Chaudhuri, Aadel A.

e14551 Background: Extracellular vesicles (EVs) are microscopic particles released by all cell types with a distinct fingerprint reflecting the cell of origin. In patients with prostate cancer, PSMA+EVs have been associated with clinical outcomes. In this study, we investigated tumor-derived EVs (tdEVs) using the tumor-specific CD326 marker as a novel biomarker for pancreas ductal adenocarcinoma (PDAC). Methods: The study included patients with localized PDAC treated on a prospective phase 2 clinical trial evaluating a 15-fraction chemoradiation (CRT) regimen. Blood samples were obtained after induction chemotherapy (C) (prior to CRT) and in follow-up. 20 healthy controls were included. High-resolution flow cytometry enumerated levels of fluorescent antibody labelled tumor-specific CD326 (EpCAM)-positive EVs. All samples were de-identified and randomly coded. The technologist responsible for EV analysis was blinded to patient information and clinical data. Each sample was run in triplicate. Flow cytometry data was analyzed with custom-built software to automatically determine and apply gates and generate reports with scatter plots and EV concentrations for each combination of markers and data summaries. Results: tdEV data were analyzed from 70 patients with pre-CRT samples. Patients had mean age 66 years (9.0 standard deviation), with 60% ECOG 0, 43% borderline resectable and 48% locally advanced. Patients had a median 8 cycles of C prior to CRT, and 70% proceeded to surgery after CRT. 2-year overall survival (OS) was 54%. Patient samples showed elevated total CD326 EVs compared to the healthy controls (p = 0.0005). Furthermore, patient CD326 EVs revealed two distinct subpopulations: small (120-1,000nm) and large (1,000-3,000nm) size CD326 EVs. No large CD326 subpopulation was found in healthy donor samples. Higher pre-CRT levels of total CD326 and large CD326 EVs were associated with decreased progression-free survival (PFS) (hazard ratio (HR) = 2.38, p = 0.013 and HR = 2.017, p = 0.021) on multivariable models with surgical resection. Resection was associated with increased PFS (p < 0.0001), and the interaction terms did not meet significance (p = 0.083 and p = 0.21). No differences in pre-CRT total or large CD326 EVs were identified between patients with or without surgical resection (p = 0.72 and p = 0.83). CA19-9 levels were not predictive of clinical outcomes in this trial and showed no association with EV levels. Conclusions: We present an association between clinical outcomes and tdEVs in PDAC, highlighting their potential as a novel, independent biomarker. If validated, tdEVs may offer an opportunity to personalize care and improve risk stratification.

Background: Multicellular programs in the tumor microenvironment (TME) drive cancer pathogenesis and response to therapy but remain challenging to identify and profile clinically. Here, we present Spatial EcoTyper, a multimodal machine learning framework for large-scale profiling of spatially dependent cell states and multicellular ecosystems, termed spatial ecotypes (SEs). Methods and Findings: By integrating five million single-cell and spot-level spatial transcriptomes from 10 diverse human neoplasms, including carcinomas and melanomas, we identified a rich tapestry of conserved regional plasticity across nine TME cell types. We then applied Spatial EcoTyper to deeply dissect these data, revealing nine novel SEs with broad conservation - each with unique biology, TME cell states, geospatial features, and clinical outcome associations. SEs were linked to specific biogeographic domains, including the tumor core, margin, and adjacent stroma, and were remarkably well-validated in 235 held-out tumor samples profiled by either single-cell RNA sequencing, sequencing-based spatial transcriptomics (10x Visium), or high-resolution imaging-based spatial transcriptomics (Vizgen MERSCOPE and NanoString CosMx). By deconvolving SEs from over 1,200 bulk tumor RNA-seq profiles, we discovered striking associations between SE levels and response to immune checkpoint inhibitors (ICIs), outperforming nearly 40 potential correlates of ICI response, including previously described signatures of tertiary lymphoid structures and tumor tissue PD-L1 expression. Notably, baseline levels of a proinflammatory SE - localized to the tumor core and defined by a hub of unique T cell, macrophage, fibroblast, and endothelial cell states - strongly forecasted ICI benefit, while another SE - localized to the tumor margin and defined by myofibroblast and hypoxia-associated endothelial cell states - portended ICI resistance. Conclusion: Our data reveal fundamental units of TME organization and demonstrate a platform for large-scale profiling of spatial cellular ecosystems in any tissue, with implications for improved forecasting of immunotherapy response and the development of novel therapeutic strategies. Citation Format: Wubing Zhang,Erin L. Brown,Abul Usmani,Noah Earland,Hyun Soo Jeon,Irfan Alahi,Nicholas P. Semenkovich,Janella C. Schwab,Chloé B. Steen,Chloe M. Sachs,Bilge Gungoren,Faridi Qaium,Peter K. Harris,Jacqueline Mudd,Qingyuan Cai,Antonella Bacchiocchi,Ryan C. Fields,Mario Sznol,Ruth Halaban,David Y. Chen,Aadel A. Chaudhuri,Aaron M. Newman. High-resolution profiling of the tumor microenvironment with spatial ecotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 153.