Severe trauma not only remains a major cause of hospitalization and morbidity, but is also the leading cause of death in individuals under the age of 45. The frequency of post-trauma infections, sepsis, and MSOF remain all too frequent due, in large part, to the heterogeneity of the host immune response to severe trauma, and the inability to accurately identify patients who have immunological abnormalities early after admission and who are therefore at high risk of complicated outcomes. Our overarching hypothesis is that early changes in the human blood leukocyte transcriptome (<12-24 hrs) after severe blunt trauma can be used to identify patients who will have an adverse clinical outcome, and who ultimately may benefit from intervention therapies targeting innate and adaptive immune responses. We propose to develop a clinical bed-side platform, based on genomic expression patterns from total and enriched blood leukocyte populations, on which to develop a rapid genomics-based diagnostic at the bedside in severely injured patients, which can be integrated into clinical treatment programs. There are three specific aims: (1) Using existing RNA samples from over 400 trauma patients and a database of over 2,300 trauma patients from the "Inflammation" Glue Grant, develop statistical models based on the leukocyte transcriptome that can be validated for predicting a complicated clinical outcome after severe trauma;(2) develop a point of care device that can isolate blood leukocytes and PMNs at the bedside and determine mRNA abundance in a matter of hours using a NanoString" technology;and (3) validate prospectively in 75 severe trauma patients the statistical model and point of care device to identify patients at risk of having a complicated clinical trajectory. Successful completion of the program will create a genomics-based diagnostic that can provide in the clinical setting, timely genomic data predictive of clinical outcomes in critically ill trauma patients. Success in this endeavor will go far to reach the goals to integrate biotechnology, genomics and bioinformatics, to change clinical practice, and to invigorate the concept of personalized medicine in the critically ill patient.
Data from this study would help develop a clinical diagnostic test to predict patient outcomes after severe trauma, and to discover signatures that could help doctors select a personalized drug therapy for an individual patient.
|Sinha, Sanjoy K; Kaushal, Amit; Xiao, Wenzhong (2014) Inference for longitudinal data with nonignorable nonmonotone missing responses. Comput Stat Data Anal 72:77-91|
|Ryu, So Young; Qian, Wei-Jun; Camp, David G et al. (2014) Detecting differential protein expression in large-scale population proteomics. Bioinformatics 30:2741-6|
|Sigdel, Tara K; Salomonis, Nathan; Nicora, Carrie D et al. (2014) The identification of novel potential injury mechanisms and candidate biomarkers in renal allograft rejection by quantitative proteomics. Mol Cell Proteomics 13:621-31|