Proteomic models to predict pre-analytical variation
Background
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.
Methods
Human EDTA plasma and serum samples, subjected to standardized sample processing methods, with distinct excursions from ideal collection, were assayed on the SomaScan® Platform measuring ~7,000 analytes. Using machine-learning methods, these quantitative protein measurements were compared to sample processing truth standards (eg, time-to-spin) to create predictive models. These models, termed SomaSignal® tests (SSTs), were developed to enable the assessment of PAV related to processing methods.
Results
SomaSignal tests (SSTs) have been developed to predict time-to-spin, time-to-decant and time-to-freeze, reported in the number of hours, for both plasma and serum. Models that predict the number for freeze/thaws a sample has been subjected to, have also been developed. All eight models had Lin’s CCC and R2 values greater than 0.90 in hold-out validation datasets. In addition to these sample handling predictions, effect size calculations for all ~7,000 measurements have been determined for multiple time points, or freeze-thaw cycles, for each model.
Conclusions
SomaLogic has developed a unique class of PAV models that are able to assess variation related to processing methods. Results from these predictions can be used during biomarker evaluations to exclude samples due to apparent excessive delay in processing, identify collection site bias for current and future analysis and identify sample groupings that may impact analysis. Further, knowing the effect size metrics for all measurements could also enable the removal of specific analytes from modeling and/or be used as covariates in model development.
Authors
David Astling
Dan Drolet
Joe Gogain
Yolanda Hagar
Laura Sampson
Kaitlin Soucie
Kinsey Trinder
Ira von Carlowitz
Matthew Westacott
SomaLogic Operating Co., Inc., Boulder, CO USA
Download this poster to learn more
More 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.