DeepSeal: Empowering clinical researchers to analyze clinicogenomic data with an intuitive chat-based interface.

Abstract

1562 Background: Traditional clinicogenomic analysis workflows in oncology require substantial bioinformatics expertise and custom coding, creating a bottleneck where clinical researchers must rely on analysts for data interpretation. This dependency hinders hypothesis development, exploration and discovery, as researchers cannot directly interact with their data in real-time. DeepSeal addresses these challenges by providing a user-friendly, chat-based interface that enables immediate analysis of clinicogenomic data and seamless generation of results, empowering clinical researchers to independently explore and validate hypotheses. Methods: DeepSeal, an integration of a large language model with clinical and molecular databases and bioinformatics tools, was evaluated by replicating the findings of Riaz et al. (Cell, 2017), in advanced melanoma patients treated with nivolumab. Through natural language prompts, DeepSeal performed multiple analyses including differential gene expression analysis and Gene Set Enrichment Analysis (GSEA) to generate comprehensive molecular profiles of responders versus non-responders and to identify molecular signatures associated with treatment response. Results: DeepSeal successfully replicated the key findings of Riaz et al., identifying significant differential expression of immune-related genes in treatment responders. GSEA executed through DeepSeal’s chat interface further revealed enrichment of immune-related pathways critical for response, including B cell activation (GO:0042113), T cell activation (GO:0042110), and regulation of adaptive immune response (GO:0002819). The chat interface enabled rapid hypothesis testing and visualization generation, with analyses completed in minutes without the need for programming expertise. Conclusions: DeepSeal demonstrates the feasibility of enabling clinical researchers to independently analyze complex clinicogenomic data through natural language interaction. By successfully replicating and validating findings from Riaz et al., it generates reliable insights without programming expertise, offering a transformative approach to accelerate translational research. This removal of technical barriers between researchers and their data has the potential to substantially speed hypothesis testing and discovery in oncology, ultimately enhancing the pathway from molecular insights to improved patient care.