Half the Human Proteome.
Whole New Insight.
Compliment your mass spec data with
orthogonal protein and antibody profiling.
Join us at US HUPO | Booth #8
Join us for a breakfast event
Wednesday, February 25 | Park View Room
7:15 – 8:15am
Decoding Alzheimer’s Biology Through Deep Biofluid Proteomics
Beyond amyloid and tau lies a rich proteomic landscape. This talk will showcase how high-throughput platforms, including the SomaScan™ Assay and advanced mass spectrometry, reveal disease-relevant biological pathways in plasma and cerebrospinal fluid.
Attendees will learn about strategies for integrating diverse datasets, addressing QC challenges, and extracting biological insights from thousands of protein measurements that can inform precision medicine approaches and clinical trial endpoints.
Featured Speaker:
Erik Johnson, MD, PhD, is an Assistant Professor in the Department of Neurology and a member of the Goizueta Alzheimer’s Disease Research Center and Center for Neurodegenerative Diseases at Emory University. He is a cognitive neurologist with an active clinical practice who sees patients with Alzheimer’s disease (AD) and other cognitive disorders.
His research interests focus on using proteomics to understand the biochemical mechanisms that underlie AD and develop molecular fluid biomarkers for these pathological changes. He is actively involved in multiple large consortium research efforts on AD, including the Global Neurodegenerative Proteomics Consortium (GNPC).
Poster #458: Development of a Prostate Cancer Risk Model Using High Throughput Proteomics
Tuesday, February 24 | 4:30–6:30 pm | Exhibit & Poster Hall/Upper Level
Clare Paterson, Hannah Biegel, Jessica Chadwick, Sama Shrestha, Jess Kuzma, Stephen A. Williams
Standard BioTools, Boston, MA, USA
This study evaluated whether high-throughput proteomics could improve prostate cancer risk stratification beyond serum PSA alone. Citrate plasma samples from 6,599 cancer-free men in the EPIC cohort, including 219 incident prostate cancer cases over 20 years of follow-up, were analyzed using the SomaScan 7K Assay. Machine learning identified a 13-protein model that predicted five-year prostate cancer risk with a validated AUC of 0.837, sensitivity of 0.767 and specificity of 0.737. The model incorporated multiple PSA-targeting aptamers, each significantly improving model fit (p<0.01), despite moderate to high correlation, indicating that multiple PSA proteoforms and additional proteins contribute complementary prognostic information beyond PSA alone.