Abstract A025: Extracellular-vesicles from the peri-prostatic adipose tissue of obese, but not lean, men promote prostate cancer aggressivity
Bibliographic record
Abstract
Abstract Prostate cancer (PC) affects 1-in-8-men and obesity, termed a global epidemic by the WHO, affects 1-in-3. Obesity is the largest modifiable cancer risk-factor: every five-point increase in body-mass index increases risk of fatal PC by almost 10% and shortens the time to development of treatment-refractory metastatic disease. Further, weight gain is a common side-effect of mainstay androgen-deprivation therapy. The peri-prostatic adipose tissue (PPAT) is an important component of the PC tumor microenvironment (TME). PPAT volume is associated with increased PC lethality/reduced therapy response. Additionally, adipose tissue (AT) is the largest human endocrine gland, showing an altered (potentially pro-tumor) secretome in obesity. PPAT can also secrete extracellular vesicles (EVs) carrying cargo including microRNAs, which are involved in melanoma, lung, ovarian and breast cancer progression. Despite accumulating evidence showing the importance of AT secretome in tumor growth, its roles in PC progression are still poorly understood. This project investigates EV-mediated mechanisms of communication between PPAT and PC epithelial cells, and their clinical implications. To date, we established a biobank from >120 patients, with matching PPAT, tumor tissue, clinical information and MRI scans. Functionally, we showed that PPAT EVs from obese but not lean patients significantly increase proliferation and migration of PC cells in vitro. Obese PPAT EVs also reduce angiogenesis, consistent with chronic hypoxia observed in obese patient adipose suggesting a switch to non-oxidative metabolism in PC, to meet increasing energy demands. We performed small RNA-seq on PPAT EVs from obese and lean PC patients, and mRNA-seq on PC cells treated with these EVs. These analyses revealed dysregulation of cellular metabolism and extracellular-matrix (ECM) remodeling by PPAT EVs. Top PPAT-EV dysregulated genes are associated with PC survival and are increased in PC vs normal tissue. Silencing of one such PPAT-upregulated gene, TBX1, repressed PC cell migration, invasion, proliferation and EMT. We also optimized in vitro adipocyte differentiation from PPAT stem cells to demonstrate that PPAT effects are specifically attributable to mature-adipocytes. Finally, we showed that RNA- seq analysis of PPAT from genetically engineered mouse models (GEMMs) modelling PC natural history, showed dramatic changes in tissue histology, immune response, lipid metabolism and ECM genes in PPAT of aggressive-versus-indolent tumors. Since genetic alterations in GEMMs are prostate-confined, such transcriptomic changes must be mediated by paracrine signaling from PC cells. Integrative analysis of these data will hopefully elucidate novel, actionable drivers of aggressive PC progression for personalized medicine. Citation Format: Nil Grunberg, Jiani Qian, Joseph Tam, Marc Lorentzen, Sila Akdogan, Nathan Lack, Moray Campbell, Cory Abate-Shen, Bijan Khoubehi, Taimur Shah, Mathias Winkler, Hashim Ahmed, Charlotte Bevan, Claire Fletcher. Extracellular-vesicles from the peri-prostatic adipose tissue of obese, but not lean, men promote prostate cancer aggressivity [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A025.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".