<i>PTEN</i> Loss Shapes Macrophage Dynamics in High-Grade Serous Ovarian Carcinoma
Bibliographic record
Abstract
High-grade serous ovarian carcinoma (HGSC) remains a disease with poor prognosis that is unresponsive to current immune checkpoint inhibitors. Although PI3K pathway alterations, such as PTEN loss, are common in HGSC, attempts to target this pathway have been unsuccessful. We hypothesized that aberrant PI3K pathway activation may alter the HGSC immune microenvironment and present a targeting opportunity. Single-cell RNA sequencing identified populations of resident macrophages specific to Pten-null omental tumors in murine models, which were confirmed by flow cytometry. These macrophages were derived from peritoneal fluid macrophages and exhibited a unique gene expression program, marked by high expression of the enzyme heme oxygenase-1 (HMOX1). Targeting resident peritoneal macrophages prevented the appearance of HMOX1hi macrophages and reduced tumor growth. In addition, direct inhibition of HMOX1 extended survival in vivo. RNA sequencing identified IL33 in Pten-null tumor cells as a likely candidate driver, leading to the appearance of HMOX1hi macrophages. Human HGSC tumors also contained HMOX1hi macrophages with a corresponding gene expression program. Moreover, the presence of these macrophages was correlated with activated tumoral PI3K/mTOR signaling and poor overall survival in patients with HGSC. In contrast, tumors with low numbers of HMOX1hi macrophages were marked by increased adaptive immune response gene expression. These data suggest targeting HMOX1hi macrophages as a potential therapeutic strategy for treating poor prognosis HGSC. Significance: Macrophages with elevated HMOX1 expression are enriched in PTEN-deficient high-grade serous ovarian carcinoma, promote tumor growth, and represent a potential therapeutic target.
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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".