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Biomarkers from circulating neutrophil transcriptomes have potential to detect unruptured intracranial aneurysms.

ABSTRACT: BACKGROUND:Intracranial aneurysms (IAs) are dangerous because of their potential to rupture and cause deadly subarachnoid hemorrhages. Previously, we found significant RNA expression differences in circulating neutrophils between patients with unruptured IAs and aneurysm-free controls. Searching for circulating biomarkers for unruptured IAs, we tested the feasibility of developing classification algorithms that use neutrophil RNA expression levels from blood samples to predict the presence of an IA. METHODS:Neutrophil RNA extracted from blood samples from 40 patients (20 with angiography-confirmed unruptured IA, 20 angiography-confirmed IA-free controls) was subjected to next-generation RNA sequencing to obtain neutrophil transcriptomes. In a randomly-selected training cohort of 30 of the 40 samples (15 with IA, 15 controls), we performed differential expression analysis. Significantly differentially expressed transcripts (false discovery rate?

SUBMITTER: Tutino VM

PROVIDER: S-EPMC6310942 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Biomarkers from circulating neutrophil transcriptomes have potential to detect unruptured intracranial aneurysms.

Tutino Vincent M VM Poppenberg Kerry E KE Li Lu L Shallwani Hussain H Jiang Kaiyu K Jarvis James N JN Sun Yijun Y Snyder Kenneth V KV Levy Elad I EI Siddiqui Adnan H AH Kolega John J Meng Hui H

Journal of translational medicine 20181228 1

<h4>Background</h4>Intracranial aneurysms (IAs) are dangerous because of their potential to rupture and cause deadly subarachnoid hemorrhages. Previously, we found significant RNA expression differences in circulating neutrophils between patients with unruptured IAs and aneurysm-free controls. Searching for circulating biomarkers for unruptured IAs, we tested the feasibility of developing classification algorithms that use neutrophil RNA expression levels from blood samples to predict the presen ...[more]

PMID: 30593281

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Project description:BACKGROUND:Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. METHODS:Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n?=?94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n?=?40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. RESULTS:Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC)?=?0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. CONCLUSIONS:We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

Project description:Importance:Unruptured intracranial aneurysms (UIAs) are relatively common in the general population and are being increasingly diagnosed; a significant proportion are tiny (≤3 mm) aneurysms. There is significant heterogeneity in practice and lack of clear guidelines on the management of incidental, tiny UIAs. It is important to quantify the implications of different management strategies in terms of health benefits to patients. Objective:To evaluate the effectiveness of routine treatment (aneurysm coiling) vs 3 strategies for imaging surveillance compared with no preventive treatment or routine follow-up of tiny UIAs. Design, Setting, and Participants:A decision-analytic model-based comparative effectiveness analysis was conducted from May 1 to June 30, 2017, using inputs from the medical literature. PubMed searches were performed to identify relevant literature for all key model inputs, each of which was derived from the clinical study with the most robust data and greatest applicability. Analysis included 10 000 iterations simulating adult patients with incidental detections of UIAs 3 mm or smaller and no history of subarachnoid hemorrhage. Interventions:The following 5 management strategies for tiny UIAs were evaluated: annual magnetic resonance angiography (MRA) screening, biennial MRA screening, MRA screening every 5 years, aneurysm coiling and follow-up, and no treatment or preventive follow-up. Main Outcomes and Measures:A Markov decision model for lifetime rupture was constructed from a societal perspective per 10 000 patients with incidental, tiny UIAs. Outcomes were assessed in terms of quality-adjusted life-years. Probabilistic, 1-way, and 2-way sensitivity analyses were performed. Results:In this analysis of 10 000 iterations simulating adult patients with a mean age of 50 years, the base-case calculation shows that the management strategy of no treatment or preventive follow-up has the highest health benefit (mean [SD] quality-adjusted life-years, 19.40 [0.31]). Among the management strategies that incorporate follow-up imaging, MRA every 5 years is the best strategy with the next highest effectiveness (mean [SD] quality-adjusted life-years, 18.05 [0.62]). The conclusion remains robust in probabilistic and 1-way sensitivity analyses. No routine follow-up remains the optimal strategy when the annual growth rate and risk of rupture of growing aneurysms are varied. When the annual risk of rupture of nongrowing UIAs is less than 1.7% (0.23% in base case scenario), no follow-up is the optimal strategy. If annual risk of rupture is more than 1.7%, coiling should be performed directly. Conclusions and Relevance:Given the current literature, no preventive treatment or imaging follow-up is the most effective strategy in patients with aneurysms that are 3 mm or smaller, resulting in better health outcomes. More aggressive imaging surveillance for aneurysm growth or preventive treatment should be reserved for patients with a high risk of rupture. Given these findings, it is important to critically evaluate the appropriateness of current clinical practices, and potentially determine specific guidelines to reflect the most effective management strategy for patients with incidental, tiny UIAs.

Dataset Information

Biomarkers from circulating neutrophil transcriptomes have potential to detect unruptured intracranial aneurysms.

Publications

Biomarkers from circulating neutrophil transcriptomes have potential to detect unruptured intracranial aneurysms.

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