Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis
| Autor: | Hester F. Lingsma, Mary J. Vassar, Gunnar E. Carlsson, Jesse Paquette, David O. Okonkwo, Geoffrey T. Manley, Wayne A. Gordon, Tanya C. Petrossian, John K. Yue, Esther L. Yuh, Alex B. Valadka, Track-Tbi Investigators, Pratik Mukherjee, Tomoo Inoue, Jessica L. Nielson, Marco D. Sorani, Pek Yee Lum, Adam R. Ferguson, Shelly R. Cooper |
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| Přispěvatelé: | Public Health, Kobeissy, Firas H |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Traumatic
Male Oncology Critical Care and Emergency Medicine Traumatic Brain Injury Test Statistics Poly (ADP-Ribose) Polymerase-1 Biochemistry Diagnostic Radiology Stress Disorders Post-Traumatic 0302 clinical medicine Brain Injuries Traumatic Medicine Amines lcsh:Science Tomography ANKK1 Organic Compounds Neurotransmitters 3. Good health Physical Sciences Population study Traumatic Injury Statistics (Mathematics) 4.2 Evaluation of markers and technologies Biogenic Amines medicine.medical_specialty Physical Injury - Accidents and Adverse Effects Imaging Techniques TRACK-TBI Investigators Clinical Trials and Supportive Activities Traumatic Brain Injury (TBI) Catechol O-Methyltransferase 03 medical and health sciences Text mining Clinical Research Dopamine D2 Humans Polymorphism Statistical Methods Traumatic Head and Spine Injury Receptors Dopamine D2 lcsh:R Chemical Compounds Biology and Life Sciences Precision medicine medicine.disease Hormones Computed Axial Tomography Clinical trial 030104 developmental biology Brain Injuries Post-Traumatic lcsh:Q Injury - Traumatic brain injury Biomarkers Mathematics 030217 neurology & neurosurgery Neuroscience 0301 basic medicine Gerontology Dopamine lcsh:Medicine Machine Learning Catecholamines Mathematical and Statistical Techniques Injury - Trauma - (Head and Spine) Receptors Medicine and Health Sciences Trauma Medicine Stress Disorders screening and diagnosis Multidisciplinary Radiology and Imaging Neurochemistry Single Nucleotide Middle Aged Protein-Serine-Threonine Kinases Magnetic Resonance Imaging Detection Chemistry Research Design Biomarker (medicine) Mental health Female Research Article Adult General Science & Technology Traumatic brain injury Neuroimaging Protein Serine-Threonine Kinases Research and Analysis Methods Polymorphism Single Nucleotide Diagnostic Medicine Molecular genetics Internal medicine business.industry Organic Chemistry Neurosciences Pilot Studies Brain Disorders 4.1 Discovery and preclinical testing of markers and technologies Good Health and Well Being Injury (total) Accidents/Adverse Effects business Neurotrauma |
| Zdroj: | PLoS ONE, Vol 12, Iss 3, p e0169490 (2017) PLoS One (print), 12(3):e0169490. Public Library of Science PLoS ONE PloS one, vol 12, iss 3 |
| ISSN: | 1932-6203 |
| Popis: | BACKGROUND:Traumatic brain injury (TBI) is a complex disorder that is traditionally stratified based on clinical signs and symptoms. Recent imaging and molecular biomarker innovations provide unprecedented opportunities for improved TBI precision medicine, incorporating patho-anatomical and molecular mechanisms. Complete integration of these diverse data for TBI diagnosis and patient stratification remains an unmet challenge. METHODS AND FINDINGS:The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot multicenter study enrolled 586 acute TBI patients and collected diverse common data elements (TBI-CDEs) across the study population, including imaging, genetics, and clinical outcomes. We then applied topology-based data-driven discovery to identify natural subgroups of patients, based on the TBI-CDEs collected. Our hypothesis was two-fold: 1) A machine learning tool known as topological data analysis (TDA) would reveal data-driven patterns in patient outcomes to identify candidate biomarkers of recovery, and 2) TDA-identified biomarkers would significantly predict patient outcome recovery after TBI using more traditional methods of univariate statistical tests. TDA algorithms organized and mapped the data of TBI patients in multidimensional space, identifying a subset of mild TBI patients with a specific multivariate phenotype associated with unfavorable outcome at 3 and 6 months after injury. Further analyses revealed that this patient subset had high rates of post-traumatic stress disorder (PTSD), and enrichment in several distinct genetic polymorphisms associated with cellular responses to stress and DNA damage (PARP1), and in striatal dopamine processing (ANKK1, COMT, DRD2). CONCLUSIONS:TDA identified a unique diagnostic subgroup of patients with unfavorable outcome after mild TBI that were significantly predicted by the presence of specific genetic polymorphisms. Machine learning methods such as TDA may provide a robust method for patient stratification and treatment planning targeting identified biomarkers in future clinical trials in TBI patients. TRIAL REGISTRATION:ClinicalTrials.gov Identifier NCT01565551. |
| Databáze: | OpenAIRE |
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