Posters
PosterComparison of Proteomic CV Risk to Established ASCVD 10-Year Risk Decision Points
The ASCVD pooled cohort equation (PCE) is well-established for CV risk assessment. Decision points for determining treatment plans are low, intermediate and high risk over 10 years, however this approach over and underestimates risk in certain subgroups. The validated CV Risk SomaSignal® Test (SST) provides 4-year risk probability of MACE allowing for timely assessment of risk, but the shorter timescale makes comparison to 10-year PCE risk less intuitive.
PosterStatin signature: using proteomics to detect pharmacological fingerprints
Using a previously described metacohort (n=5,575) of patients with increased CV risk, we hypothesized that PCE would stratify patients differently than the CV Risk SST, and that CV Risk score scaled to 10 years would yield an improved net reclassification index (NRI).
PosterUsing a proteomics-based cardiovascular risk test to identify systemic changes in a clinical trial of nonalcoholic fatty liver disease
Improvement in hepaKc inflammaKon, NAFLD acKvity score and fibrosis were associated with improved proteomic CV risk scores regardless of treatment provided.
PosterUtilization of proteomic surrogates for early detection of unexpected drug benefits
Detection of benefits and adverse effects of therapies in early clinical trial phases could improve the safety, efficiency, and cost of clinical trials. Earlier identification of their benefits beyond improved diabetic control may have had the potential to save loss of patients’ lives and years of sales.
PosterNormalization to external reference for reduction of technical variation
Mitigation of technical variability is a key aspect in relating proteomic biomarkers to clinical endpoints. A primary source of variability comes from inherent issues in the actual technology used to measure the protein levels in a sample, but additional variability can come from many different sources, including plate differences, sample handling differences, and study or cohort differences. Without bridging samples, these different sources of variation can lead to difficulties in combining different cohorts and across studies longitudinally, preventing researchers from sharing data easily. Here, we present results for normalization to an external reference population, which allows us to combine datasets without the need for bridging samples.
PosterLatest research shows benefit of non-invasive, high-plex protein profiling for liver disease
Learn about using high-plex, aptamer-based protein profiling for NASH research through these three SomaLogic assets. Watch one webinar and download two posters that each highlight NASH research.
PosterProteomic Indicators of Metabolic Health in Diabetes and Social Deprivation
Understanding the health impacts of socioeconomic deprivation (SED) and its interaction with type 2 diabetes is important for patient care and effective public health initiatives. Large-scale proteomic profiling using aptamer-based technology to measure 7,000 proteins has facilitated the development of blood-based proteomic signatures for 11 cardiometabolic SomaSignalTM Tests (SST)
PosterHeritability, pQTLs, and environmental influence on proteins involved in age, cardiovascular risk, and glucose tolerance 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.
PosterThe Plasma Proteome as a Cardiovascular Disease Risk Assessment Tool in Cancer Survivors
Cardiovascular disease (CVD) is the most common non-cancer cause of death in cancer survivors and there is an unmet clinical need for easy, accurate, and safe CVD prognostic risk-stratification in adult cancer survivors. This study investigated whether a previously validated 27-plasma protein prognostic model for four-year cardiovascular (CV) events could have such a utility.
PosterEfficient development of prognostic tests for detecting cancer risk using proteomic technology
Prognostic models for assessing future health outcomes can be developed using time-to-event (also known as “survival”) data. This methodology is ubiquitous in statistical literature and in the analysis of cancer outcomes, but its use in high-dimensional analyses tends to be limited as the methods are difficult to implement in a machine learning environment. Additionally, development of certified prognostic clinical tests using proteomic biomarkers for detecting future cancer risk can be time-consuming, prone to overfitting issues, and difficult to navigate. We demonstrate the utility of combining SomaScan® proteomic data with pipeline machine learning tools and survival analysis methodology to identify powerful and robust LDT-certifiable prognostic tests for assessing future risk of cancer.
PosterPredicting risk of future events in individuals with chronic coronary syndromes
Evaluate whether a previously validated 27-protein prognostic model for four-year cardiovascular event risk can be used to stratify patients with suspected chronic coronary syndrome (CCS)
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.
PosterUrinary proteome
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.