Plasma and serum are brimming with potential protein biomarkers that can provide detailed information about health and disease mechanisms without the need for invasive medical procedures. Mass spectrometry is ideal for protein biomarker discovery due to its unbiased nature, allowing for complete blood proteome exploration. Until recently, analyzing the plasma proteome was difficult due to its high complexity and large dynamic range. Moreover, the previously limited reproducibility of mass spectrometry prevented accurate plasma proteome analysis.
Now, proteomics firm Biognosys has presented its next-generation technologies at the 2021 Society for Immunotherapy of Cancer (SITC), Human Proteome Organization (HUPO), and American Society for Mass Spectrometry (ASMS) meetings. They demonstrated how the firm’s platforms are advancing proteomics so researchers can explore the potential biomarkers that could unlock a new era of precision medicine. Biognosys has incorporated some of these advances into its next-generation plasma biomarker discovery solution, launched at SITC. The new solution combines the latest technologies in mass spectrometry proteomics to unlock unprecedented depth and quantitative precision for true, unbiased discovery.
Biomarker Findings in Cancer and Alzheimer’s Disease
Biognosys demonstrated the power of its plasma proteomics solution in a study of cancer biomarkers that was recently published on bioRxiv and in poster presentations at SITC and HUPO.
The study analyzed the plasma proteomes of 180 people with lung, breast, colorectal, pancreatic, and prostate cancer using automated plasma depletion and FAIMS-DIA. This optimized workflow increased proteome coverage, identifying over 2,700 proteins in the cohort, extensively covering the tissue leakage proteome, interleukins, and signaling proteins.
The study authors discovered several novel biologically relevant biomarker candidates, confirmed known biomarkers, and developed models using protein panels to classify patients by cancer type and stage (see figure 1).
In a second study presented at HUPO, Biognosys researchers used their plasma proteomics workflow to analyze the cerebrospinal fluid (CSF) of people with Alzheimer’s disease, comparing CSF proteomes of eight healthy volunteers with samples from 16 people with late-onset Alzheimer’s.
More than 400 significantly dysregulated proteins were found in samples from patients with Alzheimer’s, encompassing several biological pathways. The data highlighted several post-translational modification processes which may be important in Alzheimer’s, showing the importance of analyzing protein structure in proteomics for neuroscience applications.
Accelerating Plasma Proteomics Technology
At ASMS, Biognosys presented several studies showcasing the company’s latest technological advances that will benefit plasma biomarker research. For example, the firm presented results highlighting the benefits of automated deep profiling technologies for enhancing low-abundance proteins and improving coverage depth without jeopardizing accuracy. In addition, using controlled plasma samples, this study showed that deep profiling increased proteome coverage from an average of 572 to 1,471 proteins per experiment while maintaining an error rate of less than one percent, demonstrating the advantages of enhancing low-abundance proteins for biomarker discovery.
Consistent results are vital for transferability across all phases of the drug development pipeline. Biognosys’ plasma biomarker discovery solution provides consistent qualitative and quantitative results across multiple samples, instruments, and studies. Biognosys presented a poster at ASMS demonstrating the ability to use intrinsically labeled proteins to create directly comparable results from plasma, regardless of the instrument used to obtain the result, highlighting the reproducibility and consistency that can now be achieved with mass spectrometry.
High-throughput proteomics is vital for supporting large sample sizes and big data that can improve biomarker identification. Highly reproducible, automated, high-throughput workflows now allow researchers to simultaneously analyze hundreds of thousands of samples. A recent pilot study by Biognosys using large-scale data from the DiOGenes weight loss study highlighted how big proteomic data could support biomarker discovery and reduce the chance of falsely identifying predictive biomarkers by up to 70 percent.
Proteomics creates highly complex data sets, which are impossible to interpret without the help of advanced computing. Results presented in an oral presentation ASMS show that Biognosys’ extended machine learning framework (AVALON) improves protein identifications by 10-15 percent. Biognosys discussed data analysis using new technologies, including deep learning, in a second oral presentation at ASMS.
A New Plasma Proteomics Solution
Biognosys is offering its next-generation plasma biomarker discovery solution as a contract service for biopharma and academic researchers, promising to identify up to 3,000 relevant proteins in each plasma, serum, or biofluid sample, including an unlimited number of proteoforms such as post-translational modifications. Tens of thousands of peptide-level data points are gathered to assure high quantitative precision and specificity. The scalable technology and high-throughput facility mean the firm can quantify thousands of proteins across thousands of samples simultaneously.
Plasma proteomics is now an end-to-end solution that allows researchers to search the complete proteome for insights into the underlying mechanisms of disease, therapeutic modes of action, predictive biomarkers, and indicators of response to therapies. The depth and precision of analysis now achievable with mass spectrometry means that proteomics is approaching next-generation sequencing in terms of ease, speed, and actionable results, transforming the future of biomarker research.
Discover more about Biognosys’ plasma proteomics offering at https://biognosys.com/resources/plasma-biomarker-discovery/ or contact Biognosys at [email protected]