Investigating the Role of PTEN in Prostate Cancer Cell-Intrinsic Type I Interferon Responses
Bibliographic record
Abstract
Prostate cancer (PCa) is the most commonly diagnosed cancer in Canadian men and is characterized by a dysregulated immune response which is influenced by cancer cell-intrinsic genetic aberrations. Loss of the tumor suppressor gene phosphatase and tensin homolog (PTEN) occurs in 20-30% of PCa tumors and is associated with disease progression and an aggressive phenotype. Clinical and preclinical studies demonstrate that loss of PTEN expression correlates with decreased Type I Interferon (IFN1) pathway markers, suggesting that PTEN has a role in regulating the immune response by impacting IFN1 signaling. To characterize the role of PTEN in shaping cellular immune-related properties, we assessed cell-intrinsic IFN1 responses using NanoString gene expression analysis and secreted cytokine profiling in PCa cells with variable PTEN expression. We observed distinct response patterns at baseline and following treatment with IFN1 agonists. We generated PTEN knockout derivatives of PCa cells and observed statistically significant differences in expression patterns of IFN1 pathway genes in PTEN-deficient cells during normal growth and following treatment with IFN1 agonists. Genes involved in innate immune signaling, antiviral responses, and inflammation were significantly decreased in PTEN-knockout cells. Additionally, PTEN-knockout cells had significantly decreased secreted levels of major inflammatory and chemotactic cytokines including CXCL1 and CXCL10 compared to PTEN-intact PCa cells. Given the significance of the cross-talk between cancer cells and surrounding immune cells in cancer progression, these findings are important in elucidating the specific contribution of cell-intrinsic pathways to the PCa tumor microenvironment. This investigation may lead to exploitation of PCa cell-intrinsic IFN1 pathways for rational design and use of immune-based therapies to improve management of PCa.
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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".