MyeGPT: an AI agent for multiple myeloma

Today, advancements in our understanding of cancer biology are increasingly attributed to large-scale clinical-molecular datasets. The case in point for multiple myeloma–the second-most prevalent haematological malignancy–is the CoMMpass study, a dataset with the paired clinical and sequencing data of 1,143 patients. However, the complexity of this rich dataset—with 763 clinical parameters and summary data spread across > 20 files—imposes hurdles to clinician-researchers interested in making simple queries like “What percentage of patients relapse after VRD induction therapy?” or “Compare the overall survival of patients with high vs normal expression of NSD2 ”. The rise of agentic AI over the past few years presents unparalleled opportunities to bridge this technical gap. We developed MyeGPT, an AI agent for clinical-molecular analysis of multiple myeloma. Based on the Reasoning-Acting (ReAct) framework, our agent converts natural language into de novo analyses grounded on the CoMMpass dataset, performs statistical analyses, and generates publication-quality plots. For validation, we created a benchmark of 20 calculation-intensive questions and designed two problems backed on published findings. MyeGPT achieves a mean reasoning-accuracy composite score of 79.4% on the internal benchmark and achieves inter-rater reliability of \(\:\kappa\:=0.965\) with human bioinformaticians. It also reproduces published findings with near perfect accuracy. We deploy the agent as a ready-to-use browser application, enabling on-the-go hypothesis validation from a smartphone. MyeGPT demonstrates how agentic AI can eliminate the laborious scripting involved in analysing a large multi-omics dataset like CoMMpass. By increasing accessibility to a wide range of analyses from univariable statistics to transcriptome-wide hypothesis testing, MyeGPT can speed up clinical-cohort validation and hypothesis generation for multiple myeloma. Not applicable.