Machine learning cross-platform proteomic imputation enables protein quality scoring and replication of epidemiological associations

Topic/Product:
CNS Disease Panel 120, Neurology
Disease Area:
Population Health
Sample Type:
Plasma

Abstract

High-throughput affinity-based proteomics has advanced biomedical research, yet fundamental, persistent discordance between mainstream platforms (SomaScan and Olink) routinely undermines the replication of findings. This platform-driven non-replication complicates downstream biological validation and biomarker prioritization. Here, we develop a machine learning-based framework for cross-platform protein value imputation to resolve this translational bottleneck. Using paired proteomic data measured by both SomaScan and Olink from 5,325 participants of the Multi-Ethnic Study of Atherosclerosis, we developed models to impute cross-platform measurements and applied them to two independent and demographically distinct cohorts (Cardiovascular Health Study [N=3,171] and UK Biobank [UKB; N=41,405]) for external validation. Our bi-directional model 1) established an imputation performance-based protein fidelity index, validated against gold-standard measurements from Atherosclerosis Risk in Communities study (N=101) and Nurses’ Health Study (N=54), 2) enabled imputation of platform-exclusive protein measurements, and 3) facilitated calibration of overlapping proteins. We demonstrate the utility of this framework through three applications: 1) fidelity-informed analyses enhanced the replication of biomarker discovery, 2) recovery of SomaScan signals that were previously inaccessible in UKB’s original Olink measurements, and 3) improved replication performance for overlapping proteins. Our study offers a translational roadmap that allows researchers to achieve reliable epidemiological replication, target specific assays for future optimization, and prioritize biological signal over platform noise.

Authors & Affiliations

Linke Li* ¹˒², Ahmed Alaa* ³˒⁴, Youxin Tan²˒⁵, Ilker Demirel⁶, Samuel Friedman⁷, Qiayi Zha²˒⁵, Russell Tracy⁸, Kent D. Taylor⁹, Bing Yu¹⁰, Christie M. Ballantyne¹¹, Rajat Deo¹², Ruth Dubin¹³, Michael Y. Tsai¹⁴, Gina M. Peloso¹⁵, Jennifer Brody¹⁶, Tom Austin¹⁶, Bruce M. Psaty¹⁶, Jayna Nicholas¹⁷, Laura M. Raffield¹⁷, Usman Tahir¹⁸, Josef Coresh¹⁹, Whitney Hornsby²˒²⁰, Andrew Chan¹˒²¹, Stephen S. Rich²², Jerome I. Rotter⁹, Peter Ganz²³, Robert Gerszten²⁰, Anthony Philippakis²⁴, Pradeep Natarajan ²˒²⁰˒²¹, and Zhi Yu** ¹˒²˒²¹

¹ Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
² Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
³ Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
⁴ University of California, San Francisco, San Francisco, CA, USA
⁵ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
⁶ Massachusetts Institute of Technology, Cambridge, MA, USA
⁷ ML4H, Broad Institute of Harvard and MIT, Cambridge, MA, USA
⁸ Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA
⁹ The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
¹⁰ Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
¹¹ Department of Medicine, Baylor College of Medicine, Houston, TX, USA
¹² Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
¹³ Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
¹⁴ Department of Laboratory Medicine & Pathology, University of Minnesota Medical School, Minneapolis, MN, USA
¹⁵ Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
¹⁶ Cardiovascular Health Research Unit, Department of Medicine & Department of Epidemiology, University of Washington, Seattle, WA, USA
¹⁷ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
¹⁸ Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
¹⁹ Department of Medicine, NYU Grossman School of Medicine, New York, NY, USA
²⁰ Heart and Vascular Institute, Mass General Brigham, Boston, MA, USA
²¹ Department of Medicine, Harvard Medical School, Boston, MA, USA
²² Department of Genome Sciences, University of Virginia, Charlottesville, VA, USA
²³ Division of Cardiology, Zuckerberg San Francisco General Hospital and Department of Medicine, University of California San Francisco, San Francisco, CA, USA
²⁴ Google Ventures, Cambridge, MA, USA

Steve Williams, MD, PhD

CSO

Dr. Willams serves as the company’s Chief Scientific Officer. He was previously Chief Medical Officer at Standard Biotools and at SomaLogic where he pioneered the discipline for discovery and validation of predictive, diagnostic and prognostic models using machine-learning applied to large-plex proteomics. 20 such tests were used for drug characterization, safety and efficacy when incorporated in clinical drug trials at Pharma/Biotech and 17 different multivariate tests were validated and translated into regulated healthcare uses. Prior to SomaLogic, Dr. Williams was at Pfizer in the UK and the USA as a clinical triallist in Translational Medicine, and subsequently as VP, Global Clinical Technology. He sat on the National Advisory Council for the National Institute of Biomedical Imaging and Bioengineering, the Executive Committee for the FNIH Biomarkers Consortium, and worked with FDA and PhRMA on developing evidentiary standards for biomarker qualification. Dr. William’s medical training was in London, at Charing Cross and Westminster Medical School, followed by a PhD in medicine/physiology at the same institution and training in Radiology at the University of Newcastle Upon Tyne. Steve is co-inventor on 26 proteomics patents and author/coauthor on multiple foundational proteomics manuscripts.

Justin McAnear

CFO

Mr. McAnear serves as the company’s Chief Financial Officer. He brings over 25 years of operational and financial leadership experience across various sectors and was instrumental in taking 10x Genomics public in 2019, serving as its CFO for over five years. Mr. McAnear served for over 3 years as Tesla’s VP of Worldwide Finance and Operations, supporting landmark initiatives such as the Model X and Model 3 launches and Solar City acquisition.  He also held various roles at Apple and J&J earlier in his career and served as a naval officer and aviator for over 9 years.