ICatalog: An open-source, collaborative platform for precision oncology studies.

Abstract

10020 Background: Genomic profiling of patient (pt) tumors using next-generation sequencing (NGS) has become an integral part of precision oncology (PO). To address the need for a streamlined, interoperable approach to clinical and NGS data management, interpretation, and reporting for a multi-institution pediatric PO study we developed iCatalog (iCat), a clinical decision support system for a multi-institution pediatric PO study. Methods: iCat was developed by the University of Chicago (UChicago) and Dana-Farber Cancer Institute to create clinical interpretation reports for the iCat2/Genomic Assessment Informs Novel Therapy Consortium (GAIN) study. It is a web-based application using the Python Django framework using open-source software and runs on virtual machines (VM) behind a secure firewall. iCat manages user and administrative permissions according to role and site. It securely stores, manages, and integrates genomic and clinical information as well as internal and external genomic knowledge on a patient level. Data types available during interpretation and report generation include pt demographics, specimen-level information such as diagnosis from pathology reports, and molecular data. iCat can host molecular data from different NGS test types. These data are entered through web interfaces and API calls. Genomic knowledge accessible in iCat is a combination of information brought in from external resources and gene and variant-level pediatric-specific curations completed in iCat by curators. Editable study-specific patient reports are generated for each test. Results: As of January 10, 2024, iCat has generated reports for 902 tests and stores information for an additional 217 tests for the 742 pts enrolled in the GAIN study. Of the 217 tests, 25 tests have reports in progress and 192 tests had reports generated manually before iCat was developed. Genomic data are from 11 different test types and clinical data are from patients with 92 rare pediatric solid tumor diagnoses. A total of 108 pts have received iCat clinical interpretation reports integrating genomic data from more than one NGS test type. The knowledge base contains pediatric-specific research-team authored curations for 561 genes, 2114 unique SNVs, 268 unique CNVs, and 227 unique structural variants. Test-specific, variant-level therapeutic recommendations have been made on 666 reports for 502 pts. Diagnostic or hereditary risk associations have been made on 729 reports for 537 pts. Conclusions: iCat is an academically developed open-source platform for PO studies that integrates clinical and genomic data, supports gene and variant interpretation, and facilitates the generation of an individualized genomic report with relevant clinical information for treating physicians. We developed and tested iCat for a pediatric precision oncology study and propose it as a customizable, collaborative platform for future precision oncology research.