Proteomic risk scores can help identify high-risk patients and monitor short- and long-term effects of treatment
Proteomic risk scores can help identify high-risk patients and monitor short- and long-term effects of treatment
A webinar presented by Clare Paterson, PhD, Associate Director of Clinical Research, SomaLogic, and Jessica Chadwick, PhD, Scientist, SomaLogic
Abstract
In this 1-hour discussion, you will learn how large-scale proteomics can be leveraged for multiple applications in the field of oncology. The focus will be on two new avenues of oncology research. The first is using proteomic risk scores to monitor the cardiotoxic effects of anthracycline treatment and identify patients who may benefit from cardioprotective therapy. The other is developing blood-based protein tests for stratifying lung cancer susceptibility. From biomarker discovery to medicine application and the potential to inform and impact patients’ standard of care—there is a lot to learn and look forward to in oncology research using proteomics.
Clare Paterson, PhD
Associate Director of Clinical Research, SomaLogic
Prior to SomaLogic, Dr. Paterson conducted a fellowship in the clinical brain disorders branch at the National Institutes of Health (NIH) before becoming an Assistant Professor in Psychiatry at the University of Colorado School of Medicine.
Her academic research career focused on the use of -omics technologies to map how the human brain develops and ages and is altered in psychiatric illnesses, with the aim of ultimately uncovering new means of patient stratification and treatment discovery.
Jessica Chadwick, PhD
Scientist, SomaLogic
Dr. Jessica Chadwick is a scientist in the Clinical Research & Development department at SomaLogic. She completed her PhD in biochemistry at The Ohio State University as an NIH fellow. Her academic research focused on novel therapeutic strategies for treating Duchenne muscular dystrophy.
Dr. Chadwick has worked at SomaLogic for over 5 years on the development and productization of proteomic-based cardiometabolic tests. More recently she led an exciting new body of work aimed at assessing the robustness and sensitivity of these tests to interventions.
Protein biomarker models for oncology risk assessment and treatment monitoring
A webinar presented by Clare Paterson, MD, and Jessica Chadwick, MD
Contact us to learn more today
More webinars
WebinarBoutique Webinar Aptamers with protein-like side chains as a versatile tool for high-content proteomics
Proteins, encoded in 20,000 genes in humans, do much of the work in biology. Measuring proteins, which change in response to various perturbations and represent targets for almost all drugs, offers insights about the health status of an organism. Since proteins operate in complex networks rather than in isolation, measuring multiple proteins simultaneously offers richer insights compared to single protein measurements.
WebinarUsing Proteomics To Advance Understanding of Alzheimer’s Disease
Limited understanding due to its complex pathophysiology and lack of definitive biomarkers currently constrains the diagnosis and treatment of Alzheimer’s disease (AD). But new research is uncovering dynamic brain changes during Alzheimer’s progression, offering potential therapeutic targets. This webinar explores how proteomics and systems biology can be integrated to elucidate AD pathology.
WebinarPredictive modeling and reliable biomarker discovery in clinical omics studies
High-content omic technologies coupled machine learning methods have transformed the biomarker discovery process. However, the translation of computational results into scalable clinical biomarkers remains challenging. A rate-limiting step is the rigorous selection of reliable biomarker candidates among a host of biological features. Drawing examples from real-world clinical omics studies, I will introduce Stabl, a general machine learning framework that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling.