Abstract 5544: Project Survival: Year 3 update on a 7-year prospective clinical study driven by quality metrics, multi-omic analysis and artificial intelligence to develop translational biomarkers for pancreatic cancer
Authors: Eric Michael Grund, Michael A. Kiebish, Viatcheslav R. Akmaev, Rangaprasad Sarangarajan, John Crowley, Amy Stoll-D'Astice, Tori Singer, Corrine Decicco, Wendy Hori, Valerie Bussberg, Karl Diedrich, Leonardo Rodrigues, Emily Chen, Vivek Vishnudas, Robert Najarian, Tomislav Dragovich, Manuel Hidalgo, Niven Narain, A. James Moser
Journal: Cancer Research (2018)

Abstract Pancreatic adenocarcinoma is expected to be the second leading cause of cancer-related death by 2020 and has a dismal 5-year survival rate of 8%. The high mortality rate is in part due to the lack of available methods to detect the disease at an early stage or stratify patient populations into more effective treatment regiments in meaningful time frames. To accomplish this, robust quality-controlled OMIC molecular profiling platforms and analytic solutions need to be developed and incorporated into discovery precision medicine protocols to align with translation goals and ensure utility of each sample type. Project Survival, a multisite prospective longitudinal study, is in year 3 of enrolling subjects within 6 categories: healthy volunteers with a relative with pancreatic cancer (N=50), pancreatitis (N=50), pancreatic cystic neoplasm (N=50), suspicious pancreatic masses with pathology other than pancreatic cancer (N=50), early stage (N=200), and metastatic pancreatic cancer (N=200). This study utilizes a systems medicine approach for translational biomarker discovery by performing analysis of matched subject sera, plasma, buffy coat, saliva, urine, and tumor/adjacent normal tissues and integrating them with the respective full clinical annotation using the BERG Interrogative Biology® platform. Multiple longitudinal time points are taken over the course of the six-year timeline, enabling dynamic modeling. Proteomic, signaling lipidomic, structural lipidomic, and metabolomic analysis are performed on all samples and stringent quality control metrics are engaged to ensure quantitative accuracy of the platforms over time. Additionally, several analytic normalization tools are employed to align longitudinal OMIC datasets for quantitative comparisons. We utilized bAIcis™ (BERG Artificial Intelligence Clinical Information System) to align multi-omic profiles with longitudinal clinical information to infer probabilistic cause-and-effect relationships among molecular and clinical variables in a network-based model. We will be presenting updated enrollment and preliminary quality metrics across the study and sites, along with initial molecular stratification markers for clinical endpoints. Citation Format: Eric Michael Grund, Michael A. Kiebish, Viatcheslav R. Akmaev, Rangaprasad Sarangarajan, John Crowley, Amy Stoll-D'Astice, Tori Singer, Corrine Decicco, Wendy Hori, Valerie Bussberg, Karl Diedrich, Leonardo Rodrigues, Emily Chen, Vivek Vishnudas, Robert Najarian, Tomislav Dragovich, Manuel Hidalgo, Niven Narain, A. James Moser. Project Survival: Year 3 update on a 7-year prospective clinical study driven by quality metrics, multi-omic analysis and artificial intelligence to develop translational biomarkers for pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5544.