PRESSnet: a novel framework for patient stratification and biomarker discovery using clinical knowledge graphs

Abstract

<title>Abstract</title> Harnessing multiomic data for patient stratification and biomarker discovery is vital for effective personalized medicine. However, as the volume and heterogeneity of data increases, so do the challenges of data integration and explainable hypotheses. To address this, we present PRESSnet (Patient REcommendation via Stratification and Selection using networks), an end-to-end framework leveraging multimodal patient knowledge graphs (KGs) for stratification and biomarker discovery. PRESSnet incorporates graph artificial intelligence and network algorithms in scalable, flexible analysis pipelines that can integrate underlying multiomic patient features with prior knowledge such as curated gene pathway data. Applied to patients from two different cancer types, PRESSnet generates explainable stratification hypotheses and captured known survival biomarkers as well as novel composite signatures that comparatively increased statistically significant survival separation compared to univariate markers. These biomarkers were validated for their translatability to unseen patients within cohorts in IO-treated NSCLC patients (MSK 2022) and across independent datasets in AML patients (TCGA and Beat AML). PRESSnet compared favourably to benchmark models such as MOFA + for stratification and Random Forests for biomarker generation and survival risk classification. We also demonstrate that PRESSnet’s ability to model prior knowledge can improve patient survival prediction, including in small datasets, and offer context-relevant insights into signalling pathways and regulatory networks involved in therapy resistance. PRESSnet is provided as a lightweight, adaptable framework for the scientific community to inform research into patient selection, asset positioning and trial design.

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