Abstract B30: PTEN loss associates with type I and II IFN response in prostate cancer
Bibliographic record
Abstract
Abstract PTEN gene deletion and protein loss occurs in 20-30% of prostate cancer (PCa) cases and leads to poor disease outcome. Loss of the PTEN tumor suppressor gene activates the PI3 kinase pathway and also appears to alter cellular interferon (IFN) responses. Recent evidence highlights the role of inflammation in the tumor microenvironment (TME) and its association with PCa disease progression. It is thus important to establish whether IFN response inflammatory biomarkers in PCa tumors are predictive of disease outcome in the context of PTEN status. The aim of this study is to determine if PTEN genomic and protein loss is associated with IFN response in the TME. We performed an in silico analysis of genomic and corresponding transcriptomic profiles PCa tumors (n=493) from the Genomic Data Commons (GDC) cohort to identify significant alterations in immune response pathways when PTEN was lost. Nexus Copy Number, v8.0 (BioDiscovery, Santa-Clara, CA, USA) was used for normalization, segmentation, and identification of corresponding copy number events of all genomic files in the GDC cohort. The GDC RNAseq dataset comprises the expression of 20,532 genes that were filtered using standard thresholds from Nexus Expression 3.0 (BioDiscovery, Santa-Clara, CA, USA). The filtering resulted in 6081 genes. Genes of interest were identified using the Gene Ontology (GO) Biological Function in Nexus Expression 3.0 with the keywords “Immune” and “Inflammatory” (http://geneontology.org/). Of the 449 selected immune genes, 124 (28%) were differentially expressed when the PTEN-loss group was compared to the PTEN-intact group. DAVID enrichment analysis showed upregulation of 16 pathways, in which three were directly associated with immune and inflammatory responses, including retinoic acid-inducible gene I (RIG-I-like) receptor signaling pathway (P<0.0001), chemokine signaling pathway (P<0.0001), and Toll-like receptor signaling pathway (P<0.0001). In addition, we identified ten downregulated pathways, including cytokine-cytokine receptor interaction pathway (P<0.003) and intestinal immune network for IgA production pathway (P<0.006). We then selected the 25 most differentially expressed genes from the three upregulated and two downregulated pathways described above to compare the cohort of 218 prostate cancer tumors from MSKCC (in which 11% had PTEN loss) to the GDC cohort. The direction of differential gene expression was identical for 19/25 (76%) genes, in both cohorts having similarly altered PTEN-dependent changes in expression of immune and inflammatory response genes. The 25 most differentially expressed genes were associated directly with type I and II IFN response, indicating that PTEN loss affects the TME probably through the IRF3-IFN axis. Collectively, our findings based on in silico studies suggest that PCa with PTEN loss exhibits dysregulated Type I and II IFN response that permits progression to an aggressive disease phenotype. Future investigations will be required to define the mechanisms underlying these correlations, and their role in PCa disease progression so that inflammation biomarker can be therapeutically exploited using immunotherapies. Citation Format: Thiago Vidotto, Jeremy Andrew Squire, Madhuri Koti. PTEN loss associates with type I and II IFN response in prostate cancer [abstract]. In: Proceedings of the AACR International Conference held in cooperation with the Latin American Cooperative Oncology Group (LACOG) on Translational Cancer Medicine; May 4-6, 2017; São Paulo, Brazil. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(1_Suppl):Abstract nr B30.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".