somascan^® discovery

In this 45-minute talk, Dr. Stefánsson will discuss the largest population proteomics study ever performed to help provide more insight on disease and health outcomes. This collaborative research assembled expertise from deCODE Genetics and SomaLogic to combine the study of genetic and protein diversity to characterize disease biomarkers in the human population. The data from this type of proteomic study will inform drug discovery and development as well as improvements in health management.

The SomaScan platform offers the most comprehensive coverage of the human proteome, with a current coverage of over 7000 proteins. We have applied the earlier version of the assay that interrogates 1300 proteins to several diseases. An over-arching theme that is emerging from our studies is the observation that the closer the interrogated body fluid is to the site of pathology, the better it is for mining disease biomarkers. For example, urine is a better fluid to interrogate (rather than serum/plasma) in patients with urinary tract diseases, while stool may be a preferred material to examine in a subject with gastrointestinal disease rather than serum/plasma.

Examples of the following tested samples will be discussed, all of which were executed using a 1300-plex SomaScan platform: lupus nephritis urine samples, bladder cancer urine samples, cerebrospinal fluid samples from patients with neuropsychiatric lupus and multiple sclerosis, colorectal cancer stool samples and inflammatory bowel disease stool samples. In all cases, ~20-40 of the identified hits were selected for ELISA verification in larger cohorts. Overall, ~85% of the SomaScan identified hits withstand verification using commercially available ELISA kits. Downstream studies initiated by the SomaScan assay will be exemplified using the completed lupus nephritis studies. The latter studies have led to the identification of novel diagnostic biomarkers, prognostic biomarkers, monitoring biomarkers, potential point-of care-tests, as well as therapeutic leads.

In summary, the SomaScan proteomic platform offers the most comprehensive proteomic coverage with good sensitivity and results that can be successfully verified using orthogonal antibody-based platforms. Taken together with other OMICs explorations, these emerging approaches have the potential to shed novel insights on disease pathways and mechanisms.

To determine whether the plasma proteome adds value to established predictors in heart failure (HF) with reduced ejection fraction (HFrEF). We sought to derive and validate a plasma proteomic risk score (PRS) for survival in patients with HFrEF (HFrEF-PRS).
Until recently, proteomics had not been as thoroughly explored partly due to comparatively low throughput. However, emerging proteomic technologies have recently matured and newer high- throughput techniques now allow larger scale proteomic characterization. One of these newer technological innovations, enhanced aptamer-based assays, has enabled a massively expanded candidate approach that borders on true proteomics in scale; thousands of protein-derived factors can be efficiently assayed simultaneously in a small biologic sample. In this webinar, you will explore this new large-scale protein array using an established HF patient registry to understand how the plasma proteome could meaningfully predict the risk of death or HF worsening and add to best conventional risk stratification, including clinical score and natriuretic peptides. By using the circulating proteome to improve risk prediction, it could add a new tool to help manage patients with HF and contribute to discovery of novel HF markers and pathways.

Understanding the tissue-specific genetic architecture of protein levels is instrumental to understand the biology of health and disease. We generated a genomic atlas of protein levels in multiple neurologically relevant tissues (380 brain, 835 cerebrospinal fluid (CSF) and 529 plasma), by profiling thousands of proteins (713 CSF, 931 plasma and 1079 brain) in a large and well-characterized cohort. We identified 274, 127 and 32 protein quantitative loci (pQTL) for CSF, plasma and brain respectively. cis-pQTL were more likely to be shared across tissues but trans-pQTL tend to be tissue-specific. Between 44% to 68.2% of the pQTL do not colocalize with expression, splicing, methylation or histone QTLs, indicating that protein levels have a different genetic architecture to those that regulate gene expression. Using Mendelian randomization, we identify causal proteins in neurological diseases, including Alzheimer’s disease, Parkinson’s disease and stroke. Then, we performed bioinformatic analyses to determine if any of the identified proteins are drugable. We identidied 15 existing drugs approved by the U.S. Food and Drug Administration (FDA) have the potential to slow or reverse brain damage in those with Alzheimer’s disease and another 14 for Parkinson Disease, ALS or stroke. In summary, we present the first multi-tissue study yielding hundreds of novel pQTLs. This data will be instrumental to identify the functional gene from GWAS signals, identify novel biological protein-protein interactions, identify novel potential biomarkers and drug targets for complex traits.

COPD and emphysema are lung diseases related to chronic exposure to tobacco smoke or air pollution. The World Health Organization anticipates that COPD will be the number three cause of death worldwide by 2030; however, the majority of smokers and people exposed to air pollution do not develop COPD or emphysema. To better understand why only some people develop disease, we used SomaScan v4.0 (4,776 human proteins) to identify plasma biomarkers of disease status as well as severity and progression in 6,017 subjects from the COPDGene cohort. Because we were able to identify hundreds of individual biomarkers, the majority of which explained only a small percentage of the phenotypes, we used penalized regression (LASSO) to develop protein risk scores, which typically include 200-300 of the most influential proteins. These protein risk scores explained as much as 40-50% variance of disease.

The NIH-funded Atherosclerosis Risk in Communities (ARIC) Study, which began in 1987, is a treasure trove of longitudinal, clinical data for biomarker discovery. In this webinar, Josef Coresh will discuss how his team and collaborators have mined that data by connecting ARIC’s rigorous epidemiologic cohorts with breakthrough proteomic quantification enabled by the SomaScan® Assay.

Using the SomaScan Assay, ~5,000 proteins were measured on frozen plasma from three visits conducted during 1990-1992 (n>10,000), 1993-1995 (N>12,000) and 2011-2013 (N>5000). Following quality control verification of high precision, data were cleaned and shared with approximately 100 scientists working with ARIC data, who developed 64 approved manuscript proposals (3 published thus far, 13 forthcoming).

Methods for discovery, internal, and external validation were developed with success for chronic kidney disease and dementia. Discoveries highlighted specific prognostic target proteins (e.g. SVEP for dementia and TNFsR1 and LMAN2 for CKD) and pathways for each disease with Mendelian Randomization evidence of causation supporting some as useful targets for intervention.

Using the power of the world’s largest proteomic platform coupled with machine learning , clinical samples and known biopsy results were used to build proteomic models capable of staging steatosis , inflammation, ballooning and fibrosis, and diagnosing at-risk NASH. These models exhibit competitive performance vs. existing standard methods. Use-cases in drug development include a participant rule-out prior to biopsy and precision demonstration of pharmacodynamic mechanistic impacts on individual liver components with multiple longitudinal measurements. Combination of the liquid liver biopsy with other proteomic tests on the same platform can expand the assessment of drug impact to include changes in cardiovascular risk, insulin resistance, kidney function and body composition. This will result in smaller, faster and more comprehensive clinical trials in the future.

Type 2 diabetes typically manifests itself in adulthood, following years of progressively worsening health status, affecting multiple biological systems, pathways and organs. But beyond measuring HbA1c, it has been difficult and expensive to measure the impact of disease and/or interventions that might impact organ damage or risk.

Large-scale proteomics is an emerging field that has recently been shown to not only capture real-time health status, but also to predict or prognose risks of future organ damage, morbidity and mortality. Proteomic models, developed from multiple large clinical and observational studies, have been utilized here to provide a holistic summary of metabolic health and risk of future adverse outcomes from individuals undergoing diabetes intervention.

In this webcast, the impact of both therapeutic (drug) and lifestyle (diet and exercise) interventions will be discussed on a host of cardiometabolic and body composition measures in subjects undergoing intervention compared to placebo or standard of care. The speakers will discuss how these proteomic models can be implemented to design more precise treatment strategies for diabetic patients, moving the field ever closer to realizing the goal of providing personalized care.

Dr. Coomb’s group has used the SomaScan assay to screen >1300 host proteins in Zika virus-infected cells, both globally and specifically in the central nervous system. Significant findings include the identification of nearly 300 astrocyte proteins that were significantly dysregulated by Zika infections, pointing to pathways that may be involved in neurological complications resulting from Zika, such as microcephaly and Guillain-Barré syndrome.

Dr. Coombs’ work in influenza has included extensive proteomic scanning of H5N1, H1N1, and H7N9 in various cell types including induced-pluripotent stem cells. Significant findings include the observation that low-pathogenicity strains induce less profound changes to the global proteome. For instance, avian strains stimulate significant downregulation of key proteins, including those involved in antimicrobial response. Seasonal strains do not elicit the same response. Further, Dr. Coombs’ team found that viral infection in stem cells can reduce pluripotency, activate autophagy, and lead to abnormal differentiation.

With SomaScan® Proteomics, predicting NASH and NAFLD is as simple as a blood test.

Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are increasingly common conditions that can progress to serious liver complications including cirrhosis and cancer. Diagnosing the severity of NAFLD and NASH has traditionally relied on invasive liver biopsies.

It was recently demonstrated that the SomaScan Assay, a multiplex proteomics platform, can noninvasively and simultaneously predict all the key elements of the liver biopsy: liver fat, inflammation, hepatocyte ballooning and fibrosis based only on serum protein biomarkers. By measuring ~5000 proteins per sample in ~700 patients, with liver biopsy results, machine learning models were developed and validated that can reliably predict NAFLD/NASH phenotypes . The tests were used to characterize the differential mechanistic effects of three different drugs during clinical trials.

In this webinar, Dr. Steve Williams, the Chief Medical Officer for SomaLogic, will present this work and introduce a soon to be launched NASH test that is available for research use only through the SomaScan Discovery Alliance.

Earlier this year, Stephen Williams (SomaLogic), Claudia Langenberg (University of Cambridge*), and Peter Ganz (UCSF) lead a team that demonstrated the first proof of concept for a comprehensive “liquid health check” that derived 11 different health indicators from 55µl of blood.

The study combined clinical data from five different groups of nearly 17,000 prospectively monitored participants in whom ~5,000 proteins were measured at study baseline using the SomaScan® Assay. Using machine learning models, they found that they were able to characterize six current health states and predict three health related behaviors—equaling metrics that would typically be generated from a battery of costly medical tests and approximately nine visits to the doctor. Additionally, they used the proteomic data and their models to reliably forecast probabilities of cardiovascular events and advancement from prediabetes to diabetes five years later.

In this webinar, Williams, Langenberg, and Ganz will discuss key findings and motivations for the study “Plasma protein patterns as comprehensive indicators of health,” which was recently published in Nature Medicine. The webinar will additionally explore the implications of their findings for public health, particularly in the areas of diabetes and cardiovascular disease.

Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) are powerful tools for identifying disease pathways, but the associations are typically weak and hard to interpret. Additionally, the complicated gene expression landscape—which is composed of interconnected cycles of transcript, protein, and metabolite levels—cannot be reliably inferred from gene sequences and chromosome modifications alone.

In this Webinar, Karsten Suhre, Director of the Bioinformatics Core at Weill Cornell Medicine-Qatar, will discuss how proteomics can complement GWAS and EWAS in order to identify SNP-protein (pQTLs) and CpG-protein associations (pQTMs) and link variation in the genetic code and DNA methylation patterns to disease endpoints.

In a recent Nature Communications manuscript, Suhre and collaborators measured 1123 circulating proteins using the SomaScan Assay. They then made connections between protein abundance and methylation levels at >470,000 CpG sites to identify 98 highly significant pQTMs (out of >12,000 total). In this webinar, Suhre will discuss these and earlier findings from GWAS and explore how the SomaScan Assay can be used as a tool in multi-omics studies.

Age is the biggest risk factor for a number of chronic diseases. While genetic changes across lifespan are limited, a recent study by Benoit Lehallier and Nir Barzilai demonstrated that the proteome undergoes measurable waves of change that reflect distinct biological pathways associated with age-linked disease. The study identified age-related patterns in ~3,000 proteins across >4,000 healthy individuals between the ages of 20 and 95 using the SomaScan® proteomics platform.

In this webinar, Lehallier and Barzilai discuss the findings of their Nature Medicine paper “Undulating changes in human plasma proteome profiles across the lifespan” and present new data exploring how probing the proteome can help us to separate chronological age from biological age.