Abstract 124: Circulating Expression Signatures Can Predict Intracranial Aneurysm Growth As Assessed By The Predicted Aneurysm Trajectory Score

Abstract

Background: Intracranial aneurysm (IA) rupture is associated with high morbidity and mortality rates. Identifying unstable, growing IAs can enable critical treatment before rupture. Hypothesis: RNA expression profiles in whole blood are different among cases of growing vs. stable IA, and thus may be candidate risk assessment biomarkers. Methods: We performed RNA sequencing on 69 blood samples from IA patients. To assess if IAs would grow or not, we calculated the published Predicted Aneurysm Trajectory (PAT) score. We dichotomized samples into stable and growing IAs by median PAT. Combat-seq removed potential batch effects and counts were normalized to TPMs. After randomly dividing the dataset into a training and testing cohort, differentially expressed (protein-coding) genes (DEGs) in the training dataset were defined as those with an FDR-corrected F-test ( q -value)<0.05, an absolute fold-change≥2, and expression (TPM>0) in at least 50% of the samples. A Gaussian Naïve Bayes (GNB) model using the DEGs was trained and independently tested. To understand the pathobiology associated with the DEGs we performed gene ontology term enrichment analysis. Results: The PAT score predicted 34 IAs to be stable and 35 to be growing. Between these groups, we identified 36 DEGs that discriminated growing from stable IAs (Figure, A and B). Our GNB model using the DEGs achieved 86% accuracy in training (Figure, C). In the testing cohort, the DEGs also delineated growing from stable IAs (Figure, D), and the model had 70% accuracy (Figure, E). DEGs with increased expression in growing IAs were enriched for biological process (BP) terms including neutrophil activation and degranulation, while DEGs decreased in growing IAs were enriched for BP that included NK-cell immunity and cytotoxicity (Figure, F). Conclusion: Circulating transcriptomes can delineate IA growth based on the PAT score. Our biomarker model must be validated in larger cohorts and evaluated in longitudinal datasets.