furthermore, we also showed that IFN induced chemokine CXCL10 is a key determinant of increased survival via immune cell recruitment in the tumour immune microenvironment (TIME). Given that signal transducer and activator of transcription 1 (STAT1) is central to the feed forward loops of cellular IFN responses, we investigated STAT1 associated transcriptomic alterations and spatial profiles of immune cells in 204 pre-treatment HGSC tumours. RNA-sequencing based whole transcriptomic profiling revealed that higher STAT1 expression significantly correlated with higher immunomodulatory gene expression, including immune checkpoints and activators, in both chemotherapy sensitive and resistant tumours. Findings were independently validated in a cohort of 379 HGSC tumour RNA-Seq profiles from The Cancer Genome Atlas Network ovarian cancer dataset. Multiplex immunofluorescence based spatial profiling of CD8+ T cells, FoxP3+ T regulatory T cells, CD68+M1, and CD163+ M2 macrophages and expression of PD-L1, PD-1, IDO1 immune checkpoints was performed. Findings from our study provide evidence for IFN mediated adaptive immune resistance in the HGSC TIME and will potentially inform the design of rational chemo-immunotherapy approaches.
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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 | 0.001 |
| 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".