Error-free, automated data integration of exosome cargo protein data with extensive clinical data in an ongoing, multi-omic translational research study.

Abstract

e16743 Background: Major advances in understanding the biology of cancer have come from genomic analysis of tumor and normal tissue. Integrating extensive patient-related data with deep analysis of omic data is crucial to informing omic data interpretation. Currently, such integrations are a highly manual, asynchronous, and costly process as well as error-prone and time-consuming. To develop new blood assays that may detect very early stage PDAC, a multi-omic investigation with deep clinical annotation is needed. Using pilot data from an on-going study, we test a new platform allowing automated error-free integration of an extensive clinical database with extensive omic data. Methods: Demographic, clinical, family pedigree and pathology data were collected on the Rave EDC platform. Exosomes were purified from 46 plasma samples from 14 controls and 24 PDAC patients and cargo proteins were quantified via SILAC. The Rave Omics platform was used to ingest and integrate clinical and omic data, run quality checks and generate integrated clinical-omic datasets. Data fidelity was validated by systematically computing differences between corresponding values in the source flies with those present in the extracted data object (integrated data). The root mean squared error (RMSE) was calculated for numeric values in each sample. Additional validation was conducted by manual inspection to ascertain data integrity. Results: We demonstrated automatic integration, without human intervention, of a subset of the clinical data and all available SILAC data into an analysis-ready data object. Data transfer was completely faithful, with 100% concordance between the source and the integrated data without loss of features. All proteins (n = 1515) and clinical variables (n = 64) were imported. Their nomenclature and corresponding sample values (n = 69690) and clinical values (n = 2432) matched exactly between datasets. In all samples, the RMSE was exactly zero, indicating no deviation between data sources. Conclusions: We demonstrated that automatic, efficient, and reliable integration of clinical-omic data is achievable during an in-flight PDAC trial. Automatic exploratory analytics supporting biomarker discovery are currently being used to uncover associations between omic and clinical features. The Rave Omics platform is disease-agnostic and we plan to expand to trials of varying size, indication, and completion status where systematic, automated integration of clinical and (multi)omic data is needed.