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e16273 Background: There is a critical need to develop fast, reliable, and cost-effective methods for the detection of pancreatic cancer (PC) at the earliest stage to maximize the impact of treatment. To-date, early detection of PC is close to impossible due to the location of the pancreas and the absence of characteristic symptoms in early cancer stages. Methods: Our team of clinicians and scientists has established a fast and reliable nanobiosensor technology that comprises iron/iron oxide nanoparticles attached to a protease or arginase activatable FRET pair (tetrakis (4- carboxyphenyl) porphyrin (TCPP) /cyanine 5.5). Arginase and seven proteases (MMP1, 3, and 9, cathepsin B, and E, urokinase plasminogen activator, and neutrophil elastase) were identified using the Gene Expression Omnibus (GEO) web tool based on their different expression pattern in pancreatic cancer patients, pancreatitis and healthy control subjects. Protease/arginase activities were measured in serum after 1h of incubation. Based on this data, a novel engineering approach to improved early stage detection of pancreatic cancer is reported here. This study was funded by American Cancer Society Institutional Research Grant (IRG‐16‐194‐07), awarded to the University of Kansas Medical Center. Results: In our study, 159 patients were enrolled at KU Cancer Center from 2000-2019, 47 with metastatic PC, 36 with localized PC, 26 pancreatitis and 50 healthy controls using KUCC Biospecimen Repository. The problem of early stage detection of pancreatic cancer can be modeled as a multi-class classification problem. Conventional classification approaches provide at most 77% accuracy for the dataset under consideration. A new hierarchical decision structure with specific feature engineering at each step is introduced here to improve the performance of the classifier. The fundamental premise of this information fusion-based framework involves tailoring the statistically most significant features with appropriate weights to execute an efficient binary classification task at each hierarchical step. An overall accuracy of 95% was achieved for the detection of patients with early pancreatic cancer (see table). Conclusions: Because of the dire survival statistics of pancreatic cancer, detection at the earliest possible time by means of a liquid biopsy will offer the greatest benefit. Novel nanobiosensor based protease biomarkers achieved high accuracy in early detection of pancreatic cancers by applying hierarchical decision structure. Our results need validation in a larger cohort. Predicted true class considering the following combination of classification methods: Step1 – kNN*, step2 – kNN*, step3 – RFC* (Accuracy = 94.97%).[Table: see text]

414 Background: DNA aneuploidy is an indicator of poor prognosis in epithelial cancers arising from the colon, rectum, kidney and breast. To our knowledge, there is no data on the occurrence of DNA aneuploidy in Pseudomyxoma peritonei (PMP). The aim of this study is to investigate the frequency of DNA aneuploidy and analyze its relation to clinicopathological characteristics of the patients. Methods: One hundred and twenty patients with PMP were analyzed. Flow cytometry was used on paraffin embedded tissue samples and multiple tissue blocks and serial sections were analyzed for each tumor. The results of DNA analysis were correlated to clinicopathologic data. Chi- square test was used to analyze categorical variables and t-test was used for continuous variables. Kaplan-Meier analysis was used to study impact of aneuploidy on survival. Results: Of 120 patients analyzed, 41.7% (70/120) patients had disseminated peritoneal adenomucinosis (DPAM), 32.5% (39/120) patients had either peritoneal mucinous carcinomatosis- intermediate type (PMCAI) or peritoneal mucinous carcinomatosis (PMCA) and 25.8% (31/120) had signet cell carcinoma (SCC). DNA aneuploidy was detected in 13% (16/120) of the PMP patients. The incidence of aneuploidy was 2% (1/50) in patients with DPAM, 25.6% (10/39) in patients with PMCAI or PMCA and 16% (5/31) in patients with SCC (p=0.004). Nearly 56% (67/120) of patients were males and 44% (53/120) were females. Gender did not impact the incidence of aneuploidy. Mean age of patients with aneuploid tumors and diploid- near diploid tumors was 56.8 (±13.5) years and 49.6 (± 11.8) years respectively (p=0.027). Median survival in patients with aneuploidy was 54 months compared to 100 months in patients with diploid/ near-diploid status (p=0.021). Conclusions: Older age and high-grade PMP are associated with higher rates of aneuploidy. Aneuploidy was associated with worse survival in patients with PMP.