High-dimensional analysis of the adenosine pathway in high-grade serous ovarian cancer
Bibliographic record
Abstract
BACKGROUND: Hydrolysis of extracellular ATP to adenosine (eADO) is an important immune checkpoint in cancer immunology. We here investigated the impact of the eADO pathway in high-grade serous ovarian cancer (HGSC) using multiparametric platforms. METHODS: We performed a transcriptomic meta-analysis of eADO-producing CD39 and CD73, an eADO signaling gene signature, immune gene signatures and clinical outcomes in approximately 1200 patients with HGSC. Protein expression, localization and prognostic impact of CD39, CD73 and CD8 were then performed on approximately 1000 cases on tissue microarray, and tumor-infiltrating lymphocytes (TILs) were analyzed by flow cytometry and single-cell RNA sequencing on a subset of patients. RESULTS: Concomitant CD39 and CD73 gene expression, as well as high levels of an eADO gene signature, were associated with worse prognosis in patients with HGSC, notably in the immunoregulatory molecular subtype, characterized by an immune-active microenvironment. CD39 was further associated with primary chemorefractory and chemoresistant human HGSC and platinum-based chemotherapy of murine HGSC was significantly more effective in CD39-deficient mice. At protein level, CD39 and CD73 were predominantly expressed by cancer-associated fibroblasts, and CD39 was expressed on severely exhausted, clonally expanded and putative tissue-resident memory TILs. CONCLUSIONS: Our study revealed the clinical, immunological, subtype-specific impacts of eADO signaling in HGSC, unveiled the chemoprotective effect of CD39 and supports the evaluation of eADO-targeting agents in patients with 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 | 0.001 | 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".