Faculty Collaboration Database

Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis. PLoS One 2017;12(3):e0169490

Date

03/04/2017

Pubmed ID

28257413

Pubmed Central ID

PMC5336356

DOI

10.1371/journal.pone.0169490

Scopus ID

2-s2.0-85014551008 (requires institutional sign-in at Scopus site)   69 Citations

Abstract

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.

Author List

Nielson JL, Cooper SR, Yue JK, Sorani MD, Inoue T, Yuh EL, Mukherjee P, Petrossian TC, Paquette J, Lum PY, Carlsson GE, Vassar MJ, Lingsma HF, Gordon WA, Valadka AB, Okonkwo DO, Manley GT, Ferguson AR, TRACK-TBI Investigators

Author

Michael McCrea PhD Professor in the Neurosurgery department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Adult
Biomarkers
Brain Injuries, Traumatic
Catechol O-Methyltransferase
Female
Humans
Machine Learning
Male
Middle Aged
Poly (ADP-Ribose) Polymerase-1
Polymorphism, Single Nucleotide
Receptors, Dopamine D2
Stress Disorders, Post-Traumatic