SomaScan® Assay surpasses the “gold standard” in Lupus patient stratification
Antibodies are wonderful evolutionary inventions, but like all things they have their problems. In a recent paper in Scientific Reports3, a team of researchers describe hitting – and overcoming – an antibody-based roadblock on the way to a deeper biological understanding of systemic lupus erythematosus (SLE), a devastating autoimmunity disease in which the affected individual’s immune system turns on them.
One of the molecular hallmarks of SLE is an abnormally large concentration of anti-DNA autoantibodies in the blood. Those very antibodies could render the SomaScan technology useless for studying SLDE, given the technology’s fundamental reliance on unique DNA-based protein binding reagents (our SOMAmer® reagents). Indeed, the researchers describe how samples from 27% of the SLE patients initially failed in the SomaScan Platform’s quality control, predominantly in the samples with the most anti-DNA autoantibodies.
Rather than turning to another protein measurement platform, the research team first tried an old molecular biology laboratory “trick”: adding huge excesses of plain DNA (in this case from herring sperm DNA) to the samples to bind up the anti-DNA autoantibodies, thinking that this would allow the SOMAmer reagents to bind their protein targets. This trick restored the ability of the SomaScan Assay to accurately measure proteins in nearly all the samples, passing internal stringent quality control measures. The results were confirmed using traditional immunoassays from other companies to test specific protein changes seen in the SomaScan results.
Comparing and contrasting SomaScan assay data with transcription analysis
The researchers compared the SomaScan Assay data to a “gold standard” – validated transcription signature (of many genes) typically associated with type I interferon signaling. They were able to confirm that SomaScan data correlated with the gold standard and might prove useful for when mRNA samples are not available for testing.
What’s particularly interesting is that the proteomic data revealed something not seen in the gold standard analysis. The team analyzed data from samples from people with SLE with symptoms of varying degrees of severity. With the transcription analysis, the patients sorted into two groups, high levels or low levels. Those with high levels would be considered as good candidates for treatment. The proteomic data, however, could further stratify the patients, which is not surprising because transcription data can miss things caught by proteomics (i.e., the two types of data do not always agree1,2,4). In patients scoring low in the “gold standard” analysis, a strong proteomic signature could be observed indicating that the patients had high type I IFN activity and may be candidates for treatment. This may explain why some patients identified by the “gold standard” as not likely to benefit from treatment still responded to treatment.
SomaScan assay benefiting patients
Aside from being identified as candidates for treatment, proteomics benefited patients in another way. The researchers found that the proteomic data can reveal how a patient’s biology is reacting to treatment. For anifrolumab, an antibody generated to neutralize IFN alpha receptor, the patients’ proteomic profiles changed after the administration of the drug – showing patients were responding to treatment in the phase IIb trial. It is tantalizing to envision the applications for which future researchers may use the data, such as to fine tune treatment dose, identify people that are better suited to move to a phase III trial, decrease trial duration, predict if a treatment will be effective for an individual, etc.
What can we do for you?
The authors mentioned the usefulness of the technology in monitoring the inflammation of multiple organ sources. They are not alone in noting just how useful the SomaScan technology is in monitoring what happens inside the body in a very convenient way. In fact, the SomaScan Assay has been used to study many diseases and conditions (e.g., type two diabetes, cardiac conditions, weight loss, kidney problems, asthma, pregnancy, etc.) and there are hundreds of publications as a result. To find out what the technology could do for you, please contact us.
- Fortelny, N., Overall, C. M., Pavlidis, P., & Freue, G. V. C. (2017). Can we predict protein from mRNA levels? Nature, 547(7664), E19-E20. doi:10.1038/nature22293
- Liu, Y., Beyer, A., & Aebersold, R. (2016). On the Dependency of Cellular Protein Levels on mRNA Abundance. Cell, 165(3), 535-550. doi:10.1016/j.cell.2016.03.014
- Smith, M. A., Chiang, C. C., Zerrouki, K., Rahman, S., White, W. I., Streicher, K., . . . Casey, K. A. (2020). Using the circulating proteome to assess type I interferon activity in systemic lupus erythematosus. Sci Rep, 10(1), 4462. doi:10.1038/s41598-020-60563-9
- Vogel, C., & Marcotte, E. M. (2012). Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet, 13(4), 227-232. doi:10.1038/nrg3185