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Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke.

ABSTRACT: Early and accurate diagnosis of stroke improves the probability of positive outcome. The objective of this study was to identify a pattern of gene expression in peripheral blood that could potentially be optimised to expedite the diagnosis of acute ischaemic stroke (AIS). A discovery cohort was recruited consisting of 39 AIS patients and 24 neurologically asymptomatic controls. Peripheral blood was sampled at emergency department admission, and genome-wide expression profiling was performed via microarray. A machine-learning technique known as genetic algorithm k-nearest neighbours (GA/kNN) was then used to identify a pattern of gene expression that could optimally discriminate between groups. This pattern of expression was then assessed via qRT-PCR in an independent validation cohort, where it was evaluated for its ability to discriminate between an additional 39 AIS patients and 30 neurologically asymptomatic controls, as well as 20 acute stroke mimics. GA/kNN identified 10 genes (ANTXR2, STK3, PDK4, CD163, MAL, GRAP, ID3, CTSZ, KIF1B and PLXDC2) whose coordinate pattern of expression was able to identify 98.4% of discovery cohort subjects correctly (97.4% sensitive, 100% specific). In the validation cohort, the expression levels of the same 10 genes were able to identify 95.6% of subjects correctly when comparing AIS patients to asymptomatic controls (92.3% sensitive, 100% specific), and 94.9% of subjects correctly when comparing AIS patients with stroke mimics (97.4% sensitive, 90.0% specific). The transcriptional pattern identified in this study shows strong diagnostic potential, and warrants further evaluation to determine its true clinical efficacy.

SUBMITTER: O'Connell GC

PROVIDER: S-EPMC5685316 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke.

O'Connell Grant C GC Petrone Ashley B AB Treadway Madison B MB Tennant Connie S CS Lucke-Wold Noelle N Chantler Paul D PD Barr Taura L TL

NPJ genomic medicine 20161130

Early and accurate diagnosis of stroke improves the probability of positive outcome. The objective of this study was to identify a pattern of gene expression in peripheral blood that could potentially be optimised to expedite the diagnosis of acute ischaemic stroke (AIS). A discovery cohort was recruited consisting of 39 AIS patients and 24 neurologically asymptomatic controls. Peripheral blood was sampled at emergency department admission, and genome-wide expression profiling was performed via ...[more]

PMID: 29263821

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Project description:Diagnosis and monitoring of primary brain tumours, brain metastasis and acute ischaemic stroke all require invasive, burdensome and costly diagnostics, frequently lacking adequate sensitivity, particularly during disease monitoring. Monocytes are known to migrate to damaged tissues, where they act as tissue macrophages, continuously scavenging, phagocytizing and digesting apoptotic cells and other tissue debris. We hypothesize that upon completion of their tissue-cleaning task, these tissue macrophages might migrate via the lymph system to the bloodstream, where they can be detected and evaluated for their phagolysosomal contents. We discovered a blood monocyte subpopulation carrying the brain-specific glial fibrillary acidic protein in glioma patients and in patients with brain metastasis and evaluated the diagnostic potential of this finding. Blood samples were collected in a cross-sectional study before or during surgery from adult patients with brain lesions suspected of glioma. Together with blood samples from healthy controls, these samples were flowing cytometrically evaluated for intracellular glial fibrillary acidic protein in monocyte subsets. Acute ischaemic stroke patients were tested at multiple time points after onset to evaluate the presence of glial fibrillary acidic protein-carrying monocytes in other forms of brain tissue damage. Clinical data were collected retrospectively. High-grade gliomas (N = 145), brain metastasis (N = 21) and large stroke patients (>100 cm3) (N = 3 versus 6; multiple time points) had significantly increased frequencies of glial fibrillary acidic protein+CD16+ monocytes compared to healthy controls. Based on both a training and validation set, a cut-off value of 0.6% glial fibrillary acidic protein+CD16+ monocytes was established, with 81% sensitivity (95% CI 75-87%) and 85% specificity (95% CI 80-90%) for brain lesion detection. Acute ischaemic strokes of >100 cm3 reached >0.6% of glial fibrillary acidic protein+CD16+ monocytes within the first 2-8 h after hospitalization and subsided within 48 h. Glioblastoma patients with >20% glial fibrillary acidic protein+CD16+ non-classical monocytes had a significantly shorter median overall survival (8.1 versus 12.1 months). Our results and the available literature, support the hypothesis of a tissue-origin of these glial fibrillary acidic protein-carrying monocytes. Blood monocytes carrying glial fibrillary acidic protein have a high sensitivity and specificity for the detection of brain lesions and for glioblastoma patients with a decreased overall survival. Furthermore, their very rapid response to acute tissue damage identifies large areas of ischaemic tissue damage within 8 h after an ischaemic event. These studies are the first to report the clinical applicability for brain tissue damage detection through a minimally invasive diagnostic method, based on blood monocytes and not serum markers, with direct consequences for disease monitoring in future (therapeutic) studies and clinical decision making in glioma and acute ischaemic stroke patients.

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Dataset Information

Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke.

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Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke.

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