subprojects C9 (R.F. and M.B.) and Z1 (M.B.); the German Federal Ministry of Education and Research within the framework of the e:Med research and funding concept CoNfirm FKZ 01ZX1708F (M.B.); and a Deutsche Gesellschaft für Hämatologie und Medizinische Onkologie e. V. (DGHO)eGesellschaft für Medizinische InnovationeHämatologie und Onkologie mbH (GMIHO) thesis fellowship (S.H.).Disclosures: None declared.

Pancreatic ductal adenocarcinoma (PDAC) is associated with high mortality and will become the second most common cause of cancer-associated mortality by 2030. The poor prognosis arises from a lack of sensitive biomarkers, limited therapeutic options, and the astonishingly high recurrence rate after surgery of 60–80%. The factors driving this recurrence, however, remain enigmatic. Therefore, we generated patient-derived organoids (PDOs) from early- and late-recurrent PDAC patients. Cellular identity of PDOs was confirmed by qPCR, ddPCR, and IHC analyses. This is the first study investigating the metabolism in PDOs of different, clinically significant PDAC entities by untargeted GC/MS profiling. Partial least square discriminant analysis unveiled global alterations between the two sample groups. We identified nine metabolites to be increased in early recurrent PDOs in comparison to late recurrent PDOs. More than four-times increased were fumarate, malate, glutamate, aspartate, and glutamine. Hence, α-keto acids were elevated in PDO-conditioned medium derived from early recurrent patients. We therefore speculate that an increased anaplerotic metabolism fuels the Krebs-cycle and a corresponding higher accessibility to energy fastens the recurrence in PDAC patients. Therein, a therapeutic intervention could delay PDAC recurrence and prolong survival of affected patients or could serve as biomarker to predict recurrence in the future.

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Over decades it became obvious that the structure of a medical record notably for coded data but also for narrative text and pictures must be carefully modelled. Well maintained standardized health terminologies and medical classifications are important issues for a user-friendly electronic medical record, which bring benefits for clinicians and patients.