PosterUtility of proteomic trajectories of cardiovascular risk and cardiorespiratory fitness to monitor adverse health states throughout post-COVID-19 illness
Cardiovascular involvement is a prominent observation in patients during the acute phase of COVID-19 infection, as well as in convalescence. However, the etiology, trajectory, and underlying biology of cardiac dysfunction across the spectrum of COVID-19 illness is not fully understood. To address this, the CISCO-19 study (NCT04403607) was formed to investigate the multisystem effects of COVID-19 from hospitalized patients
PosterIdentifying genetic and environmental influences on proteins associated with age, cardiovascular risk, and other endpoints using the SomaScan® Assay
Protein quantitative trait locus pQTL studies identify genetic variants that are statistically associated with protein levels Results from the growing number of pQTL studies can be combined with genome wide association studies to identify proteins that underlie the genetic risk of disease, thus revealing the mechanisms of disease and potential drug targets.
PosterSomaScan® Platform confirmation and performance validation
The SomaScan® Platform for proteomic profiling uses 7288 (7K) SOMAmer® reagents, single stranded DNA aptamers, to 6596 unique Human Protein Targets. The modified aptamer binding reagents1, SomaScan assay2, its performance characteristic for 5k3 and 7k4 content sets, and specificity5,6,7 to human targets have been previously described. We combine profiles of validation and performance metrics with orthogonal confirmation of specificity from published literature to provide a comprehensive view of the specificity and utility of the SomaScan Platform.
PosterAptamer-based analysis of plasma proteome of growing tumors
With proteins, the presence of a tumor is more often accompanied with changes in the levels of endogenous, unmutated proteins in circulation. In this context, knowing which proteins represent the earliest markers or tumor presence would be enormously useful.
PosterPrognostic proteomic models for low event rates: A case study with myocardial infarction
We have developed and assessed a novel prognostic model development method combining two statistical techniques – survival analysis and subsampling – using existing machine learning tools in R.
PosterProteomic Models to Predict Pre-Analytical Variation
In biomarker discovery, it is critical to assess any pre-analytical variation (PAV) in order to avoid artificial bias in the intended measurements. PAV may arise from both avoidable and unavoidable factors, resulting in misleading data and incorrect conclusions. Proteins, in particular, are vulnerable to variation in collection methods, storage temperatures, and processing protocols. It is vitally important to understand this PAV when analyzing samples using protein assays.
PosterEfficient development of certified diagnostic laboratory developed tests using proteomic data
We demonstrate the utility of combining pipeline tools, statistical learning techniques, and a knowledge base of in-silico proteomic datasets into a reproducible workflow that allows for efficient development of LDT-certifiable tests using SomaScan® technology.
PosterDementia risk from middle age
In the US the number of individuals affected by dementia is expected to double by 2040. Thus, tools enabling identification of at-risk individuals earlier in disease progression, or before disease onset, are vital.
PosterLung Cancer risk in ever smokers
Development and validation of a blood-based protein-only predictor of 5-year lung cancer risk in ever smokers.
Urinary proteome and its application to predict cardiovascular risk in patients with stable Coronary Heart Disease.
PosterLiquid liver biopsy
A liquid liver biopsy: serum protein patterns of liver steatosis, inflammation, hepatocyte ballooning and fibrosis in NAFLD and NASH.
PosterSurvival in heart failure
Plasma proteomic profile predicts survival in Heart Failure with reduced Ejection Fraction.
PosterClinical use of cardiovascular risk score
Clinical use of a proteomic cardiovascular risk score positively drives patient attitudes and behavior change.