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Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis

Nielson, Jessica L ; Cooper, Shelly R ; Yue, John K ; Sorani, Marco D ; Inoue, Tomoo ; Yuh, Esther L ; Mukherjee, Pratik ; Petrossian, Tanya C ; Paquette, Jesse ; Lum, Pek Y ; Carlsson, Gunnar E ; Vassar, Mary J ; Lingsma, Hester F ; Gordon, Wayne A ; Valadka, Alex B ; Okonkwo, David O ; Manley, Geoffrey T ; Ferguson, Adam R Kobeissy, Firas H

PloS one, 2017-03, Vol.12 (3), p.e0169490-e0169490 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis
Autor: Nielson, Jessica L ; Cooper, Shelly R ; Yue, John K ; Sorani, Marco D ; Inoue, Tomoo ; Yuh, Esther L ; Mukherjee, Pratik ; Petrossian, Tanya C ; Paquette, Jesse ; Lum, Pek Y ; Carlsson, Gunnar E ; Vassar, Mary J ; Lingsma, Hester F ; Gordon, Wayne A ; Valadka, Alex B ; Okonkwo, David O ; Manley, Geoffrey T ; Ferguson, Adam R
Kobeissy, Firas H
Assuntos: Adult ; Algebraic topology ; Algorithms ; Biological markers ; Biology and Life Sciences ; Biomarkers ; Brain ; Brain injuries ; Brain Injuries, Traumatic - diagnosis ; Brain Injuries, Traumatic - diagnostic imaging ; Brain Injuries, Traumatic - genetics ; Brain Injuries, Traumatic - physiopathology ; Brain research ; Care and treatment ; Catechol O-Methyltransferase - genetics ; Clinical trials ; Data analysis ; Data processing ; Data recovery ; Deoxyribonucleic acid ; Diagnosis ; Diagnostic systems ; DNA ; DNA damage ; Dopamine ; Dopamine D2 receptors ; Female ; Genetic polymorphisms ; Genetics ; Head injuries ; Hospitals ; Humans ; Injuries ; Injury analysis ; Innovations ; Learning algorithms ; Learning commons ; Machine Learning ; Male ; Medical imaging ; Medical research ; Medicine and Health Sciences ; Mental disorders ; Middle Aged ; Molecular modelling ; Neostriatum ; Neuroimaging ; Neurosciences ; Neurosurgery ; Patients ; Physical Sciences ; Poly (ADP-Ribose) Polymerase-1 - genetics ; Poly(ADP-ribose) Polymerase 1 ; Polymorphism, Single Nucleotide ; Population (statistical) ; Population genetics ; Population studies ; Post traumatic stress disorder ; Posttraumatic stress disorder ; Precision medicine ; Protein-Serine-Threonine Kinases - genetics ; Receptors, Dopamine D2 - genetics ; Research and Analysis Methods ; Signs and symptoms ; Spinal cord injuries ; Statistical analysis ; Statistical methods ; Statistical tests ; Stress Disorders, Post-Traumatic - diagnosis ; Stress Disorders, Post-Traumatic - diagnostic imaging ; Stress Disorders, Post-Traumatic - genetics ; Stress Disorders, Post-Traumatic - physiopathology ; Subgroups ; Surgery ; Topology ; Traumatic brain injury
É parte de: PloS one, 2017-03, Vol.12 (3), p.e0169490-e0169490
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TRACK-TBI Investigators are listed in the Acknowledgments.
Conceptualization: ARF JLN GTM DOO ABV WAG SRC JKY.Data curation: ARF JLN GTM SRC JKY MDS HFL TI MJV.Formal analysis: ARF JLN GTM.Funding acquisition: ARF GTM DOO ABV WAG TRACK-TBI Investigators.Investigation: ARF JLN GTM.Methodology: ARF JLN GTM TCP JP PYL GEC.Project administration: ARF GTM MJV.Resources: ARF JLN GTM DOO ABV WAG ELY PM TRACK-TBI Investigators.Software: TCP JP PYL GEC.Supervision: ARF GTM.Validation: ARF JLN TCP JP PYL GEC.Visualization: ARF JLN TCP JP PYL GEC.Writing – original draft: ARF JLN GTM SRC JKY MJV.Writing – review & editing: ARF JLN GTM DOO ABV WAG ELY PM TRACK-TBI Investigators.
Competing Interests: Authors TCP, JP, PYL and GEC are current or former employees of Ayasdi, Inc. This commercial affiliation does not alter adherence to PLOS ONE policies on sharing data and materials. There are no other competing interests declared by the rest of the co-authors ARF, JLN, GTM, SRC, JKY, MDS, TI, ELY, PM, MJV, HFL, WAG, ABV, and DOO.
Descrição: 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. 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). 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. ClinicalTrials.gov Identifier NCT01565551.
Editor: United States: Public Library of Science
Idioma: Inglês

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Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis

United States: Public Library of Science

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