A Deep Learning Framework for Causal Inference in Clinical Trial Design: The CURE AI Large Clinicogenomic Foundation Model

Clinical research is limited by the capability to define the most important combinations of clinical features and biomarkers that predict therapeutic benefit. Here, we introduce CURE AI (Clinical trials Uncovering Real Efficacy Artificial Intelligence), a novel deep learning framework designed to predict individual patient benefit from a new therapeutic intervention compared to a standard of care. CURE AI utilizes a large clinicogenomic foundation model to understand the complex relationships between the vast clinical and multiomic features in clinical trial data. To build CURE AI, we trained a proprietary foundation model based on a deep learning architecture and training schema using a large collection of clinical and multiomics datasets. Using CURE AI, we seek to understand the complex interplay between clinicogenomic information from clinical trial arms to predict the magnitude of therapeutic benefit on the individual patient level. In this paper, we finetuned the CURE AI foundation model on lung cancer data from the OAK non-small cell lung cancer clinical trial. We observed that the trial could have been significant for progression-free survival (PFS) with fewer than half of the patients enrolled using CURE AI to guide trial enrollment (p = 0.60 to p < 0.05). The finetuned CURE AI (termed CURE Lung Cancer) demonstrated direct generalizability on a held-out independent clinical trial dataset, the POPLAR trial, by converting an insignificant PFS endpoint to significance while also including the majority of patients (88%; p = 0.21 to p < 0.05). In summary, we developed a causally-aware clinicogenomic deep learning platform that can learn to predict individualized patient benefit of investigational therapy compared to an existing standard of care. Because we use a foundation model trained on readily measurable patient characteristics, CURE AI can be applied to a variety of scientific and clinical uses including adaptive clinical trials, toxicity prediction, treatment response prediction, and understanding of drug resistance and response mechanisms.