Chaudhuri, A

5068 Background: 177 Lu-PSMA-617 (LuPSMA) is a groundbreaking therapy for mPC, but biomarkers are urgently needed to improve patient selection. Plasma-derived EVs are emerging blood-based markers of tumor burden. Here, we analyzed levels of EVs of 123 patients (pts) from 4 cohorts: a pre- and during LuPSMA treatment for a contemporary prospective Mayo Clinic cohort, pre-treatment plasma levels of EVs for a historical Tulane cohort, localized prostate cancer (LPC) after radical prostatectomy (RP), and male healthy donors (HD). Methods: Blood samples were collected from 51 pts pre-cycles (pre-C) 1-3 of LuPSMA at Mayo Clinic from 12/2024-12/2025 and 26 pts pre-C1 at Tulane Cancer Center between 12/2018-1/2025. Small and large EVs were incubated with fluorescently conjugated antibodies (PSMA, STEAP1, CEACAM5, and TROP2) and enumerated using nanoscale flow cytometry. EVs from blood samples of 10 HD and 36 LPC post-RP from 6/2020-8/2024 at Mayo Clinic as controls. AUC-ROC and logistic regression analyses were performed to identify marker signatures associated with PSA50 response. Results: Baseline demographics are shown in the table. Median levels of PSMA, STEAP1, CEACAM5, and TROP2 EV/milliliter (mL) were all significantly higher for pts with mPC (pooled Tulane and Mayo cohorts) compared to LPC post-RP (p<0.001) and HD (p<0.001). In the Mayo cohort, 10 of 51 pts (20%) had undetectable PSA pre-C1. There were no significant differences in median pre-C1 plasma EV levels for these 4 different types of EVs comparing pts with undetectable vs detectable PSA. For 41 Mayo pts with detectable PSA pre-C1, 20 achieved PSA50 response at time of data cutoff (49%), while 7 pts (17%) had persistent PSA rise on LuPSMA. For pts who achieved PSA50, there was a significant decline in both CEACAM5 and TROP2 EVs/mL pre-C1 compared to pre-C3 (19 x 10 6 vs 5.8 x 10 6 , p=0.003 and 16 x 10 6 vs 5.5 x 10 6 , p<0.001, respectively), while no significant changes occurred for the 7 pts with no PSA response. A composite score of pre-C1 CEACAM5 and TROP2 EVs (large- and small-sized EVs) demonstrated the strongest association with PSA50 response (OR=2.96, 95% CI: 1.27-6.44, p=0.038), achieving AUC of 0.73. PSMA and STEAP1 EVs did not show any significant changes from pre-C1 to pre-C3. Conclusions: Plasma-derived EVs are a promising biomarker to identify patients who will benefit from LuPSMA. Longer follow-up and enrolling a larger cohort are underway. Clinical trial information: NCT06200103 . Median values for pre-LuPSMA (Mayo and Tulane), post-RP for LPC, and HD. Mayo mPC (n=51) Tulane mPC (n=26) LPC (n=36) HD (n=10) Age (range) 73 (57-87) 71 (54-83) 66 (56-77) 55 (50-57) Prior lines (range) 1 (1-3) 4 (1-11) - - Gleason score 9 8 7 - PSA (range) 1.0 (0-182) 75 (0.8-503) - - PSMA EV/mL 3.7 x 10 6 14 x 10 6 2.0 x 10 6 1.4 x 10 6 STEAP1 EV/mL 30 x 10 6 45 x 10 6 12 x 10 6 12 x 10 6 CEACAM5 EV/mL 17 x 10 6 5.1 x 10 6 3.7 x 10 6 3.9 x 10 6 TROP2 EV/mL 13 x 10 6 5.2 x 10 6 4.5 x 10 6 3.6 x 10 6

10578 Background: Multicancer detection (MCD) testing using cell-free DNA methylation patterns is a novel blood-based approach to cancer screening. In 2022, Mayo Clinic implemented offering MCD testing into routine clinical practice. Prior real-world reports have been limited to single-site experiences. We evaluated enterprise-wide implementation of MCD testing across all three Mayo Clinic sites using the Mayo Clinic Platform. Methods: We performed a retrospective analysis of de-identified clinical data from Mayo Clinic Platform Discover, including GRAIL Galleri tests performed at Mayo Clinic sites in Rochester, MN; Scottsdale, AZ; and Jacksonville, FL. This activity did not require IRB review per 45 CFR 46.102. Demographics, test results, cancer diagnoses, and prior cancer history were extracted using a privacy-preserving protocol, with data de-identified by expert determination. ICD-10 diagnostic codes were used to identify subsequent cancer diagnoses. Results: A total of 8,201 Galleri tests were performed in 7,300 patients; some patients underwent repeat testing. Fifty-seven tests (0.7%) were positive. Among MCD-positive patients, cancer diagnostic codes were identified in 32 patients, yielding a positive predictive value (PPV) of 58.7%. The most common cancers identified were oropharyngeal (34.4%), lymphoid and hematopoietic (34.4%), and gastrointestinal malignancies (18.8%).Among 8,144 MCD-negative tests (99.3% of all testing performed), 423 patients (5.2%) subsequently received a cancer diagnostic code, yielding a negative predictive value of 92.5%. The most commonly identified cancers in this group were male genital organs (36.6%), urinary tract (10.6%), and thyroid or other endocrine glands (7.3%). Overall, 10.1% of patients had a prior cancer history, which was more common in MCD-positive than MCD-negative patients (22.6% vs 10.0%). Patients with positive tests were older (mean age 68.4 vs 61.5 years) and more often male (67% vs 62%). Race distribution was similar. Conclusions: Enterprise-wide implementation of MCD testing across diverse clinical settings is feasible within routine workflows. Over half of positive tests were associated with cancer diagnostic codes, supporting the clinical promise of MCD testing. Limitations include incomplete capture of externally diagnosed cancers and lack of cancer signal-of-origin data. The Mayo Clinic Platform enables scalable, secure analytics to support future cancer interception strategies.

Postoperative surgical draining fluid (SDF) in oral squamous cell carcinoma (OSCC) was reported to contain circulating free DNA and extracellular vesicles (EVs). The potential role of SDF DNA as a cancer biomarker in OSCC patients is investigated. To determine whether EVs contribute to the DNA content of SDF, small EVs (sEVs) were isolated by size exclusion chromatography and compared with those in paired plasma. sEV obtained from a cohort of HPV(+) and HPV(−) patients were evaluated for the endocytic origin, size, vesicular morphology, and the DNA content. The sEV from HPV(+) and HPV(−) SDF were similar. The sEVs from SDF were larger and contained more DNA than the sEV from plasma. Treatments of sEV with the DNase/RNase cocktail established the presence of DNA on the surface and in the sEV lumen. Furthermore, 20 to 30% of the total SDF DNA was associated with sEV. Fragmentomic analyses identified the largest DNA (>10,000 bp) on the vesicle surface. The DNA in the vesicle lumen consisted of smaller (~5000 bp) DNA fragments. Electron microscopy of the enzyme-treated vesicles indicated that surface DNA/RNA may be involved in maintaining vesicular integrity and modulating the vesicle entry into cells. The sEV-carrying surface and luminal DNA emerge as significant contributors to the total SDF DNA and, following validation, might serve as useful biomarkers of disease presence/progression in OSCC.

Abstract Introduction Sepsis, a dysregulated immune response to infection, accounts for nearly 11 million deaths worldwide each year and remains a major cause of hospital mortality. The absence of precision diagnostics limits efforts to characterize immune dynamics and predict clinical outcomes. We evaluated whether liquid biopsies collected at ICU admission could provide insight into sepsis biology and enable precision diagnostics. Methods We applied cell-free RNA (cfRNA) liquid biopsies as a noninvasive method to highlight signaling networks, identify tissue-specific injury, and predict immune exhaustion. We collected serial samples throughout hospitalization from a cohort of 100 patients admitted with likely sepsis to a medical ICU. Comprehensive clinical data, including inpatient laboratory values and key outcome measures such as survival and ventilator-free days, were curated. Circulating cfRNA was isolated and purified from these samples and prepared for sequencing. We generated data from 254 samples at a minimum sequencing depth of 30x. We then designed a novel deep-learning computational method to enable deconvolution to better analyze and interpret these data. Results In our cohort, a total of 84 patients were adjudicated to have sepsis by the Sepsis-3 criteria, and 25 patients died within 90 days. Differential analysis was performed comparing circulating cfRNA in these septic patients to that of healthy controls, stratified by mortality along with specified subgroups including shock on admission, ventilator dependence, and preexisting immunosuppression. We identified multiple differentially expressed transcripts between septic patients and healthy controls, with significant alterations in signaling cascades associated with mitochondrial signaling, including alterations in oxidative phosphorylation and fatty acid metabolism. Using defined ligand-receptor interaction databases, we identified key pathways associated with immune and endothelial dysfunction in sepsis. Specifically, our analysis identified numerous alterations in CXCL8 (IL-8)-associated signaling cascades (engaging CXCR1, CXCR2, SDC1-3, and ACKR). Our analyses also highlighted key tissue factor pathway inhibitor (TFPI)-associated signaling via interactions with tissue factor and low-density lipoprotein receptor-related protein 1 (LRP1). These analyses are corroborated by our new deep-learning augmented approach to deconvolution, where we demonstrate the identification of rare tissue- and cell-specific signals that are associated with increased mortality in our cohort. Conclusion Liquid biopsy approaches such as cfRNA profiling can provide insight into mechanisms underlying sepsis and may uncover underlying drivers of endothelial dysfunction. These findings support ongoing efforts to develop liquid biopsy-based diagnostics and personalized therapeutic strategies for patients at high risk for sepsis and related mortality. This abstract is funded by: NIH