The Common Fund Data Ecosystem (CFDE) Biomarker Data partnership project, involving six DCCs, is focused on the unifying concept of biomarkers, with scientific and clinical relevance, and tasked with organizing and harmonizing biomarker knowledge from numerous diverse sources, facilitating integrative data science use cases for CFDE researchers and the scientific community at large. The Illuminating the Druggable Genome (IDG) Data Coordinating Center (DCC) team at UNM is highly focused on clinical relevance as inferred from the large and diverse de-identified EHR databases from Cerner, namely (1) Health Facts and (2) Real World Data, with a focus on LOINC-encoded chemical lab tests. Specialized natural language processing (NLM) named entity recognition (NER) software from NextMove Software is employed for genes and proteins, resolved to standard IDs from HGNC or UniProt. Introduction CFDE Biomarker Partnership References Methods and Tools Discussion Conclusions and Next Steps RESULTS Clinically relevant precision molecular biomarker illumination powered by Cerner Real World Data Jeremy J. Yang, Vincent T. Metzger, Cristian G. Bologa, and Christophe G. Lambert University of New Mexico, School of Medicine, Department of Internal Medicine, Translational Informatics Division, Albuquerque, New Mexico, USA Common Fund Data Ecosystem (CFDE) All-Hands Meeting -- Bethesda, Maryland -- March 19-20, 2024 POWERED BY The central goal of the CFDE Biomarker Partnership, "… to develop a community-based biomarker-centric data model to harvest and organize biomarker knowledge for diverse biological data types," for which ontology development provides "semantic organization and integration of biomarker data to provide FAIR data representation." Within this overall goal, the IDG team is focused on clinical relevance, meaning the illumination and development of biomarkers with evidence for use by clinicians and patients and improved health outcomes. Existing lab test results are understood to be useful as biomarkers, supported by research findings, but often with great uncertainty and imprecision. Through retrospective, observational study of EHR data, labs can be associated with patient profiles and diagnoses, for improved applications as precision medicine tools. In addition, study of EHR data can ensure that the labs are in use and thus readily available. IDG-DCC Sub-project For the one-year CFDE Biomarker Partnership project, the objectives of the IDG sub-project are tightly scoped and focus on molecular biomarkers and almost exclusively human proteins measured in blood plasma. From the Cerner HealthFacts and RealWorldData databases, a set of molecular biomarkers is extracted and associated with evidence for clinical relevance, for inclusion in the partnership knowledge-base, and informing ontology development. Race Female Fpct Male Mpct African American 361145 14.42 240126 12.46 Asian 53718 2.14 38158 1.98 Asian/Pacific Islander 588 0.02 539 0.03 Biracial 3284 0.13 2741 0.14 Caucasian 1760688 70.30 1381007 71.64 Hispanic 15329 0.61 12479 0.65 Mid Eastern Indian 540 0.02 540 0.03 Native American 17233 0.69 13168 0.68 Not Mapped 26445 1.06 19805 1.03 Pacific Islander 4848 0.19 3585 0.19 Other 100728 4.02 75418 3.91 Unknown 69126 2.76 60177 3.12 NULL 90805 3.63 79996 4.15 TOTAL 2504477 100.00 1927739 100.00 Figure 1: Histogram of encounter count vs. age by gender for representative year 2016. Figure 2: Histogram of encounters vs. PSA by age group for males, representative year 2016. Figure 3: Mean encounter and patient count vs. year, for LOINC encoded laboratory test results in Cerner Real World Data. Figure 4: Schematic of biomarker-centric analytics integrating Common Fund and external data, with example human protein PSA. diagnosis_id diagnosis_co de diagnosis_description n_encounter 132782 I10 Essential (primary) hypertension 2360453 139829 E78.5 Hyperlipidemia, unspecified 1281935 142085 E11.9 Type 2 diabetes mellitus without complications